an essay about active learning

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Active Learning

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Active learning includes any type of instructional activity that engages students in learning, beyond listening, reading, and memorizing.  As examples, students might talk to a classmate about a challenging question, respond to an in-class prompt in writing, make a prediction about an experiment, or apply knowledge from a reading to a case study.  Active learning commonly includes collaboration between students in pairs or larger groups, but independent activities that involve reflection or writing—like quick-writes, or real-time polling in lectures—are also valuable.

Instructors can employ active learning in classes of any size, although certain activities may be better suited for smaller classes than large lecture halls.  Nonetheless, even large classes—including classes that meet in lecture halls with fixed seats—can incorporate a variety of activities that encourage students to talk with each other, work in small groups on an activity, or respond to a question through in-class writing or polling.  Furthermore, even small classes can increase student engagement beyond what might occur in a full group discussion by varying the instructional approaches and including small group discussions and activities.

Why should I use active learning?

 Active learning is valuable for a variety of reasons:

• It provides instructors with feedback about what students are learning.
• It helps students gauge their own understanding. By grappling with ideas, students connect new concepts to prior knowledge in meaningful ways and construct their own understanding.
• Collaborating with classmates promotes community and connection between students, which can enhance a sense of belonging as well as motivation.
• It creates a low bar to participation for quiet or passive students by encouraging every student to think and do.

Many of the larger scale studies on active learning have been conducted in STEM disciplines, although it reasonable to expect that the benefits of active learning extend to any field.  A 2014 meta-analysis of 225 research studies in STEM classes found that students in classes with active learning performed 6% better on exams than students in classes with traditional lecturing, and that students in classes with traditional lecturing were 1.5 times more likely to fail than in classes with active learning ( Freeman et al, 2014 ).  Additionally, active learning has been shown to decrease the achievement gap for underrepresented minorities and first generation college students ( Theobald et al, 2020 ).

What are some examples?

Active learning strategies come in many varieties, most of which can be grafted into existing courses without costly revisions. One of the simplest and most elegant exercises, called  Think-pair-share , could easily be written into almost any lecture. In this exercise, students are given a minute to think about—and perhaps respond in writing—to a question on their own.  Students next exchange ideas with a partner.  Finally, some students share with the entire class. A think-pair-share engages every student, and also encourages more participation than simply asking for volunteers to respond to a question.

Other active learning exercises include:

• Case studies :  In a case study, students apply their knowledge to real life scenarios, requiring them to synthesize a variety of information and make recommendations.
• Collaborative note taking :  The instructor pauses during class and asks students to take a few minutes to summarize in writing what they have just learned and/or consolidate their notes.  Students then exchange notes with a partner to compare; this can highlight key ideas that a student might have missed or misunderstood.
• Concept map :  This activity helps students understand the relationship between concepts. Typically, students are provided with a list of terms.  They arrange the terms on paper and draw arrows between related concepts, labeling each arrow to explain the relationship.
• Group work :  Whether solving problems or discussing a prompt, working in small groups can be an effective method of engaging students.  In some cases, all groups work on or discuss the same question; in other cases, the instructor might assign different topics to different groups.  The group’s task should be purposeful, and should be structured in such a way that there is an obvious advantage to working as a team rather than individually.  It is useful for groups to share their ideas with the rest of the class—whether by writing answers on the board, raising key points that were discussed, or sharing a poster they created.
• Jigsaw :  Small groups of students each discuss a different, but related topic. Students are then shuffled such that new groups are comprised of one student from each of the original groups. In these new groups, each student is responsible for sharing key aspects of their original discussion. The second group must synthesize and use all of the ideas from the first set of discussions in order to complete a new or more advanced task.  A nice feature of a jigsaw is that every student in the original group must fully understand the key ideas so that they can teach their classmates in the second group. 

• NB: A minute paper can also be used as a reflection at the end of class.  The instructor might ask students to write down the most important concept that they learned that day, as well as something they found confusing.  Targeted questions can also provide feedback to the instructor about students’ experience in the class.
• Statement correction , or  intentional mistakes :  The instructor provides statements, readings, proofs, or other material that contains errors.  The students are charged with finding and correcting the errors.  Concepts that students commonly misunderstand are well suited for this activity.
• Strip sequence , or  sequence reconstruction : The goal of this activity is for students to order a set of items, such as steps in a biological process or a series of historical events.  As one strategy, the instructor provides students with a list of items written on strips of paper for the students to sort.  Removable labels with printed items also work well for this activity.
• Polling :  During class, the instructor asks a multiple-choice question.  Students can respond in a variety of ways.  Possibilities include applications such as  PollEverywhere  or  Learning Catalytics .  In some courses, each student uses a handheld clicker, or personal response device, to record their answers through software such as  TurningPoint  or  iClicker .  Alternatively, students can respond to a multiple-choice question by raising the appropriate number of fingers or by holding up a colored card, where colors correspond to the different answers. A particularly effective strategy is to ask each student to first respond to the poll independently, then discuss the question with a neighbor, and then re-vote.

ABL Connect  provides more in-depth information about and examples of many of these activities.

In addition to these classroom-based strategies, instructors might take students out of the classroom; for example, students can visit museums or libraries, engage in field research, or work with the local community. 

For more information...

Tipsheet: Active Learning

PhysPort resources on stimulating productive engagement

Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How Learning Works: Seven Research-based Principles for Smart Teaching. Chicago, IL: John Wiley & Sons.

Bain, K. 2004. What the best college teachers do. Cambridge, MA: Harvard University Press.

Brookfield, S. D., & Preskill, S. (2012). Discussion As a Way of Teaching: Tools and Techniques for Democratic Classrooms. Chicago, IL: John Wiley & Sons.

Brookfield, S. D., & Preskill, S. (2016). The Discussion Book: 50 Great Ways to Get People Talking. San Francisco, CA: Jossey-Bass.

Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make It Stick: The Science of Successful Learning, 1st Edition. Cambridge, MA: Harvard University Press.

Handelsman, J., Miller, S., & Pfund, C. 2007. Scientific teaching. New York: W. H. Freeman and Company.

Lang, J. (2010). On Course: A Week-by-Week Guide to Your First Semester of College Teaching, 1st Edition. Cambridge, MA: Harvard University Press.

Lang, J. (2016). Small Teaching: Everyday Lessons from the Science of Learning. San Francisco, CA: Jossey-Bass.

Millis, B. J. 1990. Helping faculty build learning communities through cooperative groups. Available: http://digitalcommons.unl.edu/podimproveacad/202/ [2017, August 31].

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Active Learning

What is active learning.

Active learning generally refers to any instructional method that engages students in the learning process beyond listening and passive note taking. Active learning approaches promote skill development and higher order thinking through activities that might include reading, writing, and/or discussion. Metacognition -- thinking about one’s thinking -- can also be an important element, helping students connect course activities to their learning (Brame, 2016).

Active learning is rooted in constructivist learning theory , or the idea that students (humans!) learn by connecting new information and experiences to their prior knowledge and experiences, allowing them to build, or construct, new knowledge and understandings (Bransford et al., 1999). Often, although not exclusively, active learning approaches also include collaborative and cooperative learning in small groups. These approaches stem from social constructivism , which emphasizes the importance of peer-to-peer interactions in learning (Vygotsky 1978).

Beyond the theoretical underpinnings, many studies across disciplines have explored the benefits of active learning approaches in college classrooms (e.g., Freeman et al., 2014; Prince et al., 2004). Active learning strategies provide valuable opportunities for students to develop disciplinary skills and expertise, including serving as sources of knowledge, formulating questions and articulating ideas, as well as fostering interactions with peers (Turpen & Finkelstein, 2009). Perhaps most notably, compared to traditional lecture alone, use of active learning approaches has been shown to increase student performance and decrease failure rates, particularly for students from underrepresented and excluded communities (Eddy & Hogan, 2014; Haak et al., 2011; Theobald et al., 2020).

What are some strategies that I might try? 

There are many different active learning strategies that instructors might incorporate into their teaching. These can range from brief interactions during lecture, activities that may take 10-20 minutes, to strategies that could span multiple class periods. The table below outlines a variety of sample strategies with tips for both in-person and remote implementation in courses. The strategies are roughly organized based on potential time-intensity for implementation. Instructors might also explore these active learning designs as they consider opportunities for using each strategy.

Purposeful Pause

Quick write or “minute” paper, think-pair-share (tps), polling/peer instruction, concept map, case study/group problem solving, think-aloud problem solving, gallery walk, what can active learning look like in practice.

In this section, we’ve included several resources with videos that describe different types of active learning strategies and how to implement them. Many also demonstrate active learning strategies in action.

REALISE videos, SEER Center, University of Georgia

Scientific Teaching Series , iBiology

Community-building active learning strategies (remote context), OneHE 

How might I get started?

• Check out this active learning “cheat sheet” with 10 tips to help you get started, from choosing the “right” exercise to planning the logistics.
• If you are new to active learning, you might start with identifying strategies to incorporate into your lecture (see these resources on lecturing and interactive lecturing ).
• Have more questions, or interested in brainstorming for some ideas? Reach out to the Center for Teaching and Learning ( [email protected] ) for a consultation !

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What additional resources are available, active learning guides:.

• Active Learning Teaching Guide , Vanderbilt CFT
• Introduction to Active Learning , Michigan CRLT
• Active Learning , Yale Poorvu Center

Advice and strategies related to remote active learning:

• Hybrid active learning strategies , Eberly Center, CMU
• Flipping the remote classroom , Berkeley CTL

For a deeper dive:

Check out these research summaries describing common active learning techniques.

Polling with a student response system:

This Clicker Resource Guide (see PDF ) has some helpful advice for using polling questions in class with a student response system (e.g., iClicker Cloud or Poll Everywhere), including tips for logistics and "choreography" for implementation. It also touches on writing effective conceptual questions that are multiple choice.

Additional group-based learning approaches:

• Process-oriented guided inquiry learning (POGIL)
• Problem-based learning (PBL) (see also: the Problem Library ) and working in teams .

References:

Angelo, T.A. and Cross, K.P. (1993). Classroom assessment techniques: a handbook for college teachers. San Francisco: Jossey-Bass. Aronson, E.; Blaney, N.; Stephin, C.; Sikes, J., & Snapp, M. (1978). The jigsaw classroom. Beverley Hills, CA: Sage Publishing Company Aronson, Elliot. (2000) The jigsaw classroom. Retrieved from https://www.jigsaw.org/ . Barkley, Elizabeth F., K. Patricia Cross, and Clair H. Major. (2014) Collaborative learning techniques: A handbook for college faculty. Jossey-Bass. (available online and downloadable through the UC Berkeley Library; includes adaptations for synchronous and asynchronous instruction). Brame, C. (2016). Active learning. Vanderbilt University Center for Teaching. Retrieved March 10, 2021 from https://cft.vanderbilt.edu/active-learning/ . Bransford, J.D., Brown, A.L., and Cocking, R.R. (Eds.) (1999). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press. Christensen, C.R. (1987). Teaching and the case method. Boston: Harvard Business School. Crouch, C.H. and Mazur, E. (2001). Peer instruction: ten years of experience and results. Am. Journal of Physics 69, 970-977. Eddy, S. L., & Hogan, K. A. (2014). Getting under the hood: How and for whom does increasing course structure work?. CBE—Life Sciences Education, 13(3), 453-468. Fagen, A.P., Crouch, C.H., and Mazur, E. (2002). Peer instruction: results from a range of classrooms. Physics Teacher 40, 206-209. Francek, M. (2006). Promoting Discussion in the Science Classroom Using Gallery Walks. Journal of College Science Teaching, 36(1). Francek, Mark. "What is Gallery Walk?". Starting Point-Teaching Entry Level Geoscience. Retrieved March 24, 2021. Freeman, S., Eddy, S.L., McDonough, M., Smith, M.K., Okoroafor, N., Jordt, H., and Wenderoth, M.P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences USA 111, 8410-8415. Haak, D.C., HilleRisLambers, J., Pitre, E., and Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science 332, 1213–1216. Handelsman, J., Miller, S., and Pfund, C. (2007). Scientific teaching. New York: W.H. Freeman. Herreid, C.F. (1994). Case studies in science: A novel method of science education. Journal of College Science Teaching, 23(4), 221-229 Lyman, F. (1981). The responsive classroom discussions: the inclusion of all students. A. Anderson (Ed.), Mainstreaming Digest, College Park: University of Maryland Press, pp. 109-113. Millis, B. J., & Cottell Jr, P. G. (1997). Cooperative Learning for Higher Education Faculty. Series on Higher Education. Oryx Press, PO Box 33889, Phoenix, AZ 85067-3889. Nesbit, J.C. & Adesope, O.O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413-448. Novak, J.D. and Canas, A.J. (2008). The theory underlying concept maps and how to construct and use them. Technical Report IHMC CmapTools 2006-01 Rev 2008-01 (retrieved from http://cmap.ihmc.us/docs/theory-of-concept-maps ). Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education 93, 223-231. Rivard, L. O. P. (1994). A review of writing to learn in science: Implications for practice and research. Journal of Research in Science Teaching, 31(9), 969-983. Rowe, M.B. (1980). Pausing principles and their effects on reasoning in science. In Teaching the Sciences, edited by F. B. Brawer. New Directions for Community Colleges No. 31. San Francisco: Jossey-Bass. Ruhl, K., Hughes, C.A., and Schloss, P.J. (1987). Using the Pause Procedure to enhance lecture recall. Teacher Education and Special Education 10, 14-18. Smith, M. K., W. B. Wood, W. K. Adams, C. Wieman, J. K. Knight, N. Guild, and T. T. Su. (2009). “Why Peer Discussion Improves Student Performance on In-Class Concept Questions.” Science, 323, 122-24. Tanner, K. D. (2012). Promoting student metacognition. CBE—Life Sciences Education, 11(2), 113-120. Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., ... & Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476-6483. Turpen, C., & Finkelstein, N. D. (2009). Not all interactive engagement is the same: Variations in physics professors’ implementation of peer instruction. Physical Review Special Topics-Physics Education Research, 5(2), 020101. Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.

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Active learning is any approach to instruction in which all students are asked to engage in the learning process. Active learning stands in contrast to "traditional" modes of instruction in which students are passive recipients of knowledge from an expert.

Active learning can take many forms and be executed in any discipline. Commonly, students will engage in small or large activities centered around writing, talking, problem solving, or reflecting. 

What is active learning?

Active learning refers to a broad range of teaching strategies which engage students as active participants in their learning during class time with their instructor. Typically, these strategies involve some amount of students working together during class, but may also involve individual work and/or reflection. These teaching approaches range from short, simple activities like journal writing, problem solving and paired discussions, to longer, involved activities or pedagogical frameworks like case studies, role plays, and structured team-based learning.

Samples of Active Learning Activities

In a “traditional” class, it is common for only some students in a given course to participate in asking or responding to questions. In contrast, a class with successful active learning activities provide an opportunity for all students in a class to think and engage with course material and practice skills for learning, applying, synthesizing, or summarizing that material.

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Making Time for Active Learning

Using active learning strategies does not require abandoning the lecture format. Rather, adding small active learning strategies can make lecturing more effective for student learning. These activities give students just a minute or two to check their understanding of recent material, practice a skill or highlight gaps in their knowledge before giving an explanation.

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Active learning.

Active learning methods ask students to engage in their learning by thinking, discussing, investigating, and creating. In class, students practice skills, solve problems, struggle with complex questions, make decisions, propose solutions, and explain ideas in their own words through writing and discussion. Timely feedback, from either the instructor or fellow students, is critical to this learning process. Education research shows that incorporating active learning strategies into university courses significantly enhances student learning experiences (Freeman et al., 2014; Theobald et al., 2020).

Benefits of Active Learning

• Opportunities to process course material through thinking, writing, talking, and problem solving give students multiple avenues for learning.
• Applying new knowledge helps students encode information, concepts, and skills in their memories by connecting it with prior information, organizing knowledge, and strengthening neural pathways
• Receiving frequent and immediate feedback helps students correct misconceptions and develop a deeper understanding of course material
• Working on activities helps create personal connections with the material, which increases students’ motivation to learn
• Regular interaction with the instructor and peers around shared activities and goals helps create a sense of community in the classroom
• Instructors may gain more insight into student thinking by observing and talking with students as they work
• Knowing how students understand the material helps instructors target their teaching in future lessons

Considerations

• Design activities around your learning outcomes, especially with topics students typically find confusing
• Be clear about how activities relate to learning outcomes since students do not always make that connection on their own
• Be aware that you will need to cut content from your lectures to make room for discussion and activities; review your lectures and remove the least important parts; also consider asking students to read before the class meets and take a low-stakes online quiz or complete an online discussion board post so that they come to class ready to learn more advanced topics
• Plan to pause your lecture 2 or more times for activities; these can be as simple as asking students to discuss their thoughts on a question with someone sitting next to them
• Use active learning consistently so students know what to expect in class
• Build-in accountability for individual and group work (offering participation points is one way to show your students that you value the activities and their participation); for example, ask students to answer polling questions, upload a photo of their worksheet to Canvas, or turn in an index card with a response to a short writing prompt at the end of class
• When students are working on an activity in class, it is helpful for you and/or your TAs to move around the classroom to answer questions and interact with students to learn more about how they are thinking; these interactions can inform ways to follow up after an activity with clarification or to highlight student ideas
• Also, consider the value of peer feedback, such as in the form of a think-pair-share discussion with someone sitting near them

Talking to Students About Active Learning

Many students are beginning to expect their classes should include some interaction and opportunities to practice, discuss, or apply what they are learning. The best way to ensure that you and your students have a positive experience with active learning is to be transparent about how you will use it and why.

On the first day of class:

• let students know that your course uses active learning and that they will be expected to participate (add this to your course description and syllabus too)
• explain why you are using active learning and how it will help them succeed in your class (connect it to skills they will need beyond Cornell)
• point them to the latest research on learning demonstrating that students learn more and earn higher grades with active learning (e.g., Deslauriers et al., 2019)
• use a quick icebreaker or two to help students become comfortable working with one another
• introduce an active learning activity to set the expectation for an interactive class

Getting Started

Our  Active learning techniques  page offers a range of ideas that instructors can adopt whether they are just starting out with active learning or are looking for new strategies. Instructors across Cornell (from the humanities to STEM) are using these techniques, which can be adapted to almost any course.

Since 2014, Cornell has encouraged the adoption of high-impact practices across the university through its  Active Learning Initiative  (ALI). Funding from the Initiative has helped faculty redesign their courses and implement active learning teaching strategies. ALI has a broad network of faculty who have implemented active learning in different ways and who meet to share their ideas and experiences.

The best way to learn about active learning is to see it in action. If you have a colleague who uses active learning, ask to observe their class, or contact CTI for information on courses where active learning is being used.

Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019).  Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom .  Proceedings of the National Academy of Sciences ,  116 (39), 19251–19257.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014).  Active learning increases student performance in science, engineering, and mathematics .  Proceedings of the National Academy of Sciences ,  111 (23), 8410–8415.

Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., … Freeman, S. (2020).  Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math .  Proceedings of the National Academy of Sciences ,  117 (12), 6476–6483.

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Active Learning

What is active learning.

Active learning is a term used to describe instructional strategies that promote students’ active participation in knowledge construction processes. Such strategies may include hands-on activities, brief writing and discussion assignments, problem solving tasks, information gathering and synthesis, question generation, and reflection-based activities, among others. Together, these approaches seek to engage learners’ higher order thinking skills through the production and articulation of knowledge, as opposed to through the passive transmission of facts and ideas.

Active learning strategies are built upon constructivist theories of learning, which emphasize the importance of building connections between one’s prior knowledge and new experiences and concepts. As such, active learning tasks are designed to tease out learners’ current understanding, make that understanding explicit, and then create opportunities for learners to integrate new knowledge into their understanding.

Typically, active learning strategies involve a mixture of individual and collaborative tasks, giving students the chance to reflect or predict outcomes, and then to share and discuss their ideas with peers. Activities can last anywhere from mere minutes to large segments of a class period; the point is simply to activate learners’ cognitive processes while they are in class. The information below will help you design and implement strategies that support this decidedly broad category of instructional methods.

What are the benefits?

Active learning helps students reflect on their understanding by encouraging them to make connections between their prior knowledge and new concepts. Often, active learning tasks ask students to make their thinking explicit, which also allows instructors to gauge student learning. Although most of the literature on active learning has focused on STEM disciplines, research suggests that active learning may benefit students in any field, particularly students who have had fewer educational opportunities, or encounters with active learning in high school. Several studies have shown that students in active learning classrooms have a lower rate of failure, and perform better on assessments than students in a traditional lecture.

Best practices

Because active learning encompasses so many different varieties of classroom activity, it is important to keep in mind a few core principles when designing active learning tasks:

• Active learning tasks should help your students meet their learning objectives
• Active learning tasks should create a low bar for student participation
• Active learning tasks should provide students with feedback on their learning

Help students meet their learning objectives

Above all, active learning tasks should target specific learning objectives. That is, they should help students develop the knowledge and skills that they are expected to acquire in your course. Identifying an argument, using evidence to support a claim, organizing information, and defining a given problem are all skills that support complex learning objectives, such as writing and problem solving. Active learning tasks should aim to provide students with opportunities to practice and gain proficiency in such skills.

Encourage student participation

Active learning tasks should provide a low barrier-to-entry, and invite involvement among all students. Therefore, tasks should be simple or discrete. For more complex tasks, instructors should provide clear instructions that outline (and model) how students will participate in the activity. How will students engage with each other in the activity? What are the ground rules or guidelines for group interaction? Answering these questions explicitly will help students understand what is expected of their participation.

Provide opportunities for feedback and reflection

Ideally, feedback should not only target the skills and knowledge students are expected to acquire from the course learning objectives, it should clearly indicate how students can improve their performance or enhance their understanding of the topic at hand. While providing detailed, individual feedback is often time consuming for individual instructors, and therefore difficult to achieve in a single class period, feedback from an active learning task can come from a variety of sources. Personal Response Systems (e.g., “clickers”), for instance, can collect input on student thinking at large scale. Instructors can, in turn, compare this information with experimental data or examples of expert thinking to reveal “gaps” or discrepancies in student knowledge.

Peer-based discussions or review sessions in which students receive a rubric with which to assess their classmates’ learning also provide opportunities for students to both make their thinking explicit, and to obtain informal feedback. The purpose of feedback in such cases is to provide students with information on their understanding or performance that can guide them towards a desired learning goal. Whether it come from a digital tool such as a clicker, or from a classmate, active learning tasks should give students a sense of their learning progress, and help them hone further practice.

Examples of active learning

To be sure, there are many examples of classroom tasks that might be classified as “active learning.” Some of the most common examples include think-pair-share exercises, jigsaw discussions, and even simply pausing for clarification during a lecture. Members of the University of Michigan’s Center for Research on Learning and Teaching have created a useful list of active learning techniques , which they have sorted according to a “continuum” of complexity and time commitment. These techniques include:

• Minute Papers: at some point during lecture, students are asked to for one or two minutes on a given topic.
• Self-Assessment: similar to concept inventories and diagnostic assessments, these ungraded exercises, typically delivered at the beginning of a term or new unit, are used to help identify gaps in student understanding.
• Interactive Lectures: often in the form of brief polls, these activities take place during lectures, giving students a chance to make predictions, solve short problems, etc.
• Inquiry Learning: larger in scope, these exercises commonly involve having students conduct different aspects of scientific inquiry, such as observing phenomena, analyzing data, predicting outcomes, etc.
• Video demonstrating active learning techniques in a large enrollment STEM course here at BU: https://mymedia.bu.edu/media/Active+Learning+in+Large+Classrooms/1_645lb6rt

For a full list of techniques, download the UMich CRLT’s handout on active learning .

Quick tips for getting started with active learning

• What topics or ideas do students struggle with most in your course?
• What data or information will help you understand what students are learning?
• Which active learning strategies will provide this data, and ultimately help your students meet their learning objectives?
• Prepare a timeline to help you manage the activity. Will it take place in the classroom? How long will it last? What instructions will students need to participate in the activity?
• Establish ground rules for the activity. How should students interact with each other? What are they expected to do during the activity?
• Consider any roadblocks or challenges that you and your students experienced in carrying out the activity. How might these be overcome?
• Elicit feedback from students on whether or not the activity assisted in their learning. Did they find the activity helpful?
• Assess the usefulness of the information the activity provided you. Did the students improve their understanding of the topic or concept? Can you use data from the activity to make further improvements to future activities or instruction in general?

Additional Resources

Overview and Examples of Active Learning (Harvard Bok Center for Teaching and Learning)

Steps to Creating an Active Learning Environment (NYU Center for the Advancement of Teaching)

Active Learning Resources and Research (UMich Center for Research on Learning and Teaching

You may also be interested in:

Assessing learning, student engagement part 2: ensuring deep learning, experiential learning, service learning, getting stared with generative ai, project-based learning, universal design for learning: an introduction, game-based learning & gamification.

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• v.13(3); Fall 2014

Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

Debra l. linton.

*Department of Biology, Central Michigan University, Mount Pleasant, MI 48859

Wiline M. Pangle

Kevin h. wyatt.

† Department of Biology, Ball State University, Muncie, IN 47306

Karli N. Powell

‡ Mathematics Department, Linden High School, Linden, MI 48451

Rachel E. Sherwood

§ Science Department, Garden City High School, Garden City, KS 67846

Associated Data

This study compared the effectiveness of three different methods of implementing active-learning exercises in an introductory biology course. The results suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning.

We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning.

INTRODUCTION

Research in science education has identified several effective student-centered pedagogical techniques that have become the cornerstone of national efforts to reform science teaching (see Vision and Change report, table 3.2 [ American Association for the Advancement of Science, 2011 ]). Cooperative group–based active learning is one of the most commonly implemented of these techniques ( Ruiz-Primo et al. , 2011 ). Cooperative group–based active learning has been tested repeatedly and has been shown to result in significant learning gains in many individual studies (e.g., Udovic et al. , 2002 , Knight and Wood, 2005 ; Armstrong et al. , 2007 ; Freeman et al. , 2007 ). Similarly, meta-analyses of active-learning research (e.g., Hake, 1998 ; Springer et al. , 1999 ; Prince, 2004 ; Wood, 2009 ; Ruiz-Primo et al. , 2011 ) have consistently supported the conclusion that these techniques can be effective in increasing student learning. However, in a random sample of college biology courses, Andrews et al. (2011) found that active-learning instruction did not correlate with student learning gains. They pointed out that instructors of courses in which science education research was being conducted were often science education researchers with knowledge and pedagogical experiences that facilitated implementation of activities. This leads us to expect that instructors without this knowledge may not have the same success implementing these strategies. Identifying key features of effective active learning is an important step in the dissemination of reformed teaching to these instructors through peer-reviewed literature and professional development programs (e.g., Pfund et al. , 2009 ; Ebert-May et al. , 2011 ; D'Avanzo, 2013 ).

There is a wide range of different pedagogical techniques that come under the umbrella term active learning . Prince ( 2004 ) defined active learning as “the process of having students engage in some activity that forces them to reflect upon ideas and how they are using those ideas” (p. 160). The differences come when we begin to look at what “some activity” means and how best to have students interact with it. Some examples of the types of activities commonly being implemented are problem-based learning, case studies, simulations, role-playing, conceptually oriented tasks, cooperative learning, and inquiry-based projects (e.g., Prince, 2004 ; Michael, 2006 ; Ruiz-Primo et al ., 2011 ). We focused our research on the technique identified by Ruiz-Primo et al . (2011) as the most common technique present in the research literature, which they identified as “conceptually oriented tasks + collaborative learning.” In this technique, students work in groups on a task that requires some application of concepts to a problem or question. Almost 50% of the studies included in the Ruiz-Primo meta-analysis reported on the use of this strategy. Their analysis showed an effect size of 0.46–0.54, with an effect size of 0.5 (half an SD) typically considered “moderate.” Yet we know that not all instructors who try this technique are successful ( Andrews et al. , 2011 ).

A practical definition of effective active learning can best be built through studies that target individual components of active-learning design and implementation, instead of the effect of active learning as a whole, to identify what makes an effective active-learning exercise. There are many possible variations in the way an activity can be implemented. For example, students can discuss activities in groups or complete them individually. Students may only discuss aspects of the activity with others or they may be asked to write about their understanding as part of the activity, either individually or with one person per team writing the group's answer. Clickers are sometimes used as part of the processing of an active-learning exercise. Clicker questions may be answered individually or discussed in a group, or group discussion can follow after individual answers. The instructor may explain the correct answer to the activity after the work is completed or the instructor may have individual groups share their answers with the class and ask other groups to critique their answers. There are many such variations, and each leads to a question that can be investigated to help us build a shared and evidence-based definition of which of these options is most effective.

Some investigators have begun to conduct this type of research on the effectiveness of different modes of implementation of specific active-learning techniques. For example, Smith et al. (2009) explored the effect of peer discussion in the context of cooperative group–based instruction. They showed that students learned from group discussion of clicker questions and were able to apply their learning to answer novel questions on the concepts discussed. In a follow-up study, Smith et al. (2011) compared three different modes of implementing peer discussion of clicker questions and found that a combination of peer discussion followed by instructor explanation provided greater learning gains than either alone. If the science education community confirms these results through continued study, then we could begin to build a shared definition that includes the idea that effective active learning should include peer discussion followed by instructor explanation.

Student writing is another common feature of active-learning exercises. While a writing component is often included with the purpose of providing formative assessment data to the instructor, the concept of “writing to learn” suggests that writing also helps increase students' comprehension of complex concepts ( Rivard, 1994 ). Writing about a concept requires students to examine and organize their thinking and thereby facilitates making connections between concepts ( Bangert-Drowns et al. , 2004 ). Writing also provides an opportunity for self-assessment and metacognition ( Armstrong et al. , 2008 ), as a learner is confronted with his or her own ability or inability to clearly articulate the concepts needed to answer a complex question. Meta-analyses of writing-to-learn research conducted in science ( Rivard, 1994 ; Reynolds et al. , 2012 ) and non-science ( Bangert-Drowns et al. , 2004 ) classrooms conclude that writing can improve student learning when implemented effectively. However, some studies (e.g., Armstrong et al. , 2008 ; Fry and Villagomez, 2012 ) did not find any effect of writing on student learning. These contradictory results have led to recommendations that future research should focus on determining the most effective implementation strategies for writing within specific instructional contexts. Within the context of active learning, students are often required to write about their understanding of a concept, either individually or in teams. The time required to grade and perhaps provide comments on written responses from hundreds of students in a large class setting is daunting. In addition, the time spent on student writing during class reduces time available for other activities and content coverage. A better understanding of the effects of writing on learner-centered outcomes would provide useful information as to whether or not writing is an effective use of class time.

The goal of our research was to identify some key features of effective active learning. In this study, we focused on evaluating the effectiveness of two implementation options, specifically peer discussion and writing, using a full factorial experimental design.

Experimental Design and Implementation

We implemented this research during one semester in three lecture sections of an introductory biology course for biology majors. At the start of the semester, there were ∼140 students in each section. The three sections were each taught by a different instructor. Instructor 1 (D.L.) has 14 yr of previous experience teaching large introductory biology courses and is a science education researcher who has been implementing cooperative group–based active-learning exercises for 10 yr. Instructor 2 (W.P.) has 4 yr of previous experience of teaching experience in large introductory courses and has been implementing active learning for 4 yr. Instructor 3 (K.W.) was teaching a large introductory biology course for the first time and had no previous experience implementing active-learning techniques.

The instructors met weekly to plan instruction and standardize delivery as much as possible, and the same lecture materials, in-class activities, and assessments were used in all sections. Students were assigned to four-person cooperative groups that were maintained throughout the semester. This course met three times each week for 50 min. During the first two class meetings, the lesson consisted of lecture interspersed with multiple-choice questions that students answered using personal response systems (i.e., clickers), individually or with group discussion. One day each week, students completed an in-class activity that required application, analysis, or synthesis of the major concepts covered during two previous lectures. The experimental treatments were implemented within these activities ( Figure 1 ). Each activity was preceded by one to three multiple-choice clicker questions dealing with key concepts for the activity. In all three treatments, students answered these questions without group discussion. The activity was then implemented. In one of the three sections, students completed the activity individually and wrote about their understanding of the concepts (writing-only treatment, WO). In the second section, students discussed the problem presented in the activity with their team, but did not write about their understanding (discussion-only treatment, DO). In the third section, students discussed the problem in their teams and then wrote individually about their understanding of the concepts (discussion and writing treatment, DW). The same clicker questions, followed by a new multiple-choice question, were then asked and answered by students without group discussion. After students turned in their activity worksheets, the entire activity was reviewed in a full-class discussion facilitated by the instructor. The three treatments were rotated by section each week, so each instructor implemented each of the three treatments a few times in his or her section. Students earned points based on the number of clicker questions answered correctly at the end of the activity and, in the WO and DW treatments, on the quality of their writing, to encourage students to make a good effort on all questions.

Experimental design.

Most of the activities took ∼30 min to implement (including the pre- and postclicker questions and full-class postprocessing). The DW treatment typically took a few minutes more than the DO and WO treatments. However, the three sections remained on pace with one another throughout the semester. The activities required students to perform one or more of the following tasks: making predictions, analyzing data, interpreting graphs, drawing models, explaining experimental results, and using evidence to support explanations of phenomena. We collected clicker performance data, activity writings, and exam writings for 10 different question sets based on these weekly activities, for the following concepts: the nature of matter, osmosis, transmission genetics, gene expression, natural selection, phylogenetics, community dynamics, ecosystems, carbon cycle, and global change. The essay questions on the exam were designed to be analogous (cover the same major concept and require the same skills) to the in-class activities. For example, for community dynamics, the in-class activity required students to make predictions, analyze data, and explain results based on Paine's (1966) classic Pisaster exclusion experiments from a rocky intertidal zone. On the midterm exam, students were asked to do the same with data from the Estes et al . (1998) sea otter study, and on the final exam, students predicted changes to a forest community based on proposed changes in population sizes of some species. A summary of each activity and the analogous exam questions are provided in the Supplemental Material. Many of these activities were based on published research studies ( Spencer et al. , 1991 ; Ebert-May et al. , 2003 ; Stedman et al. , 2004 ; Winder and Schindler, 2004 ; Konopka et al. , 2009 ; Nowick et al. , 2009 ).

The three instructors met each week to debrief that week's activity and to plan for the following week's instruction. Based on the debriefing discussions, three of the activities were eliminated from the research data analysis; the phylogenetics and ecosystems activities were not implemented with enough standardization between sections to ensure there were no other factors influencing student learning, while the genetics activity did not effectively make use of writing and was more computational in nature. For the seven other activities, we analyzed clicker performance, in-class (activity) writings, and midterm and final exam data. The midterm and final exams consisted of a mix of multiple-choice and written assessments, with 40–50% of points on each exam coming from student writing that included essay questions analogous to those completed during the in-class activities. For natural selection, community dynamics, carbon cycle, and osmosis, we collected writing data from the activity, midterm exam, and final exam. For global change, nature of matter, and gene expression, we collected writing data from the activity and one midterm exam.

Data Analysis

Three hundred and forty-six students signed the consent forms to participate in the study and were included in the analyses. Students who were not present on activity days were removed from the analysis of exam questions, as they had not received the treatment for that concept.

Clicker question scores on the repeated questions were compared post- versus preactivity for each student and for each activity. If the student improved on the clicker questions from pre- to postactivity, his or her clicker performance was coded as “1” for improvement. If the student did not improve or performed worse on the postactivity questions, his or her performance was coded as “0.” Sample sizes for clicker data, split by treatment and instructor, are shown in Table 1 .

Sample sizes for the clicker data and the exam writing data for all three instructors and across the three treatments a

a DO, discussion only; WO, writing only; DW, discussion and writing.

We coded all student writings for correct concepts based on coding rubrics developed for each assessment item by the research team. These rubrics were designed to parse out each individual correct concept that might be included in the students' writing. For example, the statement “Carbon dioxide from the atmosphere entered the plant through stomata in the leaves during photosynthesis” would be split into four concepts: 1) source of CO 2 is the atmosphere; 2) CO 2 enters the plant; 3) CO 2 enters through stomata in leaf; and 4) the process involved is photosynthesis. The “correct concepts” in the rubrics included not only statements of fact, but also explanations of concepts, identifications of causal mechanisms, and statements of evidence used to support a conclusion. Therefore, this coding scheme allowed us to distinguish between different levels of completeness and complexity of students' answers on the essay questions.

A team of three raters coded the essays and disparities were settled by discussion. We calculated a total number of correct concepts for each student for each written assessment item. For concepts that were tested on both a midterm and final exam, the scores on the two essays were totaled to give a composite score on that concept. A total of 4477 essays were coded. Student writings were deidentified before analysis, so the evaluators could not determine to which treatment each writing belonged. Sample sizes for exam writings, split by treatment and instructor, are shown in Table 1 . Sample sizes for in-class writings are shown in Table 2 .

Sample sizes for the in-class writing data for all three instructors and across the two writing treatments a

a WO, writing only; DW, discussion and writing.

Because of differences in the complexity of the assessment questions (some of which were multipart), the total number of concepts in the coding rubric varied widely among questions, ranging from seven (the nature of matter) to 26 (community dynamics). To combine all essay data into a single data set for analysis, we normalized the essay scores based on the highest number of concepts included by any student on each essay question. For example, if the highest number of concepts included by any student on an essay question was 10, students who included 10 concepts would be assigned a score of 1, while students who included eight concepts would be assigned a score of 0.8.

We used generalized linear mixed-effect models (GLMM) with binomial distributions to analyze our data set. Generalized linear models are an extension of ordinary linear regressions. These models relate the responses of dependent variables to linear combinations of “predictor” independent variables. Whereas ordinary linear regressions assume a normal error distribution, the generalized linear models can take on a variety of other distributions. In our study, we used a binomial error distribution, which best fit the nature of our data. Our analyses are also termed “mixed” because they include both random and fixed factors; here, our random factor was the student unique identifier. We attempted to identify and account for as many independent variables that could potentially influence student performance in our study as possible. The GLMM analysis identifies which variables had a significant effect and also identifies any significant interactions between the independent variables.

In our analyses, student identification was entered as the random effect to avoid pseudoreplication (as one student could be represented up to seven times if he or she completed all seven activities). Three dependent variables were considered: 1) the improvement or lack of improvement on the clicker questions; 2) the standardized score for the essay writing during an in-class activity; and 3) the standardized score for the exam essay questions. For each dependent variable, we conducted a GLMM with three independent variables: 1) instructor (Instructor 1, Instructor 2, or Instructor 3); 2) treatment (DO, WO or DW); and 3) the average multiple-choice score of students on all exams (three midterm exams and one final exam) to account for aptitude and individual effort of students. The concepts assessed on the multiple-choice questions were not included as part of the activities, so student learning of these concepts should not have been influenced directly by the effects of the writing and discussion treatments. We prefer this measure of what we are calling “aptitude” over ACT scores or incoming grade point average, because it includes not only students' natural abilities but also allows us to account for variation due to study time, student motivation during the course, and other unmeasurable variables outside the classroom that might influence student performance specifically during the period of the course. Significance of the different independent variables was evaluated using the Wald χ 2 test.

Students' average performance on clicker questions across all activities was compared pre- versus postactivity (repeated questions only) using a Wilcoxon signed-rank test, as the data were not normally distributed. We also evaluated the effect of student aptitude (the average score obtained in all exams on the multiple-choice questions) on student clicker scores (the average postclicker score that included the new question added at the end of the activity) using a Spearman correlation.

All analyses were performed in the statistical software package R, version 2.1.0 ( R Development Core Team, 2005 ), using two-tailed tests with α = 0.05. The GLMMs were carried out using the R library MASS (glmmPQL function; Breslow and Clayton, 1993 ; Wolfinger and O'Connell, 1993 ), while the Wald tests used the aod R library (wald.test function). Unless otherwise indicated, means ± SE are represented.

Clicker Question Performance

Students scored significantly higher on the postactivity clicker questions than on the preactivity questions ( V = 74,090, df = 1932, p < 0.0001) in all treatments. However, this improvement was not significantly different between treatments ( Figure 2 ; χ 2 = 2.8, df = 2, p = 0.24). Students' average multiple-choice exam scores did not correlate significantly with improvement in clicker scores from pre- to postactivity (χ 2 = 1.7, df = 1, p = 0.19). However, students' average multiple-choice exam grades were positively correlated with postactivity clicker scores ( r = 0.24, df = 1932, p < 0.0001). Students under different instructors did not differ in their improvement on their clicker scores (χ 2 = 3.8, df = 2, p = 0.15), nor were there treatment × instructor interactions ( Figure 2 ; χ 2 = 4.1, df = 4, p = 0.39).

Effects of treatments and instructors on student improvement on clicker questions. Preactivity clicker scores were compared with postactivity scores and coded as “0” if students obtained the same or a lower clicker score after the activity or as “1” if students improved their clicker scores after the activity. Student scores are averaged by treatment received (WO, DO, or DW) and by instructors; error bars represent SEs. There were no significant effects of treatment ( p = 0.24) or instructor ( p = 0.15) on changes in clicker scores, and there was no treatment × instructor interaction ( p = 0.39; see Results section for full GLMM results).

In-Class Writing Data

Students' average multiple-choice exam scores were very strongly correlated with how well students performed in the writing during an activity, regardless of the treatment they received in lecture (χ 2 = 111.6, df = 1, p < 0.0001). However, there were no differences between the WO and the DW treatments ( Figure 3 ; χ 2 = 0.29, df = 2, p = 0.87). There was a strong effect of instructor on students' performance during the in-class writing ( Figure 3 ; χ 2 = 13.3, df = 2, p < 0.001), with some instructors achieving higher student scores than other instructors regardless of the treatment and controlling for student aptitude. There were no significant treatment × instructor interactions ( Figure 3 ; all p values >0.25).

Effects of treatments and instructors on student writing scores during an in-class activity. Student writing samples were scored by the number of correct concepts and standardized across activities. Writing scores are corrected here for students' aptitude. Student scores are averaged by treatment received (WO or DW) and by instructor; error bars represent SEs. There is no significant effect of treatment ( p = 0.87); however, there is an effect of instructor ( p < 0.001). There are no significant treatment × instructor interactions ( p > 0.25; see Results section for full GLMM results). In this figure, the negative average residuals indicate treatment × instructor combinations that resulted in lower averages on the in-class writing than predicted by the overall regression model that includes all data. Positive residuals indicate treatment × instructor interactions that resulted in higher averages than predicted by the model.

Exam Writing Data

Students' average multiple-choice exam scores were a very strong predictor of how well students performed in the writing during an exam, regardless of the treatment they received in lectures ( Figure 4 ; χ 2 = 367.3, df = 1, p < 0.0001). The treatments received during the lecture activity did have a significant effect on how well students performed on the exam writing ( Figure 5 ; χ 2 = 7.2, df = 2, p = 0.027). The WO and DW treatments resulted in higher performance on written exam components than DO. There was also a strong effect of instructor ( Figure 5 ; χ 2 = 19.3, df = 2, p < 0.0001) and a significant treatment × instructor interaction ( Figure 5 ; χ 2 = 78.1, df = 4, p < 0.0001), with some instructors achieving better student scores consistently within a certain treatment, which was not the same for each instructor. As we would expect, there was a very strong correlation between student performance on the in-class writing and on the exam writing ( p < 0.0001).

Effect of students' average exam score on the exam writing scores. Student writing samples during exams were scored by the number of correct concepts and standardized across activities. Writing scores are corrected here for treatment and instructor effects. For an accurate representation of the large number of data points, the plot area was divided in bins, which are then shaded based on the number of data points contained (dark bins contain up to 15 data points; light bins contain 1–3 data points). There is a significant positive correlation between multiple-choice exam scores and exam writing scores ( p < 0.0001).

Effects of treatments and instructors on students' exam writing scores. Student exam writing samples were scored by the number of correct concepts and standardized across activities. Writing scores are corrected here for students' aptitude. Student scores are averaged by treatment received (DO, WO, or DW) and by instructors; error bars represent SEs. There are significant effects of treatment ( p = 0.027) and instructor ( p < 0.0001), as well as a significant treatment × instructor interaction ( p < 0.0001; see Results section for full GLMM results). In this figure, the negative average residuals indicate treatment × instructor combinations that resulted in lower averages on the exam writing than predicted by the overall regression model that includes all data. Positive residuals indicate treatment × instructor interactions that resulted in higher averages than predicted by the model.

Student Effect

Students with higher aptitude performed better on all assessments, including the clicker questions. Students' average multiple-choice exam scores were highly correlated to their scores on the in-class and exam writing. This was expected, because the multiple-choice scores were included in the analysis as a measure of student aptitude to allow us to partially control for that variable in comparisons among experimental treatments. However, student multiple-choice scores did not correlate with student improvement on the clicker questions. Although the stronger students scored higher on the clicker questions, students at all levels improved equally (on average) as a result of experiencing the activities. Similarly, there were no treatment × student interactions found in any of the analyses. Neither stronger nor weaker students were advantaged or disadvantaged by any of the treatments when compared with the other students.

Treatment Effect

There were no treatment effects on clicker performance or in-class writing assignments but there were treatment effects on the exam writing. Both writing treatments (WO and DW) provided higher student performance than the DO treatment. The lack of treatment effect on the clicker scores suggests that improvement of clicker scores (from pre- to postactivity) was a result of participation in the activity, regardless of the method of implementation. The lack of significant difference between the WO and DW treatments on the in-class activity writing indicates that peer discussion did not improve student learning over that achieved by students thinking and writing individually. Instead, students were able to answer the activity questions as well on their own as they were after peer discussion.

Our results show that students who write about a concept perform better on subsequent writing-based assessments of that concept compared with students who only discuss the concept with peers in cooperative groups. We do not assume that this increased performance is a direct measure of increased student understanding of the concepts targeted by the activities. This increased performance could be due to increased understanding, but it could also be due to increased ability to communicate understanding in writing or increased retention of knowledge. The DW treatment did require more time than the DO or WO treatments; however, the fact that there was no significant difference found between the WO and DW treatments indicates that “time on task” was not a major factor.

The lower performance of the DO treatment suggests that writing is more important to student learning than peer discussion. With that said, we do not interpret our data as indicating that discussion is not important. In fact, other studies have reported gains in conceptual understanding following peer discussion (e.g., Smith et al. , 2009 ). We documented a similar trend during clicker activities, with students in the DO treatment improving their scores on the clicker questions as a result of peer discussion of the activity. However, the DW and WO treatments led to similar improvements on the clicker questions and higher performance on the exam writings than did the DO treatment.

Research on “learning by explaining” has shown that explaining concepts can have a strong effect on learning and helps students make generalizations when presented with new applications of the same concepts ( Williams et al. , 2010 ). Explaining has also been shown to facilitate conceptual change by encouraging metacognition ( Williams et al. , 2010 ). There is a distinction made in this literature between explaining to self and explaining to others ( Ploetzner et al. , 1999 ). A student writing individually is essentially explaining to self, while during peer discussion, students are explaining to others. Early research on learning by explaining hypothesized that explaining to others would lead to greater learning by the explainer than explaining to self, but subsequent studies have not found this to be the case ( Ploetzner et al. , 1999 ). This suggests that peer discussion should be as effective as writing. All three treatments in our study required that students explain their understanding of the concepts. However, the mode of explanation was different in the three treatments. Students explained their understanding verbally, made written explanations, and made both verbal and written explanations. Based on the writing-to-learn literature (e.g., Rivard, 1994 ), we predicted that writing would require more careful organization of student thinking and, by doing so, might lead to greater understanding; our data support this prediction.

We have noted that, during a group discussion, it is rare that each student explains his or her understanding. Typically, one or two students attempt an explanation and the others agree, sometimes disagree and explain why, but often one or two team members do not speak during any given discussion (personal observation). Enforcing individual writing requires each student to explain his or her thinking. This could further explain why writing was more effective than peer discussion in this study.

Instructor Effect

The effect of instructor was strong for both the in-class and exam writings; however, we do not have sufficient data to infer what may have caused this difference. Previous research that included an instructor effect identified that both years of experience and training with active-learning techniques were important variables ( Andrews et al. , 2011 ). There was a wide disparity among the three instructors in both overall number of years of teaching large introductory biology courses and experience with active learning. The less-experienced instructors in our study did have reduced success implementing some of the student-centered activities. However, we have no way of separating the effect of overall teaching experience and experience with active learning, as we did not have an experienced instructor in our study who was new to active learning. Therefore, our small sample size for this variable does not allow us to make inferences about this effect.

Treatment × Instructor Interaction

There was a significant treatment × instructor interaction for the exam writing data. Different instructors may have been more effective with different treatments. However, this apparent interaction may have been an artifact of the differences in the difficulty of different concepts or the difficulty of the exam questions on those concepts. In our data set, the exam writing scores for the gene expression activity were considerably lower than the scores for all other activities across all three treatments. The treatment × instructor interaction may have been influenced by this trend. Instructor 1 used the WO treatment for the gene expression activity, Instructor 2 used the DW treatment, and Instructor 3 used the DO treatment. These treatments were identified by the GLMM analyses to be the ones that were least successful for these instructors ( Figure 5 ). While it is still possible that different instructors could be inherently more effective with some pedagogical techniques than with other techniques, our data do not provide rigorous evidence to support this idea.

CONCLUSIONS

Our results provide evidence that individual writing should be included as part of cooperative group–based active-learning exercises whenever possible. Although writing uses class time, this appears to be time well spent. Individual student writing not only provides formative assessment data but also promotes metacognition, as students are confronted with trying to organize the understanding of concepts, making connections, and justifying their thinking. A major assumption implicit in this type of research is the assumption that “effectiveness” is validly measured by student performance on course assessments. We consider the effectiveness of a technique to be determined by how well it facilitates student learning. A technique or activity is effective if it helps students understand the concepts being presented, and it is “more effective” if students understand better after experiencing this technique or activity than another with which it is being compared. We used performance on our assessments as a proxy measurement for students' understanding. Our results show that the writing treatments led to significantly higher student performance on our assessments than the discussion treatment.

Although our results indicate that peer discussion is not as effective as writing in facilitating student learning, we do not recommend that it be removed from active-learning exercises. In addition to the previous research cited earlier supporting the inclusion of peer discussion, there are other learning objectives that can be met using this technique. Collaboration and verbal communication skills are often objectives for introductory biology courses, and these objectives will not be met by student writing alone. Further research is needed to determine the most effective mix of discussion and writing.

The effect of instructor is a variable that should not be overlooked in national reform efforts. Although our data are limited in their ability to inform this question, the significant effect we found lends support to the idea that this may be a key variable to be addressed. There is a growing realization that effective dissemination of active-learning techniques is a bottleneck to the transformations called for in Vision and Change ( Ebert-May et al. 2011 ; D'Avanzo, 2013 ). As a community, we can find ways to make these techniques accessible (note that we do not say “easily accessible”) to any instructor willing to make the effort.

One limitation to our experimental design was that it did not allow us to analyze the effect (or effectiveness) of the different activities. While the activities we designed varied somewhat in their complexity, all activities required students to make an explanation of a biological phenomenon based on evidence, either experimental results or models that students developed to support their thinking. However, the much lower assessment scores on one of the concepts indicates that the activity associated with that concept did not meet the learning objectives, regardless of the implementation method. We suggest that more focus should be given to testing the effectiveness of specific activities that instructors design for different concepts and that the results be published for the community to share. In the same way that we would share a new technique in molecular biology, we should be able to publish procedures that our colleagues can follow (and practice and improve on) to achieve the desired results.

In conclusion, at the implementation level, we recommend the increased use of individual student writing during active-learning exercises. At the theoretical research level, we encourage more research into 1) the effect of “instructor” on the effectiveness of active learning and how to mitigate this effect and 2) the implementation strategies and types of activities that lead to the greatest student learning. At the practitioner research level, we call for increased rigorous testing and publication of specific active-learning exercises with detailed descriptions of the activities, evidence-based recommendations for implementation, and data on effectiveness. As we continue to study what makes an activity effective and to identify effective activities, we aim to make active learning more accessible to all instructors who are passionate about student learning.

Supplementary Material

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17 Great Active Learning Examples

Today’s education system highlights the importance of active learning over passive learning . An active strategy involves learning by doing rather than sitting, listening and repeating.

This approach highlights the importance of social interaction, discovery, play and trial-and-error for learning and development.

Examples of active learning strategies include:

• Learning through Play
• Group Projects
• Peer Teaching
• Think-Pair-Share
• A Kinesthetic Approach
• Phenomenon Based Learning
• Inquiry-Based Learning
• Discovery Learning
• Challenge Based Learning
• Gamification
• Game-Based Learning
• Guided Practice
• Experiential Learning

Below is an outline of each.

17 Active Learning Examples

1. Learning through Play

Play-based learning is a popular pedagogy for early years educators. It involves using hands-on, fun, and interactive experiences to stimulate cognitive development. When children learn through play, they can be engaged more willingly and in a more sustained way than if they learn passively. A play-based approach is embraced by theories such as Montessori, Steiner-Waldorf, Reggio Emilia and Froebel’s theory .

2. Role Play

Role play involves taking on different personas during a lesson in order to view things from various perspectives. It encourages critical and non-egotistical thinking, which may lead to  increased empathy and seeing issues from a more holistic angle. Children engage in role play during the ‘ symbolic ’ stage of play , but it remains an active approach that teachers employ in all levels of education.

3. Science Experiments

Science experiments help students to actually see the theoretical ideas we talk about like gravity, magnetism and cell structure.

Consider an experiment like using iron filings to visually show the traces of magnetic fields (see this great magnetic experiments kit on Amazon), using microscopes to examine cells , or setting off a rocket to explore stored potential energy.

Debates help students see things from multiple perspectives, use logic to defend their positions, and improve their public speaking skills. Teachers can split their class into two groups and ask them to take one perspective each, or get small groups to debate various different topics while the rest of the class observes and judges the winners.

5. Collaborative Learning

Group projects get students working together to solve problems. They force students to discuss issues, consider each others’ perspectives, and construct knowledge together to come to share agreements on how to go about projects. In particular, in the collaborative learning approach , students must take on an ‘active’ rather than ‘passive’ orientation to learning as students are responsible for developing shared knowledge.

Read Also: Collaborative vs. Cooperative Learning

6. Think-Pair-Share

Think-Pair-Share is a teaching strategy that asks students to start thinking about something alone. Then, students turn to the person next to them and discuss the issue as a pair. The pairs can change their minds or learn from each other to refine their thoughts. Then, students get into larger groups (or a whole class group) to ‘share’ their ideas with the whole class to stimulate further thinking.

7. A Kinesthetic Approach

Many theories of learning modalities hold that some students are kinesthetic learners. A kinesthetic learner will learn best through using their body in the learning process. This can include learning through gross motor movements (sports, for example), tactile experiences (e.g. touching something and feeling its features), or by ensuring they are exerting excess energy whenever possible.

8. Grab Bags

A grab bag is a great way to get students thinking and learning actively. Students are required to put their hands into an opaque bag (such as a canvas bag) and feel the item within the bag. They need to describe the item and guess what it is simply based on what they feel. It stimulated learning through tactile methods and encourages thinking skills to try to solve the mystery. Invite each student one at a time to come up and feel the item, then encourage them to share their thoughts on what it is that’s in the bag.

9. Phenomenon Based Learning

Phenomenon based learning is a 21st Century teaching method that originated in Finland. The approach emphasizes choosing a phenomenon to study rather than a ‘subject’ (such as mathematics, literacy, science, history, etc.). When students choose a phenomenon they are required to study it from multiple different disciplines and perspectives by conducting research into it in groups and reporting their findings to the class.

10. Inquiry Based Learning

An inquiry-based learning approach involves conducting scientific or systematic investigations into a topic under analysis. Students don’t sit-listen-observe, but rather go about following procedures to generate data about a topic. I like to use IBL with phenomenon based learning, where my students choose a phenomenon and we work in small groups to conduct inquiries into the chosen phenomenon.

11. Citizenship Education

Proponents of children’s citizenship argue that children should be considered full, active participants in society. This approach highlights that students of all ages need the right to have their voices and perspectives heard and respected by teachers and the school. It encourages speaking up, acting to contribute to school improvement, and taking votes on important matters affecting their lives.

12. Place Based Education

Place Based Education is an approach to environmental education that reinforces the importance of taking action in the local community to learn. Students find an area of need in their local community and work to improve that aspect of the community. It could involve volunteering, helping regenerate a natural environment, preserving heritage, and beautifying the city. It emphasizes the importance of learning that has tangible benefits for people in your life.

13. Gamification

Gamification involves turning regular lessons into games by incorporating elements of gameplay. This can include turning a boring lesson into a competition, winning points for answering questions correctly, ‘levelling up’ such as gaining a new rank or privilege after achieving a skill, or creating a ‘crack the code’ lesson. These examples of gamification make students more active and engaged learners by inserting fun and activity into lessons.

14. Game Based Learning

Game based learning involves using games to learn. Gamification involves incorporating ‘elements of games’ while game based learning brings whole games like dominoes (for math), Sim City (for city planning), Monopoly (for money management) and so on into a lesson. The game’s premise needs to overlap with your intended lesson outcomes.

15. Guided Practice

Guided practice involves the teacher gradually releasing responsibility to students. It starts with the teacher modelling a task, then having the students do the task with the teacher, then finally has the students doing the task independently. In this approach, the lesson starts with a traditional passive learning approach, and concludes with active learning after the students have built foundation knowledge and confidence.

16. Education for Sustainable Development

Education for Sustainable Development involves teaching about environmental sustainability through getting students to take action in their own lives. Students are asked to assess their own consumer behaviors and take action to become more environmentally responsible. This can include conducting ‘ biodiversity audits ’ and regenerating local ecosystems for flora and fauna, or auditing their own consumption and trying to reduce it week-on-week.

17. Situated Learning

Situated learning theory believes that students learn best while participating as apprentice in workforce-like environments. Students are placed within the environment and start as peripheral participants, observing and asking questions. As they develop confidence and competence, they gradually become more and more integrated into the setting until they are integral participants in the workforce.

18. Peer Teaching

Peer teaching is an approach to education where a student who is more advanced on a topic mentors a less advanced student. This approach is beneficial for both the advanced and apprentice learner. The advanced learner needs to refine their knowledge and structure it in a presentable way, while the apprentice learner gets to learn from a ‘ more knowledgeable other ’ in the classroom.

Final Thoughts

Active learning is an approach to education that encourages children to learn through hands-on physical scenarios. It encourages children to learn through trial, error and discovery rather than rote memorization . It has its basis in Piaget’s constructivist theory of learning which emphasizes ‘constructing’ knowledge rather than ‘absorbing’ information.

Active learning is increasingly understood to be the best approach to education. Since the rise of Vygotsky’s sociocultural theory and Piaget’s constructivist theory, education theorists have argued it as a way to help students develop deep knowledge, learn in contextually appropriate ways, and apply their learning in ways that are meaningful to their lives.

There is also an argument to be made for the ‘enjoyment’ factor of active lessons which may engage and motivate students for longer (Hyun, Ediger & Lee, 2017), giving them more engaged learning time than ‘boring’ passive approaches.

So, see if you can integrate these active learning examples into your classroom to stimulate learning!

References and Further Reading

Bartholomew, J. B., Jowers, E. M., Roberts, G., Fall, A. M., Errisuriz, V. L., & Vaughn, S. (2018). AL increases children’s physical activity across demographic subgroups. Translational Journal of the American College of Sports Medicine, 3 (1): 1.

Hyun, J., Ediger, R., & Lee, D. (2017). Students’ Satisfaction on Their Learning Process in AL and Traditional Classrooms. International Journal of Teaching and Learning in Higher Education, 29 (1), 108-118.

Johnson, R. T., & Johnson, D. W. (2008). AL: Cooperation in the classroom. The annual report of educational psychology in Japan, 47, 29-30.

Ramirez-Loaiza, M. E., Sharma, M., Kumar, G., & Bilgic, M. (2017). AL: an empirical study of common baselines. Data mining and knowledge discovery, 31 (2): 287-313.

Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences. Retrieved from: https://minds.wisconsin.edu/bitstream/handle/1793/60660/TR1648.pdf?sequence=1

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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• Open access
• Published: 15 March 2021

Instructor strategies to aid implementation of active learning: a systematic literature review

• Kevin A. Nguyen 1 ,
• Maura Borrego 2 ,
• Cynthia J. Finelli   ORCID: orcid.org/0000-0001-9148-1492 3 ,
• Matt DeMonbrun 4 ,
• Caroline Crockett 3 ,
• Sneha Tharayil 2 ,
• Prateek Shekhar 5 ,
• Cynthia Waters 6 &
• Robyn Rosenberg 7  

International Journal of STEM Education volume  8 , Article number:  9 ( 2021 ) Cite this article

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Despite the evidence supporting the effectiveness of active learning in undergraduate STEM courses, the adoption of active learning has been slow. One barrier to adoption is instructors’ concerns about students’ affective and behavioral responses to active learning, especially student resistance. Numerous education researchers have documented their use of active learning in STEM classrooms. However, there is no research yet that systematically analyzes these studies for strategies to aid implementation of active learning and address students’ affective and behavioral responses. In this paper, we conduct a systematic literature review and identify 29 journal articles and conference papers that researched active learning, affective and behavioral student responses, and recommended at least one strategy for implementing active learning. In this paper, we ask: (1) What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies? (2) What instructor strategies to aid implementation of active learning do the authors of these studies provide?

In our review, we noted that most active learning activities involved in-class problem solving within a traditional lecture-based course ( N = 21). We found mostly positive affective and behavioral outcomes for students’ self-reports of learning, participation in the activities, and course satisfaction ( N = 23). From our analysis of the 29 studies, we identified eight strategies to aid implementation of active learning based on three categories. Explanation strategies included providing students with clarifications and reasons for using active learning. Facilitation strategies entailed working with students and ensuring that the activity functions as intended. Planning strategies involved working outside of the class to improve the active learning experience.

To increase the adoption of active learning and address students’ responses to active learning, this study provides strategies to support instructors. The eight strategies are listed with evidence from numerous studies within our review on affective and behavioral responses to active learning. Future work should examine instructor strategies and their connection with other affective outcomes, such as identity, interests, and emotions.

Introduction

Prior reviews have established the effectiveness of active learning in undergraduate science, technology, engineering, and math (STEM) courses (e.g., Freeman et al., 2014 ; Lund & Stains, 2015 ; Theobald et al., 2020 ). In this review, we define active learning as classroom-based activities designed to engage students in their learning through answering questions, solving problems, discussing content, or teaching others, individually or in groups (Prince & Felder, 2007 ; Smith, Sheppard, Johnson, & Johnson, 2005 ), and this definition is inclusive of research-based instructional strategies (RBIS, e.g., Dancy, Henderson, & Turpen, 2016 ) and evidence-based instructional practices (EBIPs, e.g., Stains & Vickrey, 2017 ). Past studies show that students perceive active learning as benefitting their learning (Machemer & Crawford, 2007 ; Patrick, Howell, & Wischusen, 2016 ) and increasing their self-efficacy (Stump, Husman, & Corby, 2014 ). Furthermore, the use of active learning in STEM fields has been linked to improvements in student retention and learning, particularly among students from some underrepresented groups (Chi & Wylie, 2014 ; Freeman et al., 2014 ; Prince, 2004 ).

Despite the overwhelming evidence in support of active learning (e.g., Freeman et al., 2014 ), prior research has found that traditional teaching methods such as lecturing are still the dominant mode of instruction in undergraduate STEM courses, and low adoption rates of active learning in undergraduate STEM courses remain a problem (Hora & Ferrare, 2013 ; Stains et al., 2018 ). There are several reasons for these low adoption rates. Some instructors feel unconvinced that the effort required to implement active learning is worthwhile, and as many as 75% of instructors who have attempted specific types of active learning abandon the practice altogether (Froyd, Borrego, Cutler, Henderson, & Prince, 2013 ).

When asked directly about the barriers to adopting active learning, instructors cite a common set of concerns including the lack of preparation or class time (Finelli, Daly, & Richardson, 2014 ; Froyd et al., 2013 ; Henderson & Dancy, 2007 ). Among these concerns, student resistance to active learning is a potential explanation for the low rates of instructor persistence with active learning, and this negative response to active learning has gained increased attention from the academic community (e.g., Owens et al., 2020 ). Of course, students can exhibit both positive and negative responses to active learning (Carlson & Winquist, 2011 ; Henderson, Khan, & Dancy, 2018 ; Oakley, Hanna, Kuzmyn, & Felder, 2007 ), but due to the barrier student resistance can present to instructors, we focus here on negative student responses. Student resistance to active learning may manifest, for example, as lack of student participation and engagement with in-class activities, declining attendance, or poor course evaluations and enrollments (Tolman, Kremling, & Tagg, 2016 ; Winkler & Rybnikova, 2019 ).

We define student resistance to active learning (SRAL) as a negative affective or behavioral student response to active learning (DeMonbrun et al., 2017 ; Weimer, 2002 ; Winkler & Rybnikova, 2019 ). The affective domain, as it relates to active learning, encompasses not only student satisfaction and perceptions of learning but also motivation-related constructs such as value, self-efficacy, and belonging. The behavioral domain relates to participation, putting forth a good effort, and attending class. The affective and behavioral domains differ from much of the prior research on active learning that centers measuring cognitive gains in student learning, and systematic reviews are readily available on this topic (e.g., Freeman et al., 2014 ; Theobald et al., 2020 ). Schmidt, Rosenberg, and Beymer ( 2018 ) explain the relationship between affective, cognitive, and behavioral domains, asserting all three types of engagement are necessary for science learning, and conclude that “students are unlikely to exert a high degree of behavioral engagement during science learning tasks if they do not also engage deeply with the content affectively and cognitively” (p. 35). Thus, SRAL and negative affective and behavioral student response is a critical but underexplored component of STEM learning.

Recent research on student affective and behavioral responses to active learning has uncovered mechanisms of student resistance. Deslauriers, McCarty, Miller, Callaghan, and Kestin’s ( 2019 ) interviews of physics students revealed that the additional effort required by the novel format of an interactive lecture was the primary source of student resistance. Owens et al. ( 2020 ) identified a similar source of student resistance, which was to their carefully designed biology active learning intervention. Students were concerned about the additional effort required and the unfamiliar student-centered format. Deslauriers et al. ( 2019 ) and Owens et al. ( 2020 ) go a step further in citing self-efficacy (Bandura, 1982 ), mindset (Dweck & Leggett, 1988 ), and student engagement (Kuh, 2005 ) literature to explain student resistance. Similarly, Shekhar et al.’s ( 2020 ) review framed negative student responses to active learning in terms of expectancy-value theory (Wigfield & Eccles, 2000 ); students reacted negatively when they did not find active learning useful or worth the time and effort, or when they did not feel competent enough to complete the activities. Shekhar et al. ( 2020 ) also applied expectancy violation theory from physics education research (Gaffney, Gaffney, & Beichner, 2010 ) to explain how students’ initial expectations of a traditional course produced discomfort during active learning activities. To address both theories of student resistance, Shekhar et al. ( 2020 ) suggested that instructors provide scaffolding (Vygotsky, 1978 ) and support for self-directed learning activities. So, while framing the research as SRAL is relatively new, ideas about working with students to actively engage them in their learning are not. Prior literature on active learning in STEM undergraduate settings includes clues and evidence about strategies instructors can employ to reduce SRAL, even if they are not necessarily framed by the authors as such.

Recent interest in student affective and behavioral responses to active learning, including SRAL, is a relatively new development. But, given the discipline-based educational research (DBER) knowledge base around RBIS and EBIP adoption, we need not to reinvent the wheel. In this paper, we conduct a system review. Systematic reviews are designed to methodically gather and synthesize results from multiple studies to provide a clear overview of a topic, presenting what is known and what is not known (Borrego, Foster, & Froyd, 2014 ). Such clarity informs decisions when designing or funding future research, interventions, and programs. Relevant studies for this paper are scattered across STEM disciplines and in DBER and general education venues, which include journals and conference proceedings. Quantitative, qualitative, and mixed methods approaches have been used to understand student affective and behavioral responses to active learning. Thus, a systematic review is appropriate for this topic given the long history of research on the development of RBIS, EBIPs, and active learning in STEM education; the distribution of primary studies across fields and formats; and the different methods taken to evaluate students’ affective and behavioral responses.

Specifically, we conducted a systematic review to address two interrelated research questions. (1) What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies ? (2) What instructor strategies to aid implementation of active learning do the authors of these studies provide ? These two questions are linked by our goal of sharing instructor strategies that can either reduce SRAL or encourage positive student affective and behavioral responses. Therefore, the instructor strategies in this review are only from studies that present empirical data of affective and behavioral student response to active learning. The strategies we identify in this review will not be surprising to highly experienced teaching and learning practitioners or researchers. However, this review does provide an important link between these strategies and student resistance, which remains one of the most feared barriers to instructor adoption of RBIS, EBIPs, and other forms of active learning.

Conceptual framework: instructor strategies to reduce resistance

Recent research has identified specific instructor strategies that correlate with reduced SRAL and positive student response in undergraduate STEM education (Finelli et al., 2018 ; Nguyen et al., 2017 ; Tharayil et al., 2018 ). For example, Deslauriers et al. ( 2019 ) suggested that physics students perceive the additional effort required by active learning to be evidence of less effective learning. To address this, the authors included a 20-min lecture about active learning in a subsequent course offering. By the end of that course, 65% of students reported increased enthusiasm for active learning, and 75% said the lecture intervention positively impacted their attitudes toward active learning. Explaining how active learning activities contribute to student learning is just one of many strategies instructors can employ to reduce SRAL (Tharayil et al., 2018 ).

DeMonbrun et al. ( 2017 ) provided a conceptual framework for differentiating instructor strategies which includes not only an explanation type of instructor strategies (e.g., Deslauriers et al., 2019 ; Tharayil et al., 2018 ) but also a facilitation type of instructor strategies. Explanation strategies involve describing the purpose (such as how the activity relates to students’ learning) and expectations of the activity to students. Typically, instructors use explanation strategies before the in-class activity has begun. Facilitation strategies include promoting engagement and keeping the activity running smoothly once the activity has already begun, and some specific strategies include walking around the classroom or directly encouraging students. We use the existing categories of explanation and facilitation as a conceptual framework to guide our analysis and systematic review.

As a conceptual framework, explanation and facilitation strategies describe ways to aid the implementation of RBIS, EBIP, and other types of active learning. In fact, the work on these types of instructor strategies is related to higher education faculty development, implementation, and institutional change research perspectives (e.g., Borrego, Cutler, Prince, Henderson, & Froyd, 2013 ; Henderson, Beach, & Finkelstein, 2011 ; Kezar, Gehrke, & Elrod, 2015 ). As such, the specific types of strategies reviewed here are geared to assist instructors in moving toward more student-centered teaching methods by addressing their concerns of student resistance.

SRAL is a particular negative form of affective or behavioral student response (DeMonbrun et al., 2017 ; Weimer, 2002 ; Winkler & Rybnikova, 2019 ). Affective and behavioral student responses are conceptualized at the reactionary level (Kirkpatrick, 1976 ) of outcomes, which consists of how students feel (affective) and how they conduct themselves within the course (behavioral). Although affective and behavioral student responses to active learning are less frequently reported than cognitive outcomes, prior research suggests a few conceptual constructs within these outcomes.

Affective outcomes consist of any students’ feelings, preferences, and satisfaction with the course. Affective outcomes also include students’ self-reports of whether they thought they learned more (or less) during active learning instruction. Some relevant affective outcomes include students’ perceived value or utility of active learning (Shekhar et al., 2020 ; Wigfield & Eccles, 2000 ), their positivity toward or enjoyment of the activities (DeMonbrun et al., 2017 ; Finelli et al., 2018 ), and their self-efficacy or confidence with doing the in-class activity (Bandura, 1982 ).

In contrast, students’ behavioral responses to active learning consist of their actions and practices during active learning. This includes students’ attendance in the class, their participation , engagement, and effort with the activity, and students’ distraction or off-task behavior (e.g., checking their phones, leaving to use the restroom) during the activity (DeMonbrun et al., 2017 ; Finelli et al., 2018 ; Winkler & Rybnikova, 2019 ).

We conceptualize negative or low scores in either affective or behavioral student outcomes as an indicator of SRAL (DeMonbrun et al., 2017 ; Nguyen et al., 2017 ). For example, a low score in reported course satisfaction would be an example of SRAL. This paper aims to synthesize instructor strategies to aid implementation of active learning from studies that either address SRAL and its negative or low scores or relate instructor strategies to positive or high scores. Therefore, we also conceptualize positive student affective and behavioral outcomes as the absence of SRAL. For easy categorization of this review then, we summarize studies’ affective and behavioral outcomes on active learning to either being positive , mostly positive , mixed/neutral , mostly negative , or negative .

We conducted a systematic literature review (Borrego et al., 2014 ; Gough, Oliver, & Thomas, 2017 ; Petticrew & Roberts, 2006 ) to identify primary research studies that describe active learning interventions in undergraduate STEM courses, recommend one or more strategies to aid implementation of active learning, and report student response outcomes to active learning.

A systematic review was warranted due to the popularity of active learning and the publication of numerous papers on the topic. Multiple STEM disciplines and research audiences have published journal articles and conference papers on the topic of active learning in the undergraduate STEM classroom. However, it was not immediately clear which studies addressed active learning, affective and behavioral student responses, and strategies to aid implementation of active learning. We used the systematic review process to efficiently gather results of multiple types of studies and create a clear overview of our topic.

Definitions

For clarity, we define several terms in this review. Researchers refer to us, the authors of this manuscript. Authors and instructors wrote the primary studies we reviewed, and we refer to these primary studies as “studies” consistently throughout. We use the term activity or activities to refer to the specific in-class active learning tasks assigned to students. Strategies refer to the instructor strategies used to aid implementation of active learning and address student resistance to active learning (SRAL). Student response includes affective and behavioral responses and outcomes related to active learning. SRAL is an acronym for student resistance to active learning, defined here as a negative affective or behavioral student response. Categories or category refer to a grouping of strategies to aid implementation of active learning, such as explanation or facilitation. Excerpts are quotes from studies, and these excerpts are used as codes and examples of specific strategies.

Study timeline, data collection, and sample selection

From 2015 to 2016, we worked with a research librarian to locate relevant studies and conduct a keyword search within six databases: two multidisciplinary databases (Web of Science and Academic Search Complete), two major engineering and technology indexes (Compendex and Inspec), and two popular education databases (Education Source and Education Resource Information Center). We created an inclusion criteria that listed both search strings and study requirements:

Studies must include an in-class active learning intervention. This does not include laboratory classes. The corresponding search string was:

“active learning” or “peer-to-peer” or “small group work” or “problem based learning” or “problem-based learning” or “problem-oriented learning” or “project-based learning” or “project based learning” or “peer instruction” or “inquiry learning” or “cooperative learning” or “collaborative learning” or “student response system” or “personal response system” or “just-in-time teaching” or “just in time teaching” or clickers

Studies must include empirical evidence addressing student response to the active learning intervention. The corresponding search string was:

“affective outcome” or “affective response” or “class evaluation” or “course evaluation” or “student attitudes” or “student behaviors” or “student evaluation” or “student feedback” or “student perception” or “student resistance” or “student response”

Studies must describe a STEM course, as defined by the topic of the course, rather than by the department of the course or the major of the students enrolled (e.g., a business class for mathematics majors would not be included, but a mathematics class for business majors would).

Studies must be conducted in undergraduate courses and must not include K-12, vocational, or graduate education.

Studies must be in English and published between 1990 and 2015 as journal articles or conference papers.

In addition to searching the six databases, we emailed solicitations to U.S. National Science Foundation Improving Undergraduate STEM Education (NSF IUSE) grantees. Between the database searches and email solicitation, we identified 2364 studies after removing duplicates. Most studies were from the database search, as we received just 92 studies from email solicitation (Fig. 1 ).

PRISMA screening overview styled after Liberati et al. ( 2009 ) and Passow and Passow ( 2017 )

Next, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for screening studies with our inclusion criteria (Borrego et al., 2014 ; Petticrew & Roberts, 2006 ). From 2016 to 2018, a team of seven researchers conducted two rounds of review in Refworks: the first round with only titles and abstracts and the second round with the entire full-text. In both rounds, two researchers independently decided whether each study should be retained based on our inclusion criteria listed above. At the abstract review stage, if there was a disagreement between independent coders, we decided to pass the study on to the full text screening round. We screened a total of 2364 abstracts, and only 746 studies passed the first round of title and abstract verification (see PRISMA flow chart on Fig. 1 ). If there was still a disagreement between independent coders at the full text screening round, then the seven researchers met and discussed the study, clarified the inclusion criteria as needed to resolve potential future disagreements, and when necessary, took a majority vote (4 out of the 7 researchers) on the inclusion of the study. Due to the high number of coders, it was unusual to reach full consensus with all 7 coders, so a majority vote was used to finalize the inclusion of certain studies. We resolved these disagreements on a rolling basis, and depending on the round (abstract or full text), we disagreed about 10–15% of the time on the inclusion of a study. In both the first and second round of screening, studies were often excluded because they did not gather novel empirical data or evidence (inclusion criteria #2) or were not in an undergraduate STEM course (inclusion criteria #3 and #4). Only 412 studies met all our final inclusion criteria.

Coding procedure

From 2017 to 2018, a team of five researchers then coded these 412 studies for detailed information. To quickly gather information about all 412 studies and to answer the first part of our research question (What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?), we developed an online coding form using Google Forms and Google Sheets. The five researchers piloted and refined the coding form over three rounds of pair coding, and 19 studies were used to test and revise early versions of the coding form. The final coding form (Borrego et al., 2018 ) used a mix of multiple choice and free response items regarding study characteristics (bibliographic information, type of publication, location of study), course characteristics (discipline, course level, number of students sampled, and type of active learning), methodology (main type of evidence collected, sample size, and analysis methods), study findings (types of student responses and outcomes), and strategy reported (if the study explicitly mentioned using strategies to implementation of active learning).

In the end, only 29 studies explicitly described strategies to aid implementation of active learning (Fig. 1 ), and we used these 29 studies as the dataset for this study. The main difference between these 29 studies and the other 383 studies was that these 29 studies explicitly described the ways authors implemented active learning in their courses to address SRAL or positive student outcomes. Although some readers who are experienced active learning instructors or educational researchers may view pedagogies and strategies as integrated, we found that most papers described active learning methods in terms of student tasks, while advice on strategies, if included, tended to appear separately. We chose to not over interpret passing mentions of how active learning was implemented as strategies recommended by the authors.

Analysis procedure for coding strategies

To answer our second research question (What instructor strategies to aid implementation of active learning do the authors of these studies provide?), we closely reviewed the 29 studies to analyze the strategies in more detail. We used Boyatzis’s ( 1998 ) thematic analysis technique to compile all mentions of instructor strategies to aid implementation of active learning and categorize these excerpts into certain strategies. This technique uses both deductive and inductive coding processes (Creswell & Creswell, 2017 ; Jesiek, Mazzurco, Buswell, & Thompson, 2018 ).

In 2018, three researchers reread the 29 studies, marking excerpts related to strategies independently. We found a total of 126 excerpts. The number of excerpts within each study ranged from 1 to 14 excerpts ( M = 4, SD = 3). We then took all the excerpts and pasted each into its own row in a Google Sheet. We examined the entire spreadsheet as a team and grouped similar excerpts together using a deductive coding process. We used the explanation and facilitation conceptual framework (DeMonbrun et al., 2017 ) and placed each excerpt into either category. We also assigned a specific strategy (i.e., describing the purpose of the activity, or encouraging students) from the framework for each excerpt.

However, there were multiple excerpts that did not easily match either category; we set these aside for the inductive coding process. We then reviewed all excerpts without a category and suggested the creation of a new third category, called planning . We based this new category on the idea that the existing explanation and facilitation conceptual framework did not capture strategies that occurred outside of the classroom. We discuss the specific strategies within the planning category in the Results. With a new category in hand, we created a preliminary codebook consisting of explanation, facilitation, and planning categories, and their respective specific strategies.

We then passed the spreadsheet and preliminary codebook to another researcher who had not previously seen the excerpts. The second researcher looked through all the excerpts and assigned categories and strategies, without being able to see the suggestions of the initial three researchers. The second researcher also created their own new strategies and codes, especially when a specific strategy was not presented in the preliminary codebook. All of their new strategies and codes were created within the planning category. The second researcher agreed on assigned categories and implementation strategies for 71% of the total excerpts. A researcher from the initial strategies coding met with the second researcher and discussed all disagreements. The high number of disagreements, 29%, arose from the specific strategies within the new third category, planning. Since the second researcher created new planning strategies, by default these assigned codes would be a disagreement. The two researchers resolved the disagreements by finalizing a codebook with the now full and combined list of planning strategies and the previous explanation and facilitation strategies. Finally, they started the last round of coding, and they coded the excerpts with the final codebook. This time, they worked together in the same coding sessions. Any disagreements were immediately resolved through discussion and updating of final strategy codes. In the end, all 126 excerpts were coded and kept.

Characteristics of the primary studies

To answer our first research question (What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?), we report the results from our coding and systematic review process. We discuss characteristics of studies within our dataset below and in Table 1 .

Type of publication and research audience

Of the 29 studies, 11 studies were published in conference proceedings, while the remaining 18 studies were journal articles. Examples of journals included the European Journal of Engineering Education , Journal of College Science Teaching , and PRIMUS (Problems, Resources, and Issues in Mathematics Undergraduate Studies).

In terms of research audiences and perspectives, both US and international views were represented. Eighteen studies were from North America, two were from Australia, three were from Asia, and six were from Europe. For more details about the type of research publications, full bibliographic information for all 29 studies is included in the Appendix.

Types of courses sampled

Studies sampled different types of undergraduate STEM courses. In terms of course year, most studies sampled first-year courses (13 studies). All four course years were represented (4 second-year, 3 third-year, 2 fourth-year, 7 not reported). In regards to course discipline or major, all major STEM education disciplines were represented. Fourteen studies were conducted in engineering courses, and most major engineering subdisciplines were represented, such as electrical and computer engineering (4 studies), mechanical engineering (3 studies), general engineering courses (3 studies), chemical engineering (2 studies), and civil engineering (1 study). Thirteen studies were conducted in science courses (3 physics/astronomy, 7 biology, 3 chemistry), and 2 studies were conducted in mathematics or statistics courses.

For teaching methods, most studies sampled traditional courses that were primarily lecture-based but included some in-class activities. The most common activity was giving class time for students to do problem solving (PS) (21 studies). Students were instructed to either do problem solving in groups (16 studies) or individually (5 studies) and sometimes both in the same course. Project or problem-based learning (PBL) was the second most frequently reported activity with 8 studies, and the implementation of this teaching method ranged from end of term final projects to an entire project or problem-based course. The third most common activity was using clickers (4 studies) or having class discussions (4 studies).

Research design, methods, and outcomes

The 29 studies used quantitative (10 studies), qualitative (6 studies), or mixed methods (13 studies) research designs. Most studies contained self-made instructor surveys (IS) as their main source of evidence (20 studies). In contrast, only 2 studies used survey instruments with evidence of validity (IEV). Other forms of data collection included using institutions’ end of course evaluations (EOC) (10 studies), observations (5 studies), and interviews (4 studies).

Studies reported a variety of different measures for researching students’ affective and behavioral responses to active learning. The most common measure was students’ self-reports of learning (an affective outcome); twenty-one studies measured whether students thought they learned more or less due to the active learning intervention. Other common measures included whether students participated in the activities (16 studies, participation), whether they enjoyed the activities (15 studies, enjoyment), and if students were satisfied with the overall course experience (13 studies, course satisfaction). Most studies included more than one measure. Some studies also measured course attendance (4 studies) and students’ self-efficacy with the activities and relevant STEM disciplines (4 studies).

We found that the 23 of the 29 studies reported positive or mostly positive outcomes for their students’ affective and behavioral responses to active learning. Only 5 studies reported mixed/neutral study outcomes, and only one study reported negative student response to active learning. We discuss the implications of this lack of negative study outcomes and reports of SRAL in our dataset in the “Discussion” section.

To answer our second research question (What instructor strategies to aid implementation of active learning do the authors of these studies provide?), we provide descriptions, categories, and excerpts of specific strategies found within our systematic literature review.

Explanation strategies

Explanation strategies provide students with clarifications and reasons for using active learning (DeMonbrun et al., 2017 ). Within the explanation category, we identified two specific strategies: establish expectations and explain the purpose .

Establish expectations

Establishing expectations means setting the tone and routine for active learning at both the course and in-class activity level. Instructors can discuss expectations at the beginning of the semester, at the start of a class session, or right before the activity.

For establishing expectations at the beginning of the semester, studies provide specific ways to ensure students became familiar with active learning as early as possible. This included “introduc[ing] collaborative learning at the beginning of the academic term” (Herkert , 1997 , p. 450) and making sure that “project instructions and the data were posted fairly early in the semester, and the students were made aware that the project was an important part of their assessment” (Krishnan & Nalim, 2009 , p. 5).

McClanahan and McClanahan ( 2002 ) described the importance of explaining how the course will use active learning and purposely using the syllabus to do this:

Set the stage. Create the expectation that students will actively participate in this class. One way to accomplish that is to include a statement in your syllabus about your teaching strategies. For example: I will be using a variety of teaching strategies in this class. Some of these activities may require that you interact with me or other students in class. I hope you will find these methods interesting and engaging and that they enable you to be more successful in this course . In the syllabus, describe the specific learning activities you plan to conduct. These descriptions let the students know what to expect from you as well as what you expect from them (emphasis added, p. 93).

Early on, students see that the course is interactive, and they also see the activities required to be successful in the course.

These studies and excerpts demonstrate the importance of explaining to students how in-class activities relate to course expectations. Instructors using active learning should start the semester with clear expectations for how students should engage with activities.

Explain the purpose

Explaining the purpose includes offering students reasons why certain activities are being used and convincing them of the importance of participating.

One way that studies explained the purpose of the activities was by leveraging and showing assessment data on active learning. For example, Lenz ( 2015 ) dedicated class time to show current students comments from previous students:

I spend the first few weeks reminding them of the research and of the payoff that they will garner and being a very enthusiastic supporter of the [active learning teaching] method. I show them comments I have received from previous classes and I spend a lot of time selling the method (p. 294).

Providing current students comments from previous semesters may help students see the value of active learning. Lake ( 2001 ) also used data from prior course offerings to show students “the positive academic performance results seen in the previous use of active learning” on the first day of class (p. 899).

However, sharing the effectiveness of the activities does not have to be constrained to the beginning of the course. Autin et al. ( 2013 ) used mid-semester test data and comparisons to sell the continued use of active learning to their students. They said to students:

Based on your reflections, I can see that many of you are not comfortable with the format of this class. Many of you said that you would learn better from a traditional lecture. However, this class, as a whole, performed better on the test than my other [lecture] section did. Something seems to be working here (p. 946).

Showing students’ comparisons between active learning and traditional lecture classes is a powerful way to explain how active learning is a benefit to students.

Explaining the purpose of the activities by sharing course data with students appears to be a useful strategy, as it tells students why active learning is being used and convinces students that active learning is making a difference.

Facilitation strategies

Facilitation strategies ensure the continued engagement in the class activities once they have begun, and many of the specific strategies within this category involve working directly with students. We identified two strategies within the facilitation category: approach students and encourage students .

Approach students

Approaching students means engaging with students during the activity. This includes physical proximity and monitoring students, walking around the classroom, and providing students with additional feedback, clarifications, or questions about the activity.

Several studies described how instructors circulated around the classroom to check on the progress of students during an activity. Lenz ( 2015 ) stated this plainly in her study, “While the students work on these problems I walk around the room, listening to their discussions” (p. 284). Armbruster et al. ( 2009 ) described this strategy and noted positive student engagement, “During each group-work exercise the instructor would move throughout the classroom to monitor group progress, and it was rare to find a group that was not seriously engaged in the exercise” (p. 209). Haseeb ( 2011 ) combined moving around the room and approaching students with questions, and they stated, “The instructor moves around from one discussion group to another and listens to their discussions, ask[ing] provoking questions” (p. 276). Certain group-based activities worked better with this strategy, as McClanahan and McClanahan ( 2002 ) explained:

Breaking the class into smaller working groups frees the professor to walk around and interact with students more personally. He or she can respond to student questions, ask additional questions, or chat informally with students about the class (p. 94).

Approaching students not only helps facilitate the activity, but it provides a chance for the instructor to work with students more closely and receive feedback. Instructors walking around the classroom ensure that both the students and instructor continue to engage and participate with the activity.

Encourage students

Encouraging students includes creating a supportive classroom environment, motivating students to do the activity, building respect and rapport with students, demonstrating care, and having a positive demeanor toward students’ success.

Ramsier et al. ( 2003 ) provided a detailed explanation of the importance of building a supportive classroom environment:

Most of this success lies in the process of negotiation and the building of mutual respect within the class, and requires motivation, energy and enthusiasm on behalf of the instructor… Negotiation is the key to making all of this work, and building a sense of community and shared ownership. Learning students’ names is a challenge but a necessary part of our approach. Listening to student needs and wants with regard to test and homework due dates…projects and activities, etc. goes a long way to build the type of relationships within the class that we need in order to maintain and encourage performance (pp. 16–18).

Here, the authors described a few specific strategies for supporting a positive demeanor, such as learning students’ names and listening to student needs and wants, which helped maintain student performance in an active learning classroom.

Other ways to build a supportive classroom environment were for instructors to appear more approachable. For example, Bullard and Felder ( 2007 ) worked to “give the students a sense of their instructors as somewhat normal and approachable human beings and to help them start to develop a sense of community” (p. 5). As instructors and students become more comfortable working with each other, instructors can work toward easing “frustration and strong emotion among students and step by step develop the students’ acceptance [of active learning]” (Harun, Yusof, Jamaludin, & Hassan, 2012 , p. 234). In all, encouraging students and creating a supportive environment appear to be useful strategies to aid implementation of active learning.

Planning strategies

The planning category encompasses strategies that occur outside of class time, distinguishing it from the explanation and facilitation categories. Four strategies fall into this category: design appropriate activities , create group policies , align the course , and review student feedback .

Design appropriate activities

Many studies took into consideration the design of appropriate or suitable activities for their courses. This meant making sure the activity was suitable in terms of time, difficulty, and constraints of the course. Activities were designed to strike a balance between being too difficult and too simple, to be engaging, and to provide opportunities for students to participate.

Li et al. ( 2009 ) explained the importance of outside-of-class planning and considering appropriate projects: “The selection of the projects takes place in pre-course planning. The subjects for projects should be significant and manageable” (p. 491). Haseeb ( 2011 ) further emphasized a balance in design by discussing problems (within problem-based learning) between two parameters, “the problem is deliberately designed to be open-ended and vague in terms of technical details” (p. 275). Armbruster et al. ( 2009 ) expanded on the idea of balanced activities by connecting it to group-work and positive outcomes, and they stated, “The group exercises that elicited the most animated student participation were those that were sufficiently challenging that very few students could solve the problem individually, but at least 50% or more of the groups could solve the problem by working as a team” (p. 209).

Instructors should consider the design of activities outside of class time. Activities should be appropriately challenging but achievable for students, so that students remain engaged and participate with the activity during class time.

Create group policies

Creating group policies means considering rules when using group activities. This strategy is unique in that it directly addresses a specific subset of activities, group work. These policies included setting team sizes and assigning specific roles to group members.

Studies outlined a few specific approaches for assigning groups. For example, Ramsier et al. ( 2003 ) recommended frequently changing and randomizing groups: “When students enter the room on these days they sit in randomized groups of 3 to 4 students. Randomization helps to build a learning community atmosphere and eliminates cliques” (p. 4). Another strategy in combination with frequent changing of groups was to not allow students to select their own groups. Lehtovuori et al. ( 2013 ) used this to avoid problems of freeriding and group dysfunction:

For example, group division is an issue to be aware of...An easy and safe solution is to draw lots to assign the groups and to change them often. This way nobody needs to suffer from a dysfunctional group for too long. Popular practice that students self-organize into groups is not the best solution from the point of view of learning and teaching. Sometimes friendly relationships can complicate fair division of responsibility and work load in the group (p. 9).

Here, Lehtovuori et al. ( 2013 ) considered different types of group policies and concluded that frequently changing groups worked best for students. Kovac ( 1999 ) also described changing groups but assigned specific roles to individuals:

Students were divided into groups of four and assigned specific roles: manager, spokesperson, recorder, and strategy analyst. The roles were rotated from week to week. To alleviate complaints from students that they were "stuck in a bad group for the entire semester," the groups were changed after each of the two in-class exams (p. 121).

The use of four specific group roles is a potential group policy, and Kovac ( 1999 ) continued the trend of changing group members often.

Overall, these studies describe the importance of thinking about ways to implement group-based activities before enacting them during class, and they suggest that groups should be reconstituted frequently. Instructors using group activities should consider whether to use specific group member policies before implementing the activity in the classroom.

Align the course

Aligning the course emphasizes the importance of purposely connecting multiple parts of the course together. This strategy involves planning to ensure students are graded on their participation with the activities as well as considering the timing of the activities with respect to other aspects of the course.

Li et al. ( 2009 ) described aligning classroom tasks by discussing the importance of timing, and they wrote, “The coordination between the class lectures and the project phases is very important. If the project is assigned near the directly related lectures, students can instantiate class concepts almost immediately in the project and can apply the project experience in class” (p. 491).

Krishnan and Nalim ( 2009 ) aligned class activities with grades to motivate students and encourage participation: “The project was a component of the course counting for typically 10-15% of the total points for the course grade. Since the students were told about the project and that it carried a significant portion of their grade, they took the project seriously” (p. 4). McClanahan and McClanahan ( 2002 ) expanded on the idea of using grades to emphasize the importance of active learning to students:

Develop a grading policy that supports active learning. Active learning experiences that are important enough to do are important enough to be included as part of a student's grade…The class syllabus should describe your grading policy for active learning experiences and how those grades factor into the student's final grade. Clarify with the students that these points are not extra credit. These activities, just like exams, will be counted when grades are determined (p. 93).

Here, they suggest a clear grading policy that includes how activities will be assessed as part of students’ final grades.

de Justo and Delgado ( 2014 ) connected grading and assessment to learning and further suggested that reliance on exams may negatively impact student engagement:

Particular attention should be given to alignment between the course learning outcomes and assessment tasks. The tendency among faculty members to rely primarily on written examinations for assessment purposes should be overcome, because it may negatively affect students’ engagement in the course activities (p. 8).

Instructors should consider their overall assessment strategies, as overreliance on written exams could mean that students engage less with the activities.

When planning to use active learning, instructors should consider how activities are aligned with course content and students’ grades. Instructors should decide before active learning implementation whether class participation and engagement will be reflected in student grades and in the course syllabus.

Review student feedback

Reviewing student feedback includes both soliciting feedback about the activity and using that feedback to improve the course. This strategy can be an iterative process that occurs over several course offerings.

Many studies utilized student feedback to continuously revise and improve the course. For example, Metzger ( 2015 ) commented that “gathering and reviewing feedback from students can inform revisions of course design, implementation, and assessment strategies” (p. 8). Rockland et al. ( 2013 ) further described changing and improving the course in response to student feedback, “As a result of these discussions, the author made three changes to the course. This is the process of continuous improvement within a course” (p. 6).

Herkert ( 1997 ) also demonstrated the use of student feedback for improving the course over time: “Indeed, the [collaborative] learning techniques described herein have only gradually evolved over the past decade through a process of trial and error, supported by discussion with colleagues in various academic fields and helpful feedback from my students” (p. 459).

In addition to incorporating student feedback, McClanahan and McClanahan ( 2002 ) commented on how student feedback builds a stronger partnership with students, “Using student feedback to make improvements in the learning experience reinforces the notion that your class is a partnership and that you value your students’ ideas as a means to strengthen that partnership and create more successful learning” (p. 94). Making students aware that the instructor is soliciting and using feedback can help encourage and build rapport with students.

Instructors should review student feedback for continual and iterative course improvement. Much of the student feedback review occurs outside of class time, and it appears useful for instructors to solicit student feedback to guide changes to the course and build student rapport.

Summary of strategies

We list the appearance of strategies within studies in Table 1 in short-hand form. No study included all eight strategies. Studies that included the most strategies were Bullard and Felder’s ( 2007 ) (7 strategies), Armbruster et al.’s ( 2009 ) (5 strategies), and Lenz’s ( 2015 ) (5 strategies). However, these three studies were exemplars, as most studies included only one or two strategies.

Table 2 presents a summary list of specific strategies, their categories, and descriptions. We also note the number of unique studies ( N ) and excerpts ( n ) that included the specific strategies. In total, there were eight specific strategies within three categories. Most strategies fell under the planning category ( N = 26), with align the course being the most reported strategy ( N = 14). Approaching students ( N = 13) and reviewing student feedback ( N = 11) were the second and third most common strategies, respectively. Overall, we present eight strategies to aid implementation of active learning.

Characteristics of the active learning studies

To address our first research question (What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?), we discuss the different ways studies reported research on active learning.

Limitations and gaps within the final sample

First, we must discuss the gaps within our final sample of 29 studies. We excluded numerous active learning studies ( N = 383) that did not discuss or reflect upon the efficacy of their strategies to aid implementation of active learning. We also began this systematic literature review in 2015 and did not finish our coding and analysis of 2364 abstracts and 746 full-texts until 2018. We acknowledge that there have been multiple studies published on active learning since 2015. Acknowledging these limitations, we discuss our results and analysis in the context of the 29 studies in our dataset, which were published from 1990 to 2015.

Our final sample included only 2 studies that sampled mathematics and statistics courses. In addition, there was also a lack of studies outside of first-year courses. Much of the active learning research literature introduces interventions in first-year (cornerstone) or fourth-year (capstone) courses, but we found within our dataset a tendency to oversample first-year courses. However, all four course-years were represented, as well as all major STEM disciplines, with the most common STEM disciplines being engineering (14 studies) and biology (7 studies).

Thirteen studies implemented course-based active learning interventions, such as project-based learning (8 studies), inquiry-based learning (3 studies), or a flipped classroom (2 studies). Only one study, Lenz ( 2015 ), used a previously published active learning intervention, which was Process-Oriented Guided Inquiry Learning (POGIL). Other examples of published active learning programs include the Student-Centered Active Learning Environment for Upside-down Pedagogies (SCALE-UP, Gaffney et al., 2010 ) and Chemistry, Life, the Universe, and Everything (CLUE, Cooper & Klymkowsky, 2013 ), but these were not included in our sample of 29 studies.

In contrast, most of the active learning interventions involved adding in-class problem solving (either with individual students or groups of students) to a traditional lecture course (21 studies). For some instructors attempting to adopt active learning, using this smaller active learning intervention (in-class problem solving) may be a good starting point.

Despite the variety of quantitative, qualitative, and mixed method research designs, most studies used either self-made instructor surveys (20 studies) or their institution’s course evaluations (10 studies). The variation between so many different versions of instructor surveys and course evaluations made it difficult to compare data or attempt a quantitative meta-analysis. Further, only 2 studies used instruments with evidence of validity. However, that trend may change as there are more examples of instruments with evidence of validity, such as the Student Response to Instructional Practices (StRIP, DeMonbrun et al., 2017 ), the Biology Interest Questionnaire (BIQ, Knekta, Rowland, Corwin, & Eddy, 2020 ), and the Pedagogical Expectancy Violation Assessment (PEVA, Gaffney et al., 2010 ).

We were also concerned about the use of institutional course evaluations (10 studies) as evidence of students’ satisfaction and affective responses to active learning. Course evaluations capture more than just students’ responses to active learning, as the scores are biased toward the instructors’ gender (Mitchell & Martin, 2018 ) and race (Daniel, 2019 ), and they are strongly correlated with students’ expected grade in the class (Nguyen et al., 2017 ). Despite these limitations, we kept course evaluations in our keyword search and inclusion criteria, because they relate to instructors concerns about student resistance to active learning, and these scores continue to be used for important instructor reappointment, tenure, and promotion decisions (DeMonbrun et al., 2017 ).

In addition to students’ satisfaction, there were other measures related to students’ affective and behavioral responses to active learning. The most common measure was students’ self-reports of whether they thought they learned more or less (21 studies). Other important affective outcomes included enjoyment (13 studies) and self-efficacy (4 students). The most common behavioral measure was students’ participation (16 studies). However, missing from this sample were other affective outcomes, such as students’ identities, beliefs, emotions, values, and buy-in.

Positive outcomes for using active learning

Twenty-three of the 29 studies reported positive or mostly positive outcomes for their active learning intervention. At the start of this paper, we acknowledged that much of the existing research suggested the widespread positive benefits of using active learning in undergraduate STEM courses. However, much of these positive benefits related to active learning were centered on students’ cognitive learning outcomes (e.g., Theobald et al., 2020 ) and not students’ affective and behavioral responses to active learning. Here, we show positive affective and behavioral outcomes in terms of students’ self-reports of learning, enjoyment, self-efficacy, attendance, participation, and course satisfaction.

Due to the lack of mixed/neutral or negative affective outcomes, it is important to acknowledge potential publication bias within our dataset. Authors may be hesitant to report negative outcomes to active learning interventions. It could also be the case that negative or non-significant outcomes are not easily published in undergraduate STEM education venues. These factors could help explain the lack of mixed/neutral or negative study outcomes in our dataset.

Strategies to aid implementation of active learning

We aimed to answer the question: what instructor strategies to aid implementation of active learning do the authors of these studies provide? We addressed this question by providing instructors and readers a summary of actionable strategies they can take back to their own classrooms. Here, we discuss the range of strategies found within our systematic literature review.

Supporting instructors with actionable strategies

We identified eight specific strategies across three major categories: explanation, facilitation, and planning. Each strategy appeared in at least seven studies (Table 2 ), and each strategy was written to be actionable and practical.

Strategies in the explanation category emphasized the importance of establishing expectations and explaining the purpose of active learning to students. The facilitation category focused on approaching and encouraging students once activities were underway. Strategies in the planning category highlight the importance of working outside of class time to thoughtfully design appropriate activities , create policies for group work , align various components of the course , and review student feedback to iteratively improve the course.

However, as we note in the “Introduction” section, these strategies are not entirely new, and the strategies will not be surprising to experienced researchers and educators. Even still, there has yet to be a systematic review that compiles these instructor strategies in relation to students’ affective and behavioral responses to active learning. For example, the “explain the purpose” strategy is similar to the productive framing (e.g., Hutchison & Hammer, 2010 ) of the activity for students. “Design appropriate activities” and “align various components of the course” relate to Vygotsky’s ( 1978 ) theories of scaffolding for students (Shekhar et al., 2020 ). “Review student feedback” and “approaching students” relate to ideas on formative assessment (e.g., Pellegrino, DiBello, & Brophy, 2014 ) or revising the course materials in relation to students’ ongoing needs.

We also acknowledge that we do not have an exhaustive list of specific strategies to aid implementation of active learning. More work needs to be done measuring and observing these strategies in-action and testing the use of these strategies against certain outcomes. Some of this work of measuring instructor strategies has already begun (e.g., DeMonbrun et al., 2017 ; Finelli et al., 2018 ; Tharayil et al., 2018 ), but further testing and analysis would benefit the active learning community. We hope that our framework of explanation, facilitation, and planning strategies provide a guide for instructors adopting active learning. Since these strategies are compiled from the undergraduate STEM education literature and research on affective and behavioral responses to active learning, instructors have compelling reason to use these strategies to aid implementation of active learning.

One way to consider using these strategies is to consider the various aspects of instruction and their sequence. That is, planning strategies would be most applicable during the phase of work that occurs prior to classroom instruction, the explanation strategies would be more useful when introducing students to active learning activities, while facilitation strategies would be best enacted while students are already working and engaged in the assigned activities. Of course, these strategies may also be used in conjunction with each other and are not strictly limited to these phases. For example, one plausible approach could be using the planning strategies of design and alignment as areas of emphasis during explanation . Overall, we hope that this framework of strategies supports instructors’ adoption and sustained use of active learning.

Creation of the planning category

At the start of this paper, we presented a conceptual framework for strategies consisting of only explanation and facilitation categories (DeMonbrun et al., 2017 ). One of the major contributions of this paper is the addition of a third category, which we call the planning category, to the existing conceptual framework. The planning strategies were common throughout the systematic literature review, and many studies emphasized the need to consider how much time and effort is needed when adding active learning to the course. Although students may not see this preparation, and we did not see this type of strategy initially, explicitly adding the planning category acknowledges the work instructors do outside of the classroom.

The planning strategies also highlight the need for instructors to not only think about implementing active learning before they enter the class, but to revise their implementation after the class is over. Instructors should refine their use of active learning through feedback, reflection, and practice over multiple course offerings. We hope this persistence can lead to long-term adoption of active learning.

Despite our review ending in 2015, most of STEM instruction remains didactic (Laursen, 2019 ; Stains et al., 2018 ), and there has not been a long-term sustained adoption of active learning. In a push to increase the adoption of active learning within undergraduate STEM courses, we hope this study provided support and actionable strategies for instructors who are considering active learning but are concerned about student resistance to active learning.

We identified eight specific strategies to aid implementation of active learning based on three categories. The three categories of strategies were explanation, facilitation, and planning. In this review, we created the third category, planning, and we suggested that this category should be considered first when implementing active learning in the course. Instructors should then focus on explaining and facilitating their activity in the classroom. The eight specific strategies provided here can be incorporated into faculty professional development programs and readily adopted by instructors wanting to implement active learning in their STEM courses.

There remains important future work in active learning research, and we noted these gaps within our review. It would be useful to specifically review and measure instructor strategies in-action and compare its use against other affective outcomes, such as identity, interest, and emotions.

There has yet to be a study that compiles and synthesizes strategies reported from multiple active learning studies, and we hope that this paper filled this important gap. The strategies identified in this review can help instructors persist beyond awkward initial implementations, avoid some problems altogether, and most importantly address student resistance to active learning. Further, the planning strategies emphasize that the use of active learning can be improved over time, which may help instructors have more realistic expectations for the first or second time they implement a new activity. There are many benefits to introducing active learning in the classroom, and we hope that these benefits are shared among more STEM instructors and students.

Availability of data and materials

Journal articles and conference proceedings which make up this review can be found through reverse citation lookup. See the Appendix for the references of all primary studies within this systematic review. We used the following databases to find studies within the review: Web of Science, Academic Search Complete, Compendex, Inspec, Education Source, and Education Resource Information Center. More details and keyword search strings are provided in the “Methods” section.

Abbreviations

Science, technology, engineering, and mathematics

Student resistance to active learning

Instrument with evidence of validity

Instructor surveys

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Problem solving

Problem or project-based learning

End of course evaluations

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Acknowledgements

We thank our collaborators, Charles Henderson and Michael Prince, for their early contributions to this project, including screening hundreds of abstracts and full papers. Thank you to Adam Papendieck and Katherine Doerr for their feedback on early versions of this manuscript. Finally, thank you to the anonymous reviewers at the International Journal of STEM Education for your constructive feedback.

This work was supported by the National Science Foundation through grant #1744407. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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All authors contributed to the design and execution of this paper. KN, MB, and CW created the original vision for the paper. RR solicited, downloaded, and catalogued all studies for review. All authors contributed in reviewing and screening hundreds of studies. KN then led the initial analysis and creation of strategy codes. CF reviewed and finalized the analysis. All authors drafted, reviewed, and finalized sections of the paper. KN, MB, MD, and CC led the final review of the paper. All authors read and approved the final manuscript.

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Nguyen, K.A., Borrego, M., Finelli, C.J. et al. Instructor strategies to aid implementation of active learning: a systematic literature review. IJ STEM Ed 8 , 9 (2021). https://doi.org/10.1186/s40594-021-00270-7

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Accepted : 18 January 2021

Published : 15 March 2021

DOI : https://doi.org/10.1186/s40594-021-00270-7

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• Systematic review
• Instructor strategies; student response

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Active Learning for Your Online Classroom: Five Strategies Using Zoom

Moving your class sessions to a virtual space, such as Zoom video conferencing, brings new opportunities for active learning and student engagement. This resource provides simple strategies that combine active learning principles with online tools so students can encounter and engage with information and ideas, and reflect on their learning. These strategies apply to both small and large class sizes, subject to the participant limit of your video conferencing program and license.

For ways to maintain privacy and security in your online class sessions, please refer to CTL’s Zoom Security and Privacy Resource .

On this page:

• What is Active Learning?

Columbia Supported Online Tools for Active Learning

Active learning strategies, additional resources.

Cite this resource: Columbia Center for Teaching and Learning (2020). Active Learning for your Online Classroom: Five Strategies Using Zoom. Columbia University. Retrieved [today’s date] from https://ctl.columbia.edu/resources-and-technology/teaching-with-technology/teaching-online/active-learning/

Zoom: Annotation and Whiteboard Tools For more details on how to use these tools, please see: Using annotation tools on a shared screen or whiteboard and Sharing a whiteboard.

What is Active Learning? 

Bonwell and Eison describe active learning strategies as “instructional activities involving students in doing things and thinking about what they are doing 1 .” In Creating Significant Learning Experiences , L. Dee Fink builds upon Bonwell and Eison’s definition by describing a holistic view of active learning that includes all of the following components: Information and Ideas, Experience, and Reflective Dialogue 2 .  This framework can be a helpful tool to consider how your students…

• e.g., by watching videos or reading PDFs in advance, or from a short presentation you give using Zoom’s Share Screen feature
• e.g., through discussions with their peers using Zoom’s Breakout Rooms feature and documenting their conversations in collaborative Google Docs
• e.g., by spending the last five minutes of the online class session engaging in reflective writing and sharing their thoughts through an open-ended poll on Poll Everywhere .

…to meet the student learning objective(s) for your course.

The CTL is here to help!

If you have questions or would like support in developing and implementing active learning in your online course, please reach out to the CTL at [email protected] . You can also get one-on-one support via phone or Zoom during our virtual office hours .

In this resource, we will reference the following online tools supported by Columbia University: 

• Share Screen —share your screen, your student’s screen, or a virtual whiteboard
• Breakout Rooms —divide the main virtual room into smaller virtual rooms
• Polling —launch multiple choice polls
• Nonverbal Feedback —allow students to express opinions by clicking on icons
• Poll Everywhere —audience response system for polling
• LionMail (Google) Docs , Sheets , Slides —collaborative documents

*Note: If you do not see any of the above Zoom features in your Zoom meeting space, you may need to enable them first.

If you have questions about teaching with any of the above tools, please reach out to the CTL at [email protected] . You can also get one-on-one support via phone or Zoom during our virtual office hours .

The active learning strategies you select should serve the course learning objectives for your students. Remember, the goal of active learning is not simply for your students to do things, but to also think about what they are doing. As you learn more about the following strategies, consider how effective each would be in promoting the learning you desire from your students.

Here are some questions to think about when selecting an active learning strategy:

• What skill should my students be able to perform by the end of our online class session?
• Which active learning strategy will allow my students to practice this skill?
• When will my students encounter and engage with information and ideas? When will they reflect on what they’ve learned? (Any of these active learning components can be done before, during, or after the online class session.)

Strategy 1: Polling

Polling is a quick, easy way to check the opinions or thought processes of your students by posing a statement or question and gathering their responses in real time. Zoom’s Polling feature allows for simple multiple-choice polls, including Likert-type questions that ask your students to state their level of agreement with a statement, assessing the level of student interest on a list of topics, or binary yes/no or true/false questions. Simple polls can be used at the start, end, or at select points during an online class session to engage and assess your students.

• Zoom’s Polling feature

Amount of pre-class preparation required

• Instructor: Low (<15 min)
• Student: Low (<15 min)

How to Implement

Determine your purpose for conducting a simple multiple-choice poll in your online class session by considering the following:

• What information would you like to get from your students in real-time?
• How will you use the poll results / information collected?

Here are some possible ways you can use polls for active learning in your online class session:

• e.g., Which of the following career paths is your top choice at this moment?
• e.g., Which of the following best represents your familiarity with the concept of atomic orbitals?
• e.g., “Genetically modified foods should not be permitted for human consumption.” Agree or Disagree?
• e.g., Which of the following factors do you think has the largest impact on the rate of DNA replication in a eukaryotic cell?
• e.g., Which of the following topics would you like to go over as a class?
• e.g., Which of the following activities are most helpful in helping you learn the skills required for this course?

Create the Zoom poll (see Zoom Help Center to learn how) and determine how much time your students will need to respond to it. Make sure the question title and prompt is clearly worded and not open to misinterpretation.

Prior to launching the poll, provide verbal and written instructions on how to complete the poll. Once launched, you will be able to see in real time the number of students and the percentage of the class that have responded to the poll, the time elapsed, and the results of the poll.

End the poll when the allocated time is up. You can then choose whether to show the class the results of the poll. Either way, be sure to directly address or have your students respond to the results of the poll, and relate it back to the purpose of the poll.

Alternative Tools for Polling

• CourseWorks (Canvas) Quiz has an ungraded survey feature that can be used for polls both synchronously and asynchronously.
• PollEverywhere can be used for more advanced polling activities such as using open-ended text questions or images. Unlike Zoom, the results from PollEverywhere can be directly transported to CourseWorks (Canvas).

Strategy 2: Think-Pair-Share

This active learning strategy involves posing a short problem, scenario, or question to your students and giving them the time and opportunity to complete the following steps:

• Think through the problem, scenario, or question individually.
• Pair with a partner to discuss.
• Share their findings or takeaways with the rest of the class.

This strategy not only gives your students time to process and apply their knowledge and skills on their own first, it also gives them the opportunity to consult and collaborate with a peer. This process usually elicits more thoughtful responses while also lowering the stakes of sharing with the rest of the class.

• Zoom’s Share Screen feature
• Zoom’s Breakout Rooms feature
• Think : First, pose a short problem, scenario, or question for your students to work through on their own for about 30 seconds to a minute. Read the question out loud while also displaying it on a slide that you share with your students using Zoom’s Share Screen feature. As your students are thinking through the problem, click on Zoom’s Breakout Rooms tool so you can enter the number of breakout rooms needed in order for each to contain a pair of students. Zoom conveniently displays the number of participants per room based on the number of participants present and the number of rooms you select. If you have an odd number of students, subtract one from the total number of students and divide that by two to get the number of rooms you should create; Zoom will automatically assign one of the breakout rooms with three students instead of a pair.
• Pair : When your students are ready to pair up, let Zoom automatically assign them to the breakout rooms. Give your students about 5 minutes to introduce themselves to their partners and share their thoughts on the assigned problem. To help your students keep track of the given problem and directions, you can broadcast the problem and instructions through a message to all the breakout rooms.
• Share : When your students are ready to share, close the breakout rooms so all your students return to the main room. Ask for volunteers to share their answers or discussion takeaways by having them use the hand-raise feature in Zoom. Unmute one volunteer at a time so they can acknowledge their partner and share their response with the entire class. Mute the volunteer who has spoken before unmuting the next one. Repeat this process until you are satisfied with the number of contributions and/or perspectives shared.

Alternative active learning strategies with similar setups

• Note-Taking Pairs 3 : Students work in pairs to improve their individual class notes.
• Three-Step Interview 3 : Students work in pairs and take turns interviewing each other, and report what they learn to another pair.
• Peer Instruction 4 : Students first answer a given poll question on their own. Then, students pair up and explain their rationale. Finally, students answer the poll question again.

Strategy 3: Minute Paper

A minute paper is a short “paper” that students individually complete in a minute (or more realistically, under five minutes) in response to a given prompt. Minute papers provide students with opportunities to reflect on course content and disciplinary skills as well as their self-awareness as learners (see the CTL’s resource on metacognition to learn more). This active learning strategy simultaneously allows you to quickly check your students’ knowledge. Minute papers can be assigned at the start, during, or at the end of your online class session as you see fit.

• Poll Everywhere

Before your online class session, write an open-ended prompt that students can respond to in less than five minutes. You can vary the prompt to target specific knowledge and skill sets or solicit big picture free responses.

Example prompts include:

• What questions about today’s topic are you most interested in exploring?
• What was the most important point of today’s lesson?
• Share an experience from your everyday life that illustrates this principle.
• What steps will you take to maximize your learning for the upcoming test?
• Reflecting on the essay you just submitted, what would you have done differently that would improve your essay?

When your prompt is ready, use it to create an open-ended poll in Poll Everywhere (external to Zoom). Using Poll Everywhere to collect minute paper responses allows you to either display the responses as they come in or download a CSV spreadsheet containing all the responses to skim for trends and themes later.

While student responses are never displayed with student identities during the poll, you may need that information for the purpose of assigning participation grades or to respond to students individually. For this information to be recorded in the CSV spreadsheet, you will need to restrict the poll to registered participants only. Your students will then need to log in to their Columbia Poll Everywhere accounts to participate in the poll.

During your online class session, when you are ready for students to complete their minute papers, activate your open-ended poll and use Zoom’s Share Screen tool to share the Poll Everywhere window with your students. While the instructions for responding to the poll will be shown via shared screen, you should also read the instructions out loud to ensure all students receive that information. 

Give your students about five minutes to go to the displayed Poll Everywhere site and type in their responses to the minute paper prompt. Depending on your goal, you have the option of addressing select responses as they come in or compiling the results after class so you can address them at the start of the next one.

• What’s the Problem 5 : Students categorize example problems according to the principles and strategies needed to solve them.
• Muddiest Point 6 : Students share their responses to the prompt “What was the muddiest (most confusing) point in _____ ?”

Strategy 4: Small Group Discussions

Small group discussions are one way for your students to delve more deeply into a given problem or issue. You can pose an open-ended question or problem, or provide your students with a scenario or case study to work through. The duration is dependent on the task. Groups can then present their results or findings to the rest of the class.

• Zoom’s Nonverbal Feedback feature (including hand raise)
• Google Docs , Sheets , Slides (collaborative documents)
• Instructor: Moderate (15–60 minutes)

Reflect on the learning objective that would most benefit from small group discussion. From this learning objective, develop the discussion prompt that you will assign to your students. For example:

• Learning Objective: Analyze Figure 3 of the assigned research article.
• Discussion Prompt: How well does the data shown in the figure support the author’s claims?

When assigning the small group discussion, be sure to include clear instructions on what your students are supposed to do. Examples include:

• How many students will be in each group
• How much time they have for the discussion
• What they need to report back to the class and how much time they have to do so
• Upholding discussion guidelines that they previously agreed to

Because your students are having these discussions completely online, it is best not to have too many students in each group; 3-4 students per group for a 10-minute small group discussion allows each student to contribute substantially to the discussion.

To help facilitate the small group discussion and ensure that all students engage, either assign or have your students volunteer for the following roles:

• Facilitator + Timekeeper—keep the discussion focused on the assigned prompt
• Notetaker—record the main points of the discussion on a collaborative document like Google Docs or Slides
• Challenger—push the group to view the problem or issue from different perspectives
• Reporter—report the main takeaways of the discussion back to the rest of the class

You could have students rotate roles across the semester so that they get to experience and learn the different skill sets associated with each role. 

Let your students know that you, and if applicable, your co-instructor(s) and/or TA(s), may be dropping into each breakout room periodically to check their progress and answer any questions, but that they do not have to stop their discussion if they do not need anything from you.

After providing your students with both verbal and written instructions, give them a minute to ask you any clarifying questions before you send them to their breakout rooms.

When the class is ready, use Zoom to automatically divide your students into breakout rooms. You can set the breakout rooms to close automatically after a set duration. This adds a countdown timer in the breakout rooms informing your students of the remaining time they have. As students are discussing in their breakout rooms, stop by several breakout rooms to see how the discussion is going and answer any questions, if any. You may also broadcast a message to all breakout rooms to solicit questions. Your students can always request for help from their breakout rooms by clicking the Ask for Help button, which alerts you to their request and prompts you to join their breakout room.

When time is up, if you did not set the breakout rooms to automatically close, manually close them so all students return to the main room. Ask all the student reporters to identify themselves using the hand-raise button (part of Zoom’s Nonverbal Feedback feature). When a student reporter is ready to share with the class, unmute that particular student and have them share their screen with the class. Other students can ask questions via the chat window. When the student reporter is done presenting, you can unmute the rest of that group to allow them to solicit and answer questions from their peers.

• Test-Taking Teams 3 : Students work in small groups to prepare for a test. Students then take the test individually and submit their responses. Immediately after, students retake the test in their small groups, working to find consensus on their responses.
• Jigsaw 3 : Students work in small groups. Each group becomes an expert in a different topic. New groups are formed, comprising at least one expert on each topic. In these new groups, each student teaches their peers the topic they became an expert on.

Strategy 5: Short Student Presentations

Short presentations provide an opportunity for students to engage in peer instruction. This type of activity invites students to synthesize and communicate their knowledge. Students can be asked to research an issue of interest to them that is related to the course topic or work on a problem outside of class, and to present their findings during an upcoming online class session. This allows students to link course content with their own interests and lived experiences, and learn from their peers.

• Google Slides
• Student: Significant (>60 minutes)

Identify a course learning objective that would greatly benefit from having students explore the topic further on their own. For example, you could have students use their analytical skills that they developed during the course to analyze a different area, setting, artifact, or scenario of their choice. Alternatively, you could have your students design proposals to address a problem raised in class.

Assign student presentations with sufficient time for your students to prepare their presentation, e.g., at least one to two weeks in advance. Be sure to provide specific instructions regarding the format and duration of the presentation, e.g., “The presentation is 5 minutes long with 10 minutes for audience questions,” as well as any criteria for evaluation, which could be represented as a rubric.

This strategy works best if you provide students with preliminary feedback on their presentations prior to your online class session. Consider having a short online meeting with each student presenter or checking in via email to provide feedback on their presentation and to answer their questions at least a few days before your online class session.

When it is time for your students to present during your online class session, first remind the class of the purpose and format of the student presentations. Encourage your students to be active listeners during the presentation, e.g., reflect on how the presentation might apply to your interests, explore how the presentation enriches your perspectives on the topic, type your questions into the Zoom chat, or write down your main takeaways from the presentation.

When the student presenter is ready, unmute their microphone and allow them to share their screen with the class.

While the student is presenting, you may monitor questions that are being submitted by other students to the Zoom chat. Once the presentation is finished, select a few questions for the presenter to address.

When the student presenter is done answering questions, consider having all your students reflect on what they learned. For example, you could ask your students to summarize their main takeaways from the presentation or describe how the presentation connects with different aspects of the course. Have your students share their reflections on a discussion board on CourseWorks (Canvas) or an open-ended poll on Poll Everywhere. You can skim through these reflections to see what your students gained from the student presentations.

• Digital scavenger hunt: Students find or create media (images, video clips, audio clips) that they think best represent assigned course concepts to share with the class.
• Book club 5 : Students choose from a list of suggested books on course content and form corresponding book clubs. Each book club presents a final report to the rest of the class, while other students identify common themes and differences between the presented books and the books they chose in their own book club.
• Student group presentations: Students work in small groups outside of class on an assigned project and present their findings during the online class session. Other students in class focus on asking questions and linking the presentation to course content.
• Working in a virtual classroom requires patience. Begin with simple low stakes activities for you and your students to get comfortable with the new format and provide time and opportunity for your students to ask you questions. Eventually, instructors, TAs, and students will gain proficiency with these online tools.
• Seek to minimize barriers that students may face in order to participate in the activities you plan for your online class session. Factors to consider include access to reliable technology and conducive spaces, student physical and mental abilities, and timing. For ways to make online learning accessible for all your students, please refer to CTL’s Accessibility Resource .
• Do a test-run of each activity you plan to use before your online class session, preferably with a CTL Learning Designer or a teaching colleague. Given the number of user-specific settings in Zoom, you will want to ensure that all the features you will be using have been enabled prior to your online class session. Some features cannot be enabled once your online class session has launched.

Community Building in Online and Hybrid (HyFlex) Courses

Collaborative Learning Online

Facilitating and Promoting Student Engagement in the Online, Synchronous Classroom (Center for the Integration of Research, Teaching, and Learning)

Online Instructional Activities Index (University of Illinois, Springfield)

Tips & Tricks: Teachers Educating on Zoom (Zoom) 

How to Be a Better Online Teacher (Flower Darby, Northern Arizona University)

• Bonwell, C.C. & Eison, J.A. (1991). Active Learning: Creating Excitement in the Classroom. ASHE-ERIC Higher Education Report 1. Washington, D.C.: George Washington University.
• Fink, D.L. (2003). Creating Significant Learning Experiences: An Integrated Approach to Designing College Courses. San Francisco: Jossey-Bass.
• Barkley, E. F., Cross, K. P., & Major, C. H. (2014). Collaborative Learning Techniques: A Handbook for College Faculty . John Wiley & Sons
• Mazur, E. (2013). Peer Instruction: A User’s Manual. Pearson Higher Ed.
• Barkley, E. F. (2009). Student Engagement Techniques: A Handbook for College Faculty . John Wiley & Sons, Incorporated. 
• Angelo, T. A., & Cross, K. P. (1993). Classroom assessment techniques: A handbook for college teachers (2nd ed). Jossey-Bass Publishers.
• First Steps for Moving a Class Online
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Active learning best for sustainability issues

"Challenge-based learning is often focused on the challenges that have global impact."

According to an international team of educators, active learning methods, such as problem-based learning, project-based learning and challenge-based learning are necessary to provide engineering students with the skills to tackle global issues. Of those, challenge-based learning is the most suitable in sustainability education. By Aldona Tuur, Communications specialist, Kaunas University of Technology, Lithuania.

“Challenge-based learning is often focused on the challenges that have global impact. The students, who accept the challenge, often don’t know what the solution will be. The facilitator is keeping them from focusing on a solution too early, and encourages [them] to analyse the challenge from multiple points of view, and from different scientific perspectives,” says Vilma Sukacke, a researcher at Kaunas University of Technology (KTU), Lithuania.

Such a learning / teaching approach is very fitting to sustainability education, which, according to some scholars, calls for a contextual, problem-oriented, reflective, interdisciplinary, collaborative, participatory, ethical and empowered learning environment. In other words, educators must shift from more traditional teacher-centred education to becoming instructional designers of student-centred education.

Aiming to evaluate the efficiency of such approaches, a group of researchers from Lithuanian, Danish, German, Portuguese and Spanish universities conducted a systematic literature review , exploring the three active learning methods: project-based, problem- based and challenge-based learning according to the ADDIE (analysis, design, development, implementation, and evaluation) framework.

Clear communication is key

Although the educators agree that problem-based learning (PBL), project-based learning (PjBL) and, more recently, challenge-based learning (CBL) are efficient in teaching students to integrate technology in real-life situations and improving their transversal skills, such as teamwork, communication and conflict resolution, the application of these methods in the classroom may be challenging for both sides.

“In my practice, I have noticed that sometimes students are questioning innovative learning methods and are considering them as a sort of a “game”. As these classes often take place in a playful environment, full of different pencils, colourful notes and building blocks, it’s difficult for the students to take them seriously. Therefore, it’s very important to communicate the methods and the goals to the learners very clearly,” says Professor Saule Petroniene from KTU Faculty of Social Sciences, Arts and Humanities, a co-author of the study.

According to her, to successfully apply unorthodox teaching methods is a challenge for a beginning teacher. However, the effort pays off, especially, when students are continuing their activities outside of the scope of the school project and focus on solving real societal issues.

Change of roles – a challenge for both teachers and students

During the study, researchers interviewed teachers implementing these new approaches in the classroom. They found implementing PBL, PjBL and CBL requires a paradigm shift, where organisations, staff and students change their view to education and learning. In this process, both teachers and students need to apply new skills and take on roles that they might not needed before. “Both students and teachers face difficulties in accepting their new roles; a teacher is no longer the main provider of knowledge, and a student is not a passive listener who absorbs the knowledge as a sponge,” observes Sukacke, who interviewed teachers at ECIU University, an EU-funded European University applying CBL in its activities.

According to Sukacke, in CBL, both teachers and learners face a strong feeling of uncertainty. At the beginning, it is not clear what resources will be needed and what solution will be implemented, contradicting the urge to find it solutions any cost, as soon as possible. Extensive multidisciplinary analysis of the challenge is at the core of CBL. Therefore, often, more consultations are needed during the intermediate stages of challenge-solving and this requires additional time and skills from the facilitating teacher.

"Students are questioning innovative learning methods and are considering them as a sort of a 'game'."

Active learning continues after class

While challenging, the educators believe the benefits of active learning methods are significant, with the greatest gain being sustainable education. Graduates become continuous learners themselves, able to continue adapting their skills and competencies to different challenges they face in real-life. “Recently, one of my former students greeted me by saying how grateful they were for solving the problem related to a real-life situation in a student dormitory,” says Professor Petroniene.

“After finding the solution in the class, the students were able to apply it in reality. I am sure, that many students have similar feelings; by learning to solve real problems, they obtain different results both in their studies, and outside the classroom. One of the main skills in these classes is learning to work in a team.”

Dr Lina Gaiziuniene, a researcher at KTU Faculty of Social Sciences, Arts and Humanities and co-author of the study, is convinced that the contemporary learning / teaching paradigm is based on the teacher’s support allowing the students to grow as independent learners. “Cooperation in this process is crucial. It allows the teacher to understand the student better – his or her goals in terms of knowledge, skills, studying preferences. A student and a teacher become partners and this motivates both parties involved to perform as well as they can. This, in turn, increases the motivation,” says Dr Gaiziuniene.

According to Sukacke, the teachers who applied CBL in their classes had an increased motivation to apply other teaching innovations; the students developed numerous soft competencies and achieved their study goals.

Challenge-based learning approach most suited for sustainability education

“At the ECIU University, we apply CBL to solve the challenges related to the 11th UN Sustainable Development Goal, Sustainable Cities and Communities. The solutions of the challenges, developed at KTU and other ECIU University members, are already applied or will be applied in the real-life context by the industries who presented the challenges. In such a way, a meaningful real change is created,” says Sukacke.

According to the authors of the study, CBL, the newest of the active learning approaches, is the most suitable for solving sustainability-related issues. The challenge-based learning experience is typically multidisciplinary, involves different stakeholders and the teams formed during the challenge-solving may co-work together on its implementation after the classroom activities had ended. Even though, CBL shares some key features with other active learning methods, it goes beyond in that the challenge is not fully predefined; the learners and the community members participate in its co-creation.

As sustainability-related challenges are always multidisciplinary, involve different stakeholders and their solutions are not obvious from the first glance, CBL may be the most suited approach to sustainability education.

The above-described study “Towards Active Evidence-Based Learning in Engineering Education: A Systematic Literature Review of PBL, PjBL, and CBL” was published in Sustainability 2022, 14(21), 13955 and can be accessed here .

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Active Learning and Student-centered Pedagogy Improve Student Attitudes and Performance in Introductory Biology

• Peter Armbruster
• Erika Johnson
• Martha Weiss

*Department of Biology, Georgetown University, Washington, DC 20057; and

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Department of Education, Cornell University, Ithaca, NY 14853

We describe the development and implementation of an instructional design that focused on bringing multiple forms of active learning and student-centered pedagogies to a one-semester, undergraduate introductory biology course for both majors and nonmajors. Our course redesign consisted of three major elements: 1) reordering the presentation of the course content in an attempt to teach specific content within the context of broad conceptual themes, 2) incorporating active and problem-based learning into every lecture, and 3) adopting strategies to create a more student-centered learning environment. Assessment of our instructional design consisted of a student survey and comparison of final exam performance across 3 years—1 year before our course redesign was implemented (2006) and during two successive years of implementation (2007 and 2008). The course restructuring led to significant improvement of self-reported student engagement and satisfaction and increased academic performance. We discuss the successes and ongoing challenges of our course restructuring and consider issues relevant to institutional change.

INTRODUCTION

The traditional lecture format of most large introductory science courses presents many challenges to both teaching and learning. Although a traditional lecture course may be effective for efficiently disseminating a large body of content to a large number of students, these one-way exchanges often promote passive and superficial learning ( Bransford et al. , 2000 ) and fail to stimulate student motivation, confidence, and enthusiasm ( Weimer, 2002 ). As a consequence, the traditional lecture model can often lead to students completing their undergraduate education without skills that are important for professional success ( National Research Council [NRC], 2007 ; also see Wright and Boggs, 2002 , p. 151). Over the past two decades, a series of influential reports and articles have called attention to the need for changes in approaches to undergraduate science education in ways that promote meaningful learning, problem solving, and critical thinking for a diversity of students ( American Association for the Advancement of Science, 1989 ; Boyer, 1998 ; NRC, 1999 , 2003 , 2007 ; Handelsman et al. , 2004 , 2007 ; Project Kaleidoscope, 2006 ). This need is particularly acute at the introductory level, where a major “leak in the pipeline” toward science careers has been noted ( Seymour and Hewett, 1998 ; Seymour, 2001 ; NRC, 2007 ).

Although the proposed improvements noted above differ in detail, a remarkably consistent theme is the call to bring student-centered instructional strategies, such as active- and inquiry-oriented learning, into the classroom. Allen and Tanner (2005) define active learning as “seeking new information, organizing it in a way that is meaningful, and having the chance to explain it to others.” This form of instruction emphasizes interactions with peers and instructors and involves a cycle of activity and feedback where students are given consistent opportunities to apply their learning in the classroom. By placing students at the center of instruction, this approach shifts the focus from teaching to learning and promotes a learning environment more amenable to the metacognitive development necessary for students to become independent and critical thinkers ( Bransford et al. , 2000 ). A substantial number of studies have shown that active-learning instructional approaches can lead to improved student attitudes (e.g., Marbach-Ad et al. , 2001 ; Prince, 2004 ; Preszler et al. , 2007 ) and increased learning outcomes ( Ebert-May et al. , 1997 ; Hake, 1998 ; Udovic et al. , 2002 ; Knight and Wood, 2005 ; Freeman et al. , 2007 ) relative to a standard lecture format.

The establishment of several national programs that promote active-learning pedagogy (The National Academies Summer Institutes 1 and FIRST II 2 ), the establishment of journals such as CBE—Life Sciences Education , and the growth of several database repositories of active-learning exercises (MERLOT pedagogy portal 3 , TIEE 4 , FIRST II, National Digital Science Library, 5 and especially BioSciEdNet 6 and SENCER Digital Libary 7 ) are all positive evidence of concerted responses to the calls for change noted above. These resources also provide significant support for faculty committed to implementing active-learning strategies in their courses both in terms of training opportunities and by making example teaching materials readily available. Nevertheless, the proposition of restructuring a large introductory course to emphasize elements of active learning can seem overwhelming for faculty with extensive time commitments in other realms and little or no formal training in pedagogy.

Here, we describe the development and implementation of an instructional design that focused on bringing multiple forms of active-learning and student-centered pedagogies into a traditionally lecture-based introductory biology course. Our course restructuring was motivated by several perceived deficiencies common to traditional lecture-based introductory courses. The most pronounced concern, shared by multiple faculty involved in the course, was poor student attitudes. Both numeric and written responses on course evaluations indicated that students were not satisfied with the course and did not recognize the importance of the course content to their education as biologists. For example, students often commented on course evaluations that the lectures and/or course materials were “boring.” Furthermore, individual instructor–student interactions often indicated that students were more concerned with their test scores than with gaining a thorough understanding of the course material. Poor student attitudes also were reflected by poor attendance, limited participation in class, and suboptimal student performance.

We hypothesized that incorporating active-learning and student-centered pedagogy into the instructional design of our course would both improve student attitudes and also lead to increased student performance ( Weimer, 2002 ). We chose to focus primarily on using problem-based learning activities because these activities tend to be more succinct and less open-ended than case-based activities, and thus it was easier to integrate problem-based activities into our previously established lecture organization. Our positive results illustrate how changing the instructional design of a course, without wholesale changes to course content, can lead to improved student attitudes and performance. The goals of this article are to 1) describe the elements of our instructional design that contributed to improved student attitudes and performance; and 2) discuss significant future challenges, so that other educators can learn from our experiences.

MATERIALS AND METHODS

Study design.

The course restructuring we describe pertains to the lecture portion of Introductory Biology II, a one-semester course that typically enrolls between 170 and 190 students (details are provided below). The course was taught in a standard lecture format in 2006 and redesigned to emphasize active learning and student-centered pedagogy in 2007 and 2008. The first author taught the course in all 3 years (2006–2008) and in the 2 years before 2006. The hypotheses we consider in this study are that student attitudes and performance increased in 2007 and 2008 in response to the instructional design we implemented.

Course Description

Introductory Biology II is the second semester of a 1-yr sequence required for biology majors and premedical students. The first semester of the sequence, Introductory Biology I, focuses on molecular and cellular biology with some treatment of development and physiology. Introductory Biology II emphasizes principles of ecology, evolution, and a survey of the diversity of life. This basic course content was not changed substantially as part of the revision we describe, although we modified the order in which the material was presented (see below). In all 3 years, the lectures consisted of three 70-min periods per week. There also was an optional weekly recitation section where the instructor was available to answer student questions. In all 3 years (2006–2008), we handed out a set of questions (“the daily dozen”) for each lecture to help guide students in their assigned textbook reading, and discussion in recitation often centered on these questions. Before our course revision, assessment for the lecture portion of the course consisted of three midterms and a final examination, with each exam consisting of a mix of quantitative problem solving, short answer, and short essay questions. As part of our course revision, we modified this assessment plan to include 10 weekly quizzes, two midterms, and a final exam. In all 3 years, all students were required to enroll in a weekly 3-h laboratory section that was assessed and evaluated separately from the lecture portion of the course. The laboratory portion of the course was not a part of this course revision.

Course Redesign

Reorder course content. We reordered the presentation of the course content in an attempt to teach specific content within the context of broad conceptual themes. For example, a new lecture on evolutionary developmental biology (“evo-devo”) was presented before the series of lectures surveying animal diversity. This lecture was designed to both serve as an intellectual bridge between the sections of the course describing evolutionary mechanisms and organismal diversity and also to help students understand patterns of animal diversity by understanding some of the mechanisms by which that diversity evolved. As another example, two lectures on photosynthesis were presented immediately following a lecture on ecosystem ecology in order to help students understand the details and importance of primary productivity within the context of nutrient cycling of ecosystems. We also ended the course with a two-lecture module on the biology of avian flu that synthesized a number of topics taught during different parts of the semester. In these lectures we emphasized the role of mutation and reassortment in viral evolution, discussed how species interactions influenced viral reassortment, and considered epidemiological models of viral transmission and spread. A copy of the course syllabus is available by request from the corresponding author.

Active learning and group problem solving. We incorporated active and problem-based learning into every lecture. Students were organized into groups of four on the first day of class, asked to sit together throughout the semester, and in almost every lecture groups were presented with a quantitative or conceptual problem. Examples of a quantitative problem concerning Hardy–Weinberg equilibrium and a strip sequence problem ( Handelsman et al. , 2007 ) concerning character displacement are presented in Table 1 , A and B, respectively. Group problems were typically displayed on a PowerPoint slide, and the groups were given 3–5 min to work on the problem. During this period, the instructor would move from group to group in the classroom to monitor student progress and offer suggestions if a group encountered difficulty. The level of student activity was clearly indicated by the noise level of student discussions in the classroom, which was monitored to determine when to bring the group work to a close. Haphazardly selected group representatives were then asked to report out to the class after each group problem-solving session. In addition to the examples in Table 1 , we used a variety of active-learning exercises as described in Handelsman et al. (2007) , including think-pair-share, 1-min papers, and concept maps.

A personal response system (a.k.a. “clickers”) also was used to promote active learning in the classroom. Each lecture included two to six “clicker questions” that were presented as multiple-choice questions on a PowerPoint slide. Generally, we developed the questions to address a specific concept covered in the lecture, but in some cases Graduate Record Exam (GRE) or Medical College Admission Test questions were presented with a label indicating the source of the question. Effective implementation of clicker questions is discussed below (see Discussion ), and two representative examples are presented in Table 2 . Students were awarded participation points (20 points of a course total of 700 points) if they answered ≥75% of all clicker questions presented over the entire semester (approximately 120 total questions each year), regardless of whether their answers were correct. Clickers were also used to administer weekly quizzes (see below).

Student-centered pedagogy. We adopted several additional strategies to create a more student-centered learning environment. Every lecture included a set of learning goals made explicit to students in the lecture PowerPoint slides ( Table 3 ). All exam and quiz questions were then labeled with the corresponding learning goals to emphasize the alignment of learning goals and assessment. We also included a set of vocabulary terms for each lecture to help the students focus on important concepts, and with the hope that students would use these technical terms to formulate more precise and succinct answers to free-response questions on exams. We also placed an increased emphasis on formative assessment by integrating assessment and self-assessment components into activities during lecture so that students would receive feedback designed to improve their performance ( Handelsman et al. , 2007 ). For example, virtually every group problem in lecture (e.g., Table 1 ) included a component of formative assessment because we always discussed the answer to each problem in class and the group work problems closely resembled the problems on exams. Finally, we administered 11 weekly quizzes worth 8 points each, with only the top 10 scores applied to the final grade. These weekly quizzes thus provided regular feedback on student performance in a “low-stakes” assessment environment and encouraged students to keep up with the material on a regular basis.

The course redesign was implemented for the first time in 2007. In 2008, the course organization closely followed that of 2007, with minor modifications based on student feedback in 2007 (see Discussion for details). The most substantial change in 2008 involved moving the weekly quizzes to Thursday, the day after the optional recitation session (in 2007 quizzes were administered on Tuesday).

A modified strip-sequence problem concerning character displacement

For both problems, students were asked to work collaboratively in preassigned groups for 5 to 10 min to solve the problem. A single successful group was then asked to describe their solution to the class. The instructor then asked for explanations from any other group that successfully solved the problem by using an alternative approach or reasoning.

In both C4 and CAM the light reactions produce ATP and NADPH to drive the Calvin cycle during the daylight hours.

In both C4 and CAM, the enzyme PEP carboxylase fixes CO 2 into a four-carbon organic acid.

In both C4 and CAM, the Calvin cycle is most active during the daylight hours.

B and C only.

All of the above.

Additional examples are discussed in Discussion .

Assessment of Student Attitudes and Performance

We assessed student attitudes toward the course in all 3 years by 1) administering a three-page questionnaire that used both Likert-scale and free response questions (see Supplemental Material I), and 2) comparing scores on university-administered course evaluations for questions that addressed student satisfaction. We assessed student performance by comparing class scores on two identical final exam questions administered in 2006, 2007, and 2008 (see Supplemental Material II). We do not hand back the approximately 20-page final exam, so it is not possible that the specifics of these questions were available to students in later years (i.e., 2007 and 2008). We chose final exam questions that addressed the fundamental topics of logistic population growth and life-history trade-offs and a more conceptual question on island biogeography (see Supplemental Material II). All of these topics were emphasized heavily in lecture in all 3 years. In addition, two educational experts scored all of the questions on the final exam in 2006, 2007, and 2008 according to Bloom's taxonomy of learning ( Bloom, 1956 ). Bloom's taxonomy identifies six hierarchical levels of understanding that range from knowledge (level 1), to comprehension (level 2), application (level 3), analysis (level 4), synthesis (level 5), and evaluation (level 6). We used a weighted Kappa statistic ( Altman, 1991 ) to quantify the interrater reliability because this statistic is appropriate for ratings that fall into discrete categories. Because the original scored exams are no longer available, direct comparison of performance on questions that differ in Bloom's ranking across years is not possible. However, our electronic grade book does permit us to compare the proportion of points at different Bloom's levels and performance on the final exam in all 3 years.

Data Analysis

We tested for differences in class composition between years based on categories in Table 4 by using a χ 2 goodness-of-fit test. We tested for differences in Likert-scale student responses concerning attitudes toward the course from both the questionnaire and university course evaluations by using one-way analysis of variance (ANOVA) followed by a posteriori comparison of means with a sequential Bonferroni correction to control for experiment-wise error (α = 0.05). We used a one-way ANOVA of Likert-scale ratings of the helpfulness of different lecture components (i.e., weekly quizzes, clickers, etc.) with lecture component as a fixed effect and students nested within lecture component as a random effect. The one-way ANOVA was followed by planned (a priori) comparisons of means of different lecture components (averaged across years) with Bonferroni correction for multiple comparisons. We tested for differences between years (2007 and 2008) for each individual lecture component with a Student's t test, again with Bonferroni correction for multiple comparisons. To test for differences in student performance on identical final exam questions among years (2006, 2007, and 2008), we also used one-way ANOVA and a posteriori comparison of means with Bonferroni correction. To test for differences in performance on the entire final exam in 2006, 2007, and 2008, we performed one-way ANOVA on square-root arcsine-transformed percentage scores in each year followed by a posteriori comparison of means with Bonferroni correction.

Students' free responses on the questionnaire (see Supplemental Material I) provided a source of qualitative data on attitudes. Each student's answers to question 5A (“What specifically did you like about the course?”) and positive comments from question 6 (“What else would you like to tell us?”) were combined to reflect the student's positive feedback. Similarly, each student's answers to question 5B (“What specifically did you dislike about the course?”) and negative comments from question 6 were combined to represent the student's negative feedback. Negative and positive feedback for each of the 3 years was coded separately. Codes were developed in vivo ( Strauss and Corbin, 1990 ). Most codes reflected specific course or lecture elements, such as clickers, quizzes, learning goals, PowerPoint slides, and guest lectures. Additional codes were developed to tag students' more general or descriptive statements such as: “too early” or “connected to the real world.” The open response nature of these questions meant that individual statements could be tagged with several codes (examples of coded text are provided in Supplemental Material III).

Categories were developed in relation to code frequencies as determined by the number of students whose statements were tagged with that code in a given year. In this way, we avoided overestimating a code's frequency when, for example, an individual mentioned a lecture element multiple times. The most frequently used codes (e.g., clickers and quizzes) were elevated to category status. Text tagged with these codes were re-examined for the explanatory details (subcodes) that are presented associated with each category in Tables 5 and 6 .

Composition of Student Body

Approximately 60% of Introductory Biology II students are in the first year of undergraduate study; 75% identified themselves as premedical students, and only 40% were declared biology majors. The student composition of the course ( Table 4 ) in 2007 and 2008 did not differ significantly from 2006 (χ 2 = 11.21, df = 7, p > 0.10).

a Based on student responses to the questionnaire in Supplemental Material I.

b Total enrollment was 165 in 2006, 179 in 2007, and 176 in 2008.

Student Attitudes

All measures of student satisfaction differed significantly between years ( Figure 1 ). These measures include change in interest in the course material from the start to the end of the semester ( F 2409 = 5.22, p < 0.001), ranking of relevance of course material to long-term student goals ( F 2407 = 6.65, p = 0.001), self-reported student learning ( F 2355 = 11.70, p < 0.001), ranking of classroom presentations as stimulating ( F 2358 = 26.52, p < 0.001), ranking of the course as challenging ( F 2355 = 15.87, p < 0.001), and overall evaluation of instructor ( F 2355 = 15.87, p < 0.001). For all measures of student satisfaction, a posteriori comparison of treatment means indicated that student satisfaction was significantly higher in 2007 and 2008 than in 2006 (sequential Bonferroni, p < 0.05) but did not differ between 2007 and 2008 ( p > 0.05; Figure 1 ). A summary of student free responses to questions probing student satisfaction and dissatisfaction in all 3 years is provided in Tables 5 and 6 , respectively.

Figure 1. Mean ± SE student-reported attitudes from 2006 (standard lecture format), 2007, and 2008 (revised lecture format). “Increased interest” and “Relevance to goals” were questions on an instructor administered questionnaire (see Supplemental Material I), all other questions were part of the university course evaluation (see text). Increased interest represents difference in interest in the subject matter after taking the course relative to interest before taking the course (i.e., questions 2A and B, see Supplemental Material I). For each question, comparison among years (2006, 2007, and 2008) was significant ( p < 0.001) by one-way ANOVA. Results of a posteriori comparison of means for each question indicated by letters where means that share the same letter are not significantly different ( p > 0.05) with Bonferonni correction for experiment-wise error (α = 0.05).

See text for details of classification of categories.

Student-centered and Active-Learning Components

Student ranking of the helpfulness of different lecture components ( Figure 2 ) indicated significant differences among components ( F 7,2245 = 129.64; p < 0.001). Planned (a priori) comparisons of ranking scores indicated that across both years, learning goals were considered the most helpful lecture element, followed by clicker questions and weekly quizzes, which did not differ significantly. The vocabulary list and “daily dozen” reading questions were ranked least helpful and did not differ significantly. Group work, recitation, and outside class study groups received intermediate rankings. Planned comparisons of specific lecture elements between years indicated significant differences in the helpfulness ranking between 2007 and 2008 for the vocabulary list ( t = 3.19, df = 284, p < 0.01) and recitation ( t = 6.06, df = 284, p < 0.001). All other lecture components did not differ in helpfulness ranking between 2007 and 2008.

Figure 2. Mean (± SE) ranking of helpfulness for lecture components by students in 2007 (○) and 2008 (●). Lecture components as described in text: clicker = clicker questions; quiz = weekly quizzes; lgoal = learning goals; vocab = vocabulary lists; group = group work in class; daildoz = daily dozen reading questions; recit = optional weekly recitation section; and stdygrp = optional study group outside class. Results of one-way ANOVA testing for differences among lecture components was highly significant ( p < 0.001). The legend on the top indicates results of planned comparisons (A) among components (pooled across years) where components that share the same letter are not significantly different ( p > 0.05) and (B) between years (2007 and 2008) for each individual component, where * indicates p < 0.05, ns indicates p > 0.05.

Student Performance

Student performance on identical final exam questions (e.g., see Supplemental Material II) was greater in years when the material was taught in an interactive format (2007 and 2008) than in 2006 when the material was taught in a standard lecture format ( Figure 3 ). Student scores on questions concerning logistic population growth and life-history tradeoffs differed significantly among years ( F 2506 = 36.97, p < 0.001) and were higher in 2007 and 2008 than in 2006 ( p < 0.05; Figure 3 A). Student scores differed significantly among years on a question concerning island biogeography ( F 2505 = 14.55, p < 0.001), with scores in 2008 higher than scores in 2006 and 2007, which did not differ significantly ( p > 0.05; Figure 3 B).

Figure 3. Mean (± SE) points scored on identical final exam questions administered in 2006, 2007, and 2008. (A) Logistic growth and life-history evolution, (8 points possible). (B) Island biogeography, see Supplemental Material II (9 points possible). The legend in the top right indicates the results of one-way ANOVA testing for differences among years, *** p < 0.001. Results of a posteriori comparison among years indicated by letters, means associated with the same letter are not significantly different ( p > 0.05).

The Bloom's taxonomy scores assigned to final exam questions by two independent raters yielded moderate ( Altman, 1991 ) interrater reliability (weighted Kappa = 0.54), with the majority of disagreements (86%) due to differences between ratings at Bloom's levels 1 and 2. Therefore, for each final exam, we pooled the number of points available across lower-level (1–2, knowledge-comprehension) and higher-level (3–4, application-analysis) Bloom's categories. In 2006 and 2007, 82–85% of the final exam points consisted of lower-level Bloom's categories and 15–18% were higher-level Bloom's categories. In 2008, 75% of the final exam points consisted of lower-level Bloom's categories and 25% were higher-level Bloom's categories. Student performance on the final exam differed significantly among years ( F 2442 = 12.24, p < 0.001). Despite the higher proportion of points associated with higher-level Bloom's categories in 2008, performance in 2008 (average score = 91%) was significantly higher ( p < 0.05) than in 2006 (86%) and 2007 (85%), which did not differ ( p > 0.05).

A traditional lecture format in a large introductory classroom often emphasizes content rather than process and in doing so often fails to convey to students the nature of hypothesis-based inquiry which is at the heart of scientific research. There is reason to believe that this deficit diminishes learning outcomes and may contribute to the loss of some of our most talented students at the introductory level ( NRC, 2003 , 2007 ; Handelsman et al. , 2007 ). The primary goal of our course restructuring was to improve student attitudes in the course, motivated by the hypothesis that improved attitudes would lead to improved learning outcomes ( Weimer, 2002 ). The course reorganization we described sought to address these challenges by 1) reorganizing the course material to emphasize context, 2) engaging students with active learning in every lecture, and 3) creating a more student-centered classroom environment.

The data in Figure 1 clearly indicate that the changes we implemented in 2007 and 2008 improved student attitudes toward the course. For every question considered, student satisfaction scores increased significantly between 2006 and 2007 and did not differ between 2007 and 2008. It is important to note that in 2006, the first author was teaching this course for the third consecutive year. University teaching evaluation scores were consistent in the three years before 2007, and in fact a major reason for implementing the changes we describe in 2007 was that the instructor (first author) felt strongly that after 3 years, additional teaching experience alone was unlikely to cause a significant change in student response to the course. We therefore attribute the clear and consistent changes in student attitudes between 2006 and 2007 ( Figure 1 ) directly to the elements of course redesign we describe here, and the similarity of student responses in 2007 and 2008 ( Figure 1 and Tables 5 and 6 ) further supports this interpretation.

The students' free-responses summarized in Tables 5 and 6 are consistent with the data presented in Figure 1 . First, it should be noted that the proportion of positive comments increased from 2006 (65%) to 2007 (81%) and 2008 (89%). In 2006, the top category (56%) of positive response concerned traditional course material (e.g., PowerPoint slides, videos), whereas in 2007 and 2008 traditional course materials were mentioned in only 10–11% of the positive comments, and quality of instruction was the most common positive comment in both years at 24 and 27%, respectively ( Table 5 ). Together, these results clearly indicate that students' perception of the quality of instruction increased in 2007 and 2008, similar to results in Figure 1 .

Students' positive free-response answers explicitly referencing specific components of the course redesign were the second (14%) and third (13%) most frequent category of positive response in 2007, and second (16%) and fourth (12%) most frequent category of positive response in 2008 ( Table 5 ). These comments in 2007 and 2008 that specifically mention the active-learning and student-centered pedagogy we introduced in 2007 included references to “engagement,” “immediate feedback,” and “multiple approaches to learning.” There were almost none of these specific references in 2006.

With respect to negative free responses ( Table 6 ), in 2006 the most frequent category of response was that lecture was not stimulating (25%), whereas in 2007 and 2008 that category composed <1% of the negative responses. Again, these data corroborate the results in Figure 1 , where lecture was ranked as more stimulating in 2007 and 2008 than in 2006.

It is important to note, however, that two specific elements of our course redesign were explicitly mentioned as the first (group work, 17%) and second (weekly quizzes, 15%) most frequent category of negative response in both 2007 and 2008. Group work was also ranked relatively low in terms of helpfulness to student learning ( Figure 2 ). Our interpretation of the feedback on group work is that we need to further refine this element of the course. We adopted strategies for effectively implementing group work as discussed by Handelsman et al. (2007) and Ebert-May and Hodder (2008) . Students did not receive credit for these in-class active-learning exercises, but the requirement to report out to the class seemed to provide a strong incentive for most students to engage seriously in these activities. During each group-work exercise the instructor would move throughout the classroom to monitor group progress, and it was rare to find a group that was not seriously engaged in the exercise. However, the attempt to include a group exercise in almost every lecture meant that both the quality and rigor of exercises varied considerably. The group exercises that elicited the most animated student participation were those that were sufficiently challenging that very few students could solve the problem individually, but at least 50% or more of the groups could solve the problem by working as a team. Some of our most active group interactions occurred when we administered a challenging quiz, and then immediately allowed the students to retake the quiz as a group with the stipulation that the students would receive the highest of either their group or individual scores. This consideration suggests that a potential modification to further increase engagement in the group work would be to assign points to these in-class exercises ( Ebert-May and Hodder, 2008 ).

Our interpretation of the relatively high proportion of negative comments regarding the weekly quizzes ( Table 6 ) differs from that regarding the group work. The weekly quizzes were implemented in order to encourage students to keep up with the course material and to provide them with regular feedback on their understanding of the material in a low-stakes assessment environment. Note that in Figure 2 students ranked the weekly quizzes third highest in terms of helpfulness in both 2007 and 2008. We thus interpret the data in Table 6 and Figure 2 to indicate that although some students may dislike the weekly quizzes (administered at 8:50 am ), many recognized that these quizzes were helpful to their learning. Sixty-four percent of the respondents rated quizzes at 4 or 5 in terms of their helpfulness. The following quote is a typical comment made by students who rated quizzes at 4 or 5:

“Quizzes seemed like a hassle at first but in the end when our exams came up, since I had been studying all along for the quizzes, I had learned/studied most of the material, so I actually appreciate the weekly quiz system.” S136.2008.Q3B

We view these results as positive evidence of metacognitive awareness ( Bransford et al. , 2000 ) in that the weekly quizzes seem to have helped these students identify strategies for enhancing their own learning. This represents a particularly important goal for introductory classes that aim to prepare students for more advanced course work and independent learning.

Figure 2 indicates considerable consistency between 2007 and 2008 in the ranking of various lecture elements in terms of the helpfulness to student learning. The explicit learning goals ( Table 3 ) ranked highest in both years ( Figure 2 ). From a student's perspective, learning goals establish clear expectations about what skills and content students should master from each lecture. From an instructor's perspective, learning goals play a critical role in shaping both instructional activities and assessment through the process of “backward design” ( Wiggins and McTighe, 1998 ; Handelsman et al. , 2007 ), whereby learning goals explicitly articulate the desired learning outcomes to both instructor and students. Those desired outcomes then specify the assessment tasks that determine whether the desired outcomes have been met, and also shape teaching activities required to meet the desired goals. During 2007 and 2008, through the process of backward design, the learning goals provided a clear “road map” for both determining the content and organization of lectures and also for writing exams, whereas in 2006 both processes took place in a much less structured manner.

The personal response system (clickers) ranked the second highest in terms of helpfulness with learning in both 2007 and 2008 ( Figure 2 ). These results are consistent with those of a large number of previous studies documenting positive student responses to clicker systems (for review, see Judson and Sawada, 2002 ) and a large body of evidence indicating that the use of clickers and associated peer interaction (see below) can lead to improved student learning ( Crouch and Mazur, 2001 ; Knight and Wood, 2005 ; Preszler et al. , 2007 ; Smith et al. , 2009 ). In a recent and intriguing study from physics, Reay et al. (2008) found that the use of clickers not only led to increased learning gains in an introductory physics course but also seemed to reduce the performance difference between males and females.

The clickers were an effective pedagogical tool in our introductory biology course in several respects. First, the clicker system provided “real-time feedback” to the students ( Table 5 ). This feedback allowed the instructor to establish clear expectations regarding the depth of student understanding required to answer quiz and exam questions correctly. Simultaneously, this information allowed students to gauge their understanding continually relative to those expectations (i.e., formative assessment). The clickers were also extremely helpful in identifying, and thus allowing us to rectify, by addressing in a more direct and thorough manner, student misconceptions. Two striking misconceptions in our class concerned the ability to interpret a phylogenetic tree (see the “tree thinking” exercise by Baum et al. , 2005 ) and the failure to recognize that photosynthetic organisms not only fix CO 2 through photosynthesis but also release CO 2 through cellular respiration ( Wilson et al. , 2006 ).

The clickers were also very useful in initiating peer instruction in the classroom ( Mazur, 1997 ; Crouch and Mazur, 2001 ). This occurred when between 35 and 75% of the class answered a clicker question incorrectly, and students were then instructed to consult with a neighbor for 1 to 2 min to discuss their answers. The students were then repolled without being informed of the correct answers. Such occasions invariably led to animated discussion among the students in the class, and almost always resulted in an increase in the proportion of correct answers when the students were repolled. The clicker questions that generated the most animated student discussion were those that either did not have a single correct answer or that elicited a relatively even number of responses between two or more answers. These results are consistent with previous studies that demonstrate the efficacy of peer instruction facilitated by clickers to promote student learning ( Crouch and Mazur, 2001 ; Freeman et al. , 2007 ; Smith et al. , 2009 ). However, it is critical to recognize that it is the peer interaction rather than the clickers per se that promotes student learning ( Smith et al. , 2009 ), emphasizing that an appropriate underlying pedagogical design is essential for the effective use of clickers ( Mazur, 1997 ; Crouch and Mazur, 2001 ).

Student ranking of helpfulness for the vocabulary list and recitation increased significantly from 2007 to 2008 ( Figure 2 ). Notably, these components were ranked relatively low in 2007 and this feedback from the student questionnaire in 2007 enabled us to target these aspects of the course design in 2008. The vocabulary list presented at the beginning of each class (see Course Redesign) was ranked as the least helpful element of the lecture in 2007. The goal of these lists was to help students use technical terminology to formulate concise and precise answers to free-response questions on exams. In 2008 we discussed this goal in lecture and explicitly modeled the process several times. In the future, we plan to develop active-learning exercises that explicitly focus on clear written communication.

The increase in helpfulness ranking from 2007 to 2008 for the optional recitation session ( Figure 2 ) was particularly notable. Based on student feedback in 2007, in 2008 we moved the quizzes to Thursday so that they were given the day immediately after the recitation sessions. Attendance at the recitation sessions increased dramatically, consistent with the data in Figure 2 . This change from 2007 to 2008 provides an excellent example of how student feedback can be used to make simple changes that have a large impact on student satisfaction and performance.

Finally, we note that one of the major elements from the course in 2006 that was carried over into 2007 and 2008 was the daily dozen (a list of questions designed to help students identify important concepts in the textbook reading assignments), which ranked relatively low in terms of helpfulness compared with elements that were introduced as part of our course restructuring in 2007. We did not receive specific positive or negative feedback regarding the daily dozen on our questionnaire in 2006–2008 ( Tables 5 and 6 ), and we attribute the relatively low ranking of this component in 2007 and 2008 ( Figure 2 ) to greater enthusiasm for other components of the course.

Our data on academic performance are consistent with previous studies indicating that student-centered pedagogy and interactive-learning activities increase student performance ( Ebert-May et al. , 1997 ; Udovic et al. , 2002 ; Knight and Wood, 2005 ; Freeman et al. , 2007 ; Walker et al. , 2008 ). The data in Figure 3 illustrate student performance on identical final exam questions administered in all 3 years and show consistent increases in performance between 2006 and 2008. Furthermore, the proportion of points on the final exam for questions at higher levels of Bloom's taxonomy (levels 3–4, application-analysis) increased from 15–18% in 2006–2007 to 25% in 2008. Furthermore, the average student performance on the final exam also increased in 2008 (91%) relative to 2006 (86%) and 2007 (85%). Together, these results indicate increased academic performance and imply increased proficiency with higher-order problem-solving skills associated with the changes in instructional design implemented in our course. These conclusions are somewhat conservative because the 2006 final exam contained a section in which students were allowed to choose six of eight questions to answer, but students were not given any choices on the 2007 and 2008 final exams.

The results on student performance noted above suggest that the most pronounced increases in performance occurred between 2007 and 2008, whereas results in Figure 1 and Table 5 indicate that student attitudes increased significantly from 2006 to 2007 and did not change between 2007 and 2008. We believe these results indicate that a semester of experience with implementing the active-learning and student-centered pedagogies in 2007 made these approaches more effective in improving student performance in 2008. Although the initial goal of our course redesign was to target student attitudes, we are now initiating more intensive efforts to quantify student learning by using pre- and postcourse assessment tools, assessment of higher-order skills such as the interpretation of primary literature, and performance on the Biology GRE.

Institutional Context

The course redesign we implemented required a significant time investment both in the approximately 6 mo leading up to 2007, and during the first semester of implementation. Attendance at a national workshop, the National Academies Summer Institutes on Undergraduate Education in Biology ( www.academiessummerinstitute.org/ ) provided significant background theory and training. Also, in fall 2006, we convened a series of on-campus seminars featuring national leaders in science education ( http://cndls.georgetown.edu/events/symposia/TFU/ ). These seminars were particularly useful both in generating support from our departmental colleagues to implement changes in a course that is foundational to the department's curriculum and also in providing the opportunity to discuss specific details of course redesign with individuals highly experienced in implementing active-learning and student-centered pedagogical approaches. It is important to note, however, that once the initial course redesign was implemented in 2007, teaching the course in 2008 did not require a significant additional time commitment relative to 2006 (before our changes were implemented) and yet the increased positive student response to the course was sustained ( Figure 1 and Table 5 ). Furthermore, the improved scores on the university-administered course evaluations (see questions 2–6 in Figure 1 ), the primary mechanisms of assessing teaching at most institutions, indicates that the time investment required to implement a course restructuring can have a positive impact on instructor evaluation criteria.

Finally, the course redesign had another unanticipated benefit: it improved not only the students' attitude toward the course but also the instructor's morale and enthusiasm. Introductory Biology II has long been a problematic course for our department because of deficiencies noted in the Introduction (poor student attitudes, passive [superficial] learning, and suboptimal student performance). As a consequence, instructors often lose enthusiasm for teaching this course after 2 to 3 years. However, the interactive pedagogy and positive student responses made this a much more exciting and rewarding course to teach in 2007 and 2008.

The changes we implemented also have had an impact at the departmental level. Based in part on the positive student reactions to interactive and student-centered pedagogy in Introductory Biology II, four instructors have implemented the use of clickers in their courses and one faculty member attended the 2007 National Academies Summer Institutes on Undergraduate Education in Biology.

In summary, we developed and implemented an instructional design that focused on incorporating active-learning and student-centered pedagogy into what was previously a traditional lecture-based introductory biology course. These changes led to sustainable improvements in student attitudes and performance. Although the changes we implemented required a significant time commitment in the first year (2007), this was essentially a “one time investment” because it did not require extra effort to teach the course using the revised model in 2008. Furthermore, several faculty in our department have begun to incorporate interactive and student-centered pedagogies into their courses. The course reorganization we describe thus not only provides a model for revision of an individual course but can also provide a catalyst for institutional reform.

ACKNOWLEDGMENTS

We thank Janet Russell and Michael Hickey for scoring our final exam questions according to Bloom's taxonomy; and Diane Ebert-May, Jay Labov, Janet Russell, Debra Tomanek, and two anonymous reviewers for helpful comments on previous versions of this manuscript. We also acknowledge Diane Ebert-May, Diane O'Dowd, and Robin Wright for valuable input on our course restructuring. Financial support was provided by the College Curriculum Renewal Project of Georgetown College and the Undergraduate Learning Initiative from the Center for New Designs in Learning and Scholarship, Georgetown University.

1 http://dels.nas.edu/summerinst/index.shtml .

2 http://first2.plantbiology.msu.edu// .

3 http://pedagogy.merlot.org/ .

4 http://tiee.ecoed.net/ .

5 http://nsdl.org/ .

6 www.biosciednet.org/portal/ .

7 www.sencer.net/search.cfm

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• Scott Cooper
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Submitted: 27 March 2009 Accepted: 28 May 2009

© 2009 by The American Society for Cell Biology

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Active essay writing: ten steps to success!

Dr Heather Taylor

What is the idea?

This chapter presents one approach to using the Active Essay Writing Programme (Taylor et al., 2019); an innovative approach we devised at the University of Sussex to support students in planning, preparing, and presenting original, insightful, and well-presented essays.

Why this idea?

From our experience, most students arrive at university with little-to-no understanding of how to approach essay assignments. If left unchecked, this can lead to them feeling overwhelmed and, in-turn, submitting incoherent and uninspired essays that do not accurately reflect their true potential. The Active Essay Writing Programme was designed to address these issues and make the task of completing essays more manageable and enjoyable for students.

How could others implement this idea?

To guide your students through this ten-step programme, you will need to create an example essay question that you can collaboratively answer with them. This will provide them with the opportunity to develop and subsequently apply the acquired skills to their own essay assignments.

Step 1:  Deconstruct the essay question

The first issue students sometimes face when approaching essay assignments is not fully understanding the question. This can result in them writing (potentially good) essays on completely the wrong topic! The first step is to therefore deconstruct the example essay question with your students, breaking it down into its component parts to ensure understanding and overcome any confusion.

Step 2: Generate ideas

Students are often under the misconception that before they can answer an essay question, they must read everything ever written on the topic. Their attempts to do this creates more confusion than it solves and, in turn, can result in submissions that are largely incoherent regurgitation of academic texts. Instead, we encourage our students to ‘think first’ to help them develop a focused strategy for identifying relevant literature to support their essays. To demonstrate this, ask your students to generate ideas, arguments and counterarguments that might help to answer the example essay question and add these to a mind map.

Step 3: Find evidence

After developing their own mind maps, students will need to see if evidence exists to support their ideas. To demonstrate this process, select some ideas from the example mind map and search for relevant reliable evidence.  If no evidence exists for certain ideas, important learning has still taken place; we know that these ideas are unsupported and should therefore not make it into an essay, and, in doing so, greater insight into the topic has been acquired.

Step 4: Critically evaluate the evidence

The type, style, and depth of critical evaluation will differ by discipline and your students’ level of study. In consideration of this, critically evaluate some of the sources identified for the example essay with your students to model the skills involved. While revisiting these sources, also be sure to take more detailed notes as these will be used at the next step.

Step 5: Get organised

Another stumbling block for our students lies in their assumption that there is one correct way to organise an essay. We alternatively encourage them to organise their essays based on their own narrative. To illustrate this, separate (either on paper or digitally) the ideas, summaries of evidence, and critical evaluations generated for the example essay and ask your students to organise this information in a way that makes sense to them. No two students will present the information in the same exact order, illustrating that multiple presentations can be ‘correct’.

Step 6: Flesh out the essay outline

The end-product of Step 5 is an essay outline, and so the natural next step is to develop paragraphs. The key here is to ensure that students are not wasting words through unconcise writing and/or inclusion of unnecessary detail, while still providing the reader with sufficient context for understanding the points being made. At this stage, it is also good idea to encourage students to cite and reference their sources as they go. Work on a couple of paragraphs with your students and/or provide them with examples of pre-made poorly written paragraphs and ask them to identify issues and edit accordingly.

Step 7: Introduce the essay

Unlike the main body of the essay, when illustrating how to format an informative introduction paragraph, we encourage our students to adhere to the following three rules:

• Set the scene by introducing the context and importance of the essay.
• Signpost the reader to the key points that well be considered.
• State the overall argument/ answer to the essay question so that the reader is made aware of this right from the start

Work collaboratively with your students to create an introduction paragraph for your example essay.

Step 8: Conclude the essay

Our main suggestion here is for students to go beyond simply summarising to synthesising the content of their essay. Using the example, encourage your students to take a step back and consider the essay in its entirety before working together to connect the main points and develop a take-home message for the reader.

Step 9: Check citations and references

As we encouraged students to cite and reference as they went along, the formatting of these may not be 100% correct. To ensure that your students understand how to format correctly, provide them with a guide and ask them to either apply it to specific sources used in your example essay or correct errors in pre-made examples.

Step 10: Proof-read

By this point students will have spent a great deal of effort panning, preparing, and presenting their essay – wouldn’t it be a shame if the reader could not understand it? To illustrate this, provide them with the example essay (or a select few paragraphs) full of typos, spelling mistakes and grammatical errors for them to correct, so that they can see first-hand the difference between an unchecked first draft versus a carefully proof-read and edited submission.

Taylor, H., Garnham, W.A. & Ormerod, T. (2019). Active essay writing: Encouraging independent research through conversation. In T. Betts, W. A. Garnham, & P. Oprandi (Eds.) Disrupting traditional pedagogy: Active learning in practice . University of Sussex Library. https://doi.org/10.20919/9780995786240

About the author

name: Dr Heather Taylor

institution: University of Sussex

After several years working as a Doctoral Tutor,  Dr Heather Taylor was appointed in 2019 as a Teaching-Focused Lecturer in Psychology at the University of Sussex. Since then, Heather has convened two Foundation Year Psychology modules and taught on other Undergraduate Psychology modules. Within her role, Heather has focused heavily on developing her students’ academic skills via active-learning approaches and has been appointed the Head of Attainment for the School of Psychology. 

100 Ideas for Active Learning Copyright © 2022 by Dr Heather Taylor is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Digital Object Identifier (DOI)

https://doi.org/10.20919/OPXR1032/34

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Getting started with Active Learning

Cambridge International Education Teaching and Learning Team

What is active learning?

Active learning is a process that has student learning at its centre. Active learning focuses on  how  students learn, not just on  what  they learn. Students are encouraged to ‘think hard’, rather than passively receive information from the teacher. Research shows us that it is not possible to transmit understanding to students by simply telling them what they need to know. Instead, teachers need to make sure that they challenge their students’ thinking. With active learning, students play an important part in their own learning process. They build knowledge and understanding in response to opportunities provided by their teacher.

What is your definition of active learning? Listen to these teachers giving their personal definitions. How do their definitions compare with yours?

In the rest of this unit we will look at the basics of active learning in more detail. We will look at the theory behind active learning, discuss the benefits of an active learning approach and discover some practical examples. We will also hear from experienced teachers, who will be sharing their ideas. Throughout the unit we will ask you reflective questions that will help you to think about how you can apply active learning in your lessons. At the end of the unit there is a glossary of key words and phrases.

What is the theory behind active learning?

Active learning is based on a theory called  constructivism . Constructivism emphasises the fact that learners construct or build their own understanding. Constructivists argue that learning is a process of 'making meaning'. Learners develop their existing knowledge and understanding in order to achieve deeper levels of understanding. This means that learners are more able to analyse, evaluate and synthesise ideas (thus achieving the higher order skills of  Bloom’s Taxonomy ). Skilled teachers make these deeper levels of understanding more possible by providing learning environments, opportunities, interactions, tasks and instruction that foster deep learning. The theory of 'social constructivism' says that learning happens mainly through social interaction with others, such as a teacher or other students. One social constructivist,  Lev Vygotsky (1896–1934) , developed the idea of the  Zone of Proximal Development . This zone lies between what a learner can achieve alone and what a learner can achieve with their teacher’s expert guidance. Skilled teachers focus learning activities in this zone. Skilled teachers scaffold learning by providing guidance and support that challenges students based on their current ability. This helps students to develop their understanding in stages. Skilled teachers also provide rich feedback using  Assessment for learning  (AFL). Skilled teachers use AFL to help students to understand two things: firstly their current strengths and weaknesses and secondly what they need to do to improve. AFL activities are sometimes based on formal assessments. However, AFL can also be based on many types of informal assessment which can include peer assessment, where students assess each other. Active learning also links to other theories of learning: Learning should be relevant and within a meaningful context. This idea was developed by the philosopher  Jean-Jacques Rousseau (1712–1778) . It influenced numerous educators in the early 20th century such as  John Dewey (1859–1952)  and  Maria Montessori (1870–1952) . The main idea is that we learn best when we can see the usefulness of what we learn and connect it to the real world. Learning is developmental. Learning experiences for young people should be appropriate to their level of development. Some of this is linked to their age, although development level and age are not always the same thing.

What are the benefits of active learning?

Active learning helps students to become 'lifelong learners' In an active learning approach, learning is not only about the content, but is also about the process. Active learning develops students’ autonomy and their ability to learn. Active learning gives students greater involvement and control over their learning. This means that students are better able to continue learning once they have left school and college. Active learning encourages success Cambridge examinations do not simply test recall of knowledge. Successful candidates draw on their understanding in order to evaluate and synthesise ideas. Therefore, Cambridge programmes and qualifications are best taught using an active learning approach. Encouraging active learning helps students to achieve higher grades, based on their enhanced skills and understanding. Because active learning encourages students to take a central role in their own learning, it prepares them better for both higher education and for the workplace. Analytical skills also help students to be better at problem solving and applying their knowledge. Universities and employers value this. Active learning is engaging and intellectually exciting An active learning approach encourages all students to stay focused on their learning, which will often give them greater enthusiasm for their studies. Teachers also find that they enjoy the level of academic discussion with their students which an active learning approach encourages. Listen to these educators giving their views on the benefits of active learning for their students. Which of the benefits are most relevant to your students?

Seven misconceptions about active learning

1. 'Active learning is all about doing a particular activity' Active learning is about encouraging students to engage actively with their studies. The learning objective is more important than the task itself. For instance, many people think that a small-group task is automatically an active learning task. People also often think that a whole-class discussion cannot be an active learning task. In fact, whether something is an active learning task or not depends on the teacher's planning and style. Skilled teachers ask themselves questions such as: In a class discussion am I using open-ended questions to get my students thinking? In a group task do the students know what the learning aims are? In a seminar activity do the students have effective resources to support them? All activities must be relevant to what you want the students to learn. Some learning objectives might lend themselves best to students engaging in small-group seminars or a collaborative project. Other objectives might be better with a more lecture-style approach. 2. 'Active learning is the same as enquiry-based learning' Enquiry-based learning is also known as problem-based learning. In enquiry-based learning, the student learns by exploring a series of questions. Sometimes these questions are set by the teacher, and sometimes by the students themselves. Students will then decide how they can answer these questions most effectively. Teachers will be on hand to help, but students lead the process. Enquiry-based learning can be an excellent technique for encouraging active learning. However, as we will see later in the 'Active learning in practice' section, it is only one of many techniques. As with all teaching, the focus needs to be on the learning not the task. Ask yourself: Is a student-led enquiry the most effective way for my students to achieve their learning objectives in this lesson? 3. 'Active learning means taking away the teacher’s influence' Active learning does not mean reducing the role of the teacher. The teacher is still the director of their students’ learning. Skilful planning is very important. For example, you need to consider: what your students are going to get from an activity, what resources you need to provide and how you are going to assess your students’ progress. 4. 'Active learning means a complete change of teaching style and classroom layout' Active learning does not have to mean a complete change to classroom practice. You should think about how your students will learn in each activity. Occasionally, you might need to design a completely new activity or major classroom change. However, the changes required will often only be small ones. You might even realise that you are already promoting active learning but you did not recognise it. 5. 'Active learning will cause bad behaviour' If students are actively engaged in a group discussion, the classroom will be noisier than if you are the only one talking. However, as with any activity, you will still be in charge of the class. You will need to decide what levels of noise you are happy with. One of the exciting things about active learning is that students will want to engage with you in discussion. Sometimes they will want to discuss your interpretations and ideas. Healthy discussion is beneficial for students and teachers. However, you are still in charge of the class, and need to decide when things are available for discussion, and when the class needs to move onto the next topic or task. 6. 'Students have to be physically active' Active learning is about making the brain active, not the person. Active learning does not mean that students have to move around the room. While students can move around the classroom if appropriate, they can also remain seated at their desks. 7. 'Active learning makes students less respectful' A student who is engaged in thinking for themselves might not always agree with their teacher. However, healthy discussion in a respectful environment does not mean that the students will respect their teacher less. Healthy discussion means that students are engaging with their teacher as a partner in their learning. Which of these seven misconceptions do you think you will hear from parents, students or colleagues? What will you say to them?

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An active learning checklist

If you are new to active learning, it will help to ask yourself the following questions: What do the students in my class need to learn? Try to think about skills as well as subject content. How will the task that I have chosen help my students to learn? Different learning outcomes need different types of task. You know your own students’ strengths and weaknesses. Therefore, you can think about what your students particularly need to help them to achieve. How am I using questioning? It is easy for teachers to ask lots of factual-recall questions and other closed questions. On the other hand, open-ended questions help students to think for themselves, and to develop their ideas. It is also helpful to ask follow-up questions that will prompt your students to say (and think) more, such as ‘Tell me more about that’, or ‘Why do you think that?’. Make sure that all students are involved in a discussion. Consider choosing students to answer, rather than inviting them to raise their hands. This way, every student has to think, because every student might be asked to contribute. Also consider pausing before letting your students answer. Leaving three seconds, rather than one, before you allow students to answer gives everyone more time to think about what they want to say. How far am I creating a positive classroom environment where it’s fine to take intellectual risks? Students need to be confident in trying out new ideas. They need to know that they will not be laughed at, and that there are high levels of mutual respect. If I need to focus on content, can I encourage the development of a skill at the same time? For instance, if a teacher wants their students to learn important factual information that they can use in an essay, he/she could try the following activity: (i) Ask the students to think of five key facts which they could use as evidence for a particular essay question. (ii) Ask the students to use at least one of these facts to write a short paragraph as part of an answer to this question. (iii) Ask how the five facts could be re-used for a different essay question on the same topic. The teacher could either give them these questions, or could get them to think of their own questions. (iv) Ask the students to write a paragraph as part of an answer to one of these new questions. They should use at least one of their five facts to support their point. In this way, the students are learning the factual information and also the analytical application of this information. The same is also true the other way around – skills development work usually leads to more high-level thinking if it is linked to meaningful content. How will I present the task to the students? Students can be a little nervous about being encouraged to take more responsibility for their learning. It will help to explain briefly to your students why they are doing the task and what they will learn from it. If possible, explain any connection between the task and what your students need for their final examination, so that they understand why it will be useful. How will I know that every child in my class has learned something? If you build in assessment tasks, you can check your students’ learning. Effective assessment will give you a good idea of what to focus on in the next lesson, and will also help you to find out which tasks are most helpful for which students. Assessments do not have to be formal (or marked). They are a diagnostic tool to help you and your students to find out what has been learned and understood.

Active learning in practice

When people start thinking about putting active learning into practice, they often make the mistake of thinking more about the activity they want to design than about the learning. The most important thing is to put the student and the learning at the centre of your planning. A task can be quite simple but still get the student to think critically and independently. Sometimes a complicated task does not actually help to develop the students’ thinking or understanding at all. Consider carefully what you want your students to learn or understand and then shape the task to activate this learning.

"...put the student and the learning at the centre of your planning."

There is no typical active learning task. However, all active learning tasks tend to focus on encouraging the students to 'think hard' for themselves, rather than being passive recipients of knowledge. The following interviews show teachers giving some examples of active learning approaches they have used with their students. As you listen, think about what it is about each activity that could help develop the students’ learning. What was the activity that each teacher chose and why did they choose it?

Here are some activities to help you to further explore active learning. Observation Observe a lesson taught by an experienced colleague. As you are watching, ask yourself what opportunities this colleague is creating for active learning. Think about what it is about the task which makes it an active learning opportunity. After the lesson, think about how you might apply this in your own teaching. Planning Think of one thing you would like to try in your teaching this week which would make learning more active for your students. If you can, try it out in one of your classes. At the end of the session, reflect on what went well: Why did it go well? Were there things which did not go well? Why do you think that was? How could you make changes next time? Next think about something you would like to try over the next term. Again, if you can, try it out with your students. Then think about what you would like to put into your planning for the next academic year. What would you need to do to help that to happen? Some people are not familiar with active learning. What would you say to a colleague to convince them of the benefits of an active learning approach? You can use the Reflection worksheet to keep a written record of your thoughts and ideas. Finding out more There are lots of excellent materials to read and watch on Active Learning. We have listed a small section of these below: The Visible Thinking Project and the ORBIT Project both have excellent websites, with lots of examples of activities which can be used to help students learn in an active way. Professor John Hattie is a researcher in education. In his book Visible Learning for Teachers: Maximizing Impact on Learning, Hattie looks at thousands of studies of teaching and learning. He then uses these studies to assess the impact of over 100 different strategies. His book includes reflective questions and activities for teachers and leaders. John Hattie’s TED talk Why are so many of our teachers and schools so successful? is a useful introduction to his ideas.

Why are so many of our teacher and schools so successful?

Cambridge's guide, Implementing the Curriculum with Cambridge: A Guide for School Leaders outlines an active learning approach. It is aimed at school principals, school leaders and others responsible for the educational programme in a school. Information on Cambridge professional development qualifications and courses can be found on our website .

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Our new course getting started with assessment for learning will help you to understand how to apply asssessment for learning in your teaching practice. during the course you will explore the following questions: - what is assessment for learning - what does effective assessment for learning look like - how does assessment for learning apply to my own practice - what is my action plan - how do i know that including assessment for learning in my classes made a positive impact for more information on the course and how to book your place visit our website..

Active learning Learning which engages students and challenges their thinking, using a variety of activities Analyse To study or examine something carefully and in detail in order to understand it more. Assessment for learning Essential teaching strategies during learning to help teachers and students evaluate progress in terms of understanding and skill acquisition, providing guidance and feedback for subsequent teaching and learning. Autonomy The freedom to follow one’s will or actions independent of external influence or control. Closed question A question that can be answered with either a single word (usually ‘yes’ or ‘no’) or a short phrase and the choice of answers is limited. Collaborate To work together with someone else, or others, for a particular purpose. Constructivism A philosophy of learning based on the concept that people construct their own understanding by reflecting on their personal experiences, and by relating the new knowledge with what they already know. Individuals create their own mental-models, known as 'schemas', to make sense of the world. Individuals accommodate new knowledge by adjusting their 'schemas'. Critical thinking The ability, underlying all rational discourse and enquiry, to assess and evaluate analytically particular assertions or concepts in the light of either evidence or wider contexts. Differentiated learning Adapting one’s teaching to suit the needs of different students for their current level of understanding and performance, by providing appropriate learning activities, support, and assessment, so that all students in the group can learn effectively. Evaluate To judge or determine the quality, importance, amount, or value of something. Formal assessment Planned and structured measurement of learning. Formative assessment Activity that provides students with developmental feedback on their progress during the learning programme and informs the design of their next steps in learning. Open ended question A question that allows for a long response and for which the choice of answers is not restricted. Scaffold learning The teacher provides appropriate guidance and support to enable students to build on their current level of understanding progressively to acquire confidence and independence in using new knowledge or skills. Summative assessment Typically end-of-learning assessment tasks such as examinations and tests, to measure and record the level of learning achieved, for progression to the next level or for certification. Synthesise To create something new by combining different existing elements or ideas. Transcribe To make a written copy of spoken material. Zone of proximal development (ZPD) The difference between what a learner can achieve when they receive support and what they can achieve independently.

Active Learning in Professional Health Education Essay

Introduction, literature review, theories of learning and teaching that inform an active learning approach, social constructivism and sociocultural approach, humanistic approach, social cognitive theory, considerations.

Active Learning (AL) is gaining popularity all over the world, even though giving up the ineffective traditional methods of education proves to be a prolonged process (Aliakbari, Parvin, Heidari, & Haghani, 2015). Moreover, AL is being actively promoted in nursing, midwifery, and other educational environments (Herinckx, Munkvold, Winter, & Tanner, 2014, p. 31). The approach is “democratic” in ensuring the participation and active engagement of students, which appears to correspond to modern trends, but to understand the reasons for its popularity, more extensive research into its theory, practice, and context is required.

Definitions

Modern health professional education (HE) can be defined as a “broad and complex set of events, processes and influences, both deliberate and unplanned” that surround the aspiring medical specialist since the beginning of studying and until the final day of practice (Mann, 2010, p. 61). Indeed, nowadays, healthcare professionals are expected to improve their skills continuously; apart from that, medical competence is now considered to be the “professional identity” of a person (Mann, 2010, p. 61). This identity includes the profession-related skills and knowledge but is not limited to them: the requirements of the profession include cognitive and communicative skills, self-awareness, self-improvement, self-assessment, and others (Mann, 2010, p. 62). As a result of these changes, lecturing has stopped being the key or the most effective way of teaching (Kroning, 2014). Traditional education is hardly capable of producing such an outcome, but new approaches, especially AL is technically geared towards achieving it.

AL is any “engaging activity other than that of passively listening to teachers lecture” (Kroning, 2014, p. 448). This definition is very generic, but it is understandable since the means of engaging students are numerous. In this paper, several approaches that can be used in classroom settings will be presented to illustrate the notion.

Active Learning Approaches

To better understand the notion of AL, its specific approaches should be considered. Classroom diversity implies that the varied methods based on these approaches can be suitable for or preferred by a particular part of the learning cohort. As a result, the use of various techniques may be preferable and should be introduced in every particular classroom (Boctor, 2013; Cubas et al., 2015; Moore, 2012; Robb, 2013).

Problem-Based Learning

Problem-based learning (PBL) is an AL approach that “uses authentic artifacts reflecting real-world situations for students to practice problem-solving skills through collaboration with their peers” (Martyn, Terwijn, Kek, & Huijser, 2014, p. 829). Thus, apart from providing the profession-related knowledge, it is aimed at the formation of cognitive and communication skills; besides, this approach qualifies as a self-directed one. It is widely used in nursing and midwifery since problem-solving skills are of especial importance for their future professions (Choi, Lindquist, & Song, 2014). As a result, the approach has proved to be useful for the training of nursing and midwifery students in pre-and post-graduate programs, especially when compared to traditional lecture-based methods (Martyn et al., 2014; Choi et al., 2014).

Team-Based Learning

Team-based learning is a very popular means of teaching nowadays that has also been proved to be effective in HE (Clark, Nguyen, Bray, & Levine, 2008; Maslakpak, Parizad, & Zareie, 2015). It improves engagement, provides the training related to interpersonal skills, and most often is also geared towards the development of other skills, for example, cognitive ones (Lubeck, Tschetter, & Mennenga, 2013). A type of team-based learning is the “near-peer” one that is particularly useful for nursing and can also be combined with simulation experience (Owen & Ward-Smith, 2014). For example, the near-peers (who are the upper-level students) can play the role of patients and mentors. As a result, not only profession-related, collaborative, and communicative skills of the students were trained; for the upper-level student, leadership and teaching ones were also being developed.

Hands-on Learning

The hands-on learning approach offers the students the opportunity to apply what they learn through “physical demonstration” (Robb, 2013, p. 304). It reinforces knowledge, improves the students’ confidence, and helps them to determine and deal with difficulties. An example of such an approach is simulations.

Simulations are concerned with creating an experience that is matching reality to some degree and is meant to recreate a real situation but is not real (Moyer, 2016). Both low- and high-fidelity simulations have been proved to help students organize their knowledge and apply it in the course of such a “practice.” At the same time, low-fidelity simulations are much more feasible and easier to arrange, which is a plus in classroom settings (Moyer, 2016, p. 68). In nursing and midwifery, simulations can be created with the help of software (even in virtual classrooms) or mannequins and actors (McAllister, Searl, & Davis, 2013).

Learning games can also be mentioned as a technique that is particularly approved by a large number of students (Boctor, 2013, p. 96). It is especially good at attracting attention and making students curious, which is one of the goals of AL (Kroning, 2014). From the point of view of the teacher, the specific outcomes may vary. The game described by Boctor (2013), for example, mimicked the “Jeopardy” game and included nursing questions, answering which brought a team “money.” It was aimed at reviewing and learning new knowledge and collaboration. Also, games produce immediate feedback in the form of scores.

Justifying AL

The reasons for the popularity of AL and its promotion in nursing and midwifery education are numerous. First of all, it is admittedly more innovative and suitable for the modern kind of HE than the traditional approach (Herinckx et al., 2014). Besides, AL has been suggested as a more appropriate model of learning for Millennial students: for example, by Robb (2013), Kroning (2014), Welch (2013). Therefore, it is a timely approach suitable for modern students. Moreover, nursing students have reported preferring AL: in other words, it contributes to their satisfaction, which is essential for engagement (Boctor, 2013, p. 96).

The shift towards AL is of particular importance for the classroom settings that are typically concerned with the acquisition of knowledge. Modern opportunities (for example, simulations) have started to blur the line between classroom and clinical settings, though, but it is still apparent that AL is in the process of dramatically changing the former (Fahlberg, Rice, Muehrer, & Brey, 2014; Moyer, 2016). Indeed, AL challenges the classic means of passing the knowledge, which makes the application of its theory to the classroom setting particularly timely. It is also noteworthy that the classroom and clinic settings should not be considered completely detached from each other: in reality, the two need to be collaboratively used to ensure that students use their classroom knowledge in the clinical environment (Po-Han, Hwang, Liang-Hao, & Yueh-Min, 2012). AL is capable of affecting both settings and assisting in uniting them (Dewing, 2010; Wonder & Otte, 2015). Still, they both have their specifics that need to be taken into account, and here, the focus will be on the classroom settings.

AL is still being developed. Its introduction into nursing and midwifery is a new tendency, which means that there is still much room for experiment, research, and improvement (Welch, 2013; Moyer, 2016). Still, its effectiveness in the development of varied skills (problem-solving, reflection, communication) and expanding and organizing knowledge, improving psychomotor skills have been studied and proved to an extent (Moyer, 2016). Therefore, the study of AL in the classroom teaching environment for nurses and midwives is justified, and this paper attempts to provide insights into this topic.

The approaches of AL are based on and reflect the theory of learning and teaching. The key learning theories that have had an impact on HE and AL are behaviorism, cognitivism, social, and humanistic learning (Hoy, Davis & Anderman, 2013).

Behaviourism

Behaviourism was the first theory that managed to explain learning from the scientific point of view (Kay & Kibble, 2016). This approach emphasizes the role of the environment (stimuli and consequences) in learning and human behavior in general (Mann, 2010). This theory of learning is well-established, and its methods (positive and negative reinforcement) are very often used in teaching to shape the desired behavior.

Clearly, behaviorism emphasizes the role of the teacher as the environmental agent that produces the stimuli and controls the consequences, but it still contributes to the AL, for example, in the form of incentives and rewards as well as the role of repetition (Kay & Kibble, 2016). Another significant aspect of behaviorism is feedback: according to Mann (2010), behaviorist theory gave rise to this phenomenon (p. 62). In other words, AL uses elements proposed by behaviorism.

Cognitivism

In contrast to behaviorism, cognitivism focuses on the unobservable processes that allow learning to happen: it studied the memory, organization and storing of knowledge, experience processing, decision-making, clinical reasoning, expertise development, and so on (Kaylor, 2014; Aliakbari et al., 2015). Unlike the behavioral theory, this one focuses on the learners and considered them to be active agents who are also unique from the point of view of their pre-existing knowledge, skills, and general ability (Kay & Kibble, 2016). From the point of view of AL, the theory has had an impact on PBL (Mann, 2010). Many memory-enhancing techniques (for example, reviewing, notes, distinguishing crucial knowledge) that are of interest for AL have also been based on the information gained by this theory (Kaylor, 2014).

Based on Vygotsky’s work in many ways, social constructivism focuses on the social and cultural aspects of learning (Hoy et al., 2013). Here, the knowledge is divided into that existing at interpersonal and intrapersonal levels, the latter being a consequence of coming into contact with the former with the help of “semiotic mechanisms” – tools, maps, technology, language, and so on (Kay & Kibble, 2016, p. 22). This theory promotes learning that advances the development of a particular student, and the levels of development are naturally diverse among the learning cohort. The performance here has lesser importance for the theory. The sociocultural approach effectively promotes collaboration, participation, and engagement, which are some of the pillars of the AL (Aliakbari et al., 2015).

The humanist approach (HA) to learning is focused on the concept of self-actualization (Mann, 2010). According to HA, people inherently want to learn and develop. The approach unites theories related to motivation, “self-regulation and self-directed lifelong learning” (Mann, 2010, p. 63). The latter is the goal of ME; apart from that, these theories have contributed to the development of PBL. The teacher is expected to be a facilitator rather than the provider of the knowledge, which is a relatively new and underdeveloped role that contrasts the traditional one (Aliakbari et al., 2015). A drawback of HA is an excessive idealization of human nature (Mann, 2010). Still, the key elements and beliefs of the approach are indeed useful for the development of a professional identity rather than just a set of skills.

The social cognitive theory (SCT) basically “incorporates the behavioral, cognitivist and humanist perspective” within the field of social learning theory (Mann, 2010, p. 63). In other words, it admits that the environment and the learner both have an impact on the learning process and the outcomes; it also focuses on the behavior itself and the way cognition mediates it. This approach favors observation as a learning method, which corresponds to role models in HE. Similarly, the simulation approach is based on this comprehensive theory, in particular on the humanistic contribution of self-reflection (Burke & Mancuso, 2012). SCT is aimed at uniting the valid points of all of the previous approaches and attempting to eliminate their limitations (Kay & Kibble, 2016). Indeed, the key theories all have advantages and disadvantages; as a result, the approaches that are used by the practitioners of education can be based on a mixture of the elements from these theories resulting in a blend that is best suited for their students (Hoy et al., 2013; Moore, 2012).

With the development of the notion of AL, the whole concept of education has been and is still being reconsidered. For example, some researchers believe that the new approach demands a new environment. The AL classroom needs to be organized differently from that meant for lectures: it needs to be more suitable for engagement and avoid discriminating students depending on the position of their seat (Park & Choi, 2014). Similarly, it needs to be equipped with necessary or recommended tools (for example, classroom response technologies or those aimed at simulation and other virtual experiences) (Welch, 2013; O’Flaherty & Laws, 2014). The integration of up-to-date technology is bordering on a must. Modern students practically grew up with it; moreover, operating contemporary devices is a skill that they are likely to need in the future. This technology also has a great potential for enhancing learning, and it can and should be used during lessons (Kroning, 2014).

Not all the considerations are as easily defined and foreshadowed. AL is more challenging from the point of view of the teacher: it requires the development of non-traditional roles, innovation, out-of-the-box thinking (Kroning, 2014; Walters, 2014). The fact that it is a challenging task is proven by the research devoted to the effectiveness of the approach. The students who were engaged in AL report feeling more confident and satisfied with their results (Everly, 2013; Fahlberg et al., 2014). However, the assessment of the actual effectiveness (end test performance) of the approach brings mixed results (Waltz, Jenkins, & Han, 2014). According to Waltz et al. (2014), the inconsistency might be connected to the fact that the studies offer varied and often incomplete definitions of AL and choose not to use randomized designs. In general, more consistent research is needed to evaluate those methods. It is also noteworthy that the standardized test maybe not sufficient to assess the outcomes of AL that are often concerned with communication skills, or, for example, general development concerning one’s level (Aliakbari et al., 2015). Still, the issue might also be connected to the mentioned difficulties of the role of the teacher in an AL classroom that the educators need to be prepared to.

AL is likely to be described as the educational approach of the future. It has absorbed the elements of the key educational theories and uses varied approaches, all of which are aimed at engaging students and helping them to learn to study, communicate, think, and work to develop their competency. AL is noticeably more challenging than the traditional lecture approach, but it is also more suitable for the development of the professional identity that is demanded from the modern HE. As a result, the AL approach is perfectly applicable to the nursing and midwifery classroom, and by combining the numerous methods and tools and aligning them with the theory of learning, teachers are capable of bringing the education in the field to a new, modern level.

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McAllister, M., Searl, K., & Davis, S. (2013). Who is that masked educator? Deconstructing the teaching and learning processes of an innovative humanistic simulation technique. Nurse Education Today, 33(12), 1453-1458. Web.

Moore, S. (2012). Is it time to blend student learning? British Journal of Midwifery , 20 (11), 812-816.

Moyer, S. (2016). Large group simulation: Using combined teaching strategies to connect classroom and clinical learning. Teaching and Learning in Nursing, 11 (2), 67-73. Web.

O’Flaherty, J.,A., & Laws, T. A. (2014). Nursing student’s evaluation of a virtual classroom experience in support of their learning bioscience. Nurse Education in Practice, 14 (6), 654-659. Web.

Owen, A. M., & Ward-Smith, P. (2014). Collaborative learning in nursing simulation: Near-peer teaching using standardized patients. Journal of Nursing Education, 53 (3), 170-3. Web.

Park, E. L., & Choi, B. K. (2014). Transformation of classroom spaces: Traditional versus active learning classroom in colleges. Higher Education, 68 (5), 749-771. Web.

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Walters, K. (2014). Sharing classroom research and the scholarship of teaching: Student resistance to active learning may not be as pervasive as is commonly believed. Nursing Education Perspectives, 35 (5), 342-343. Web.

Waltz, C. F., Jenkins, L. S., & Han, N. (2014). The use and effectiveness of active learning methods in nursing and health professions education: A literature review. Nursing Education Perspectives, 35 (6), 392-400. Web.

Welch, S. (2013). Effectiveness of classroom response systems within an active learning environment. Journal of Nursing Education, 52 (11), 653-656. Web.

Wonder, A. H., & Otte, J. L. (2015). Active learning strategies to teach undergraduate nursing statistics: Connecting class and clinical to prepare students for evidence-based practice. Worldviews on Evidence-Based Nursing , 12 (2), 126-127 2p. Web.

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IvyPanda. (2024, April 10). Active Learning in Professional Health Education. https://ivypanda.com/essays/active-learning-in-professional-health-education/

"Active Learning in Professional Health Education." IvyPanda , 10 Apr. 2024, ivypanda.com/essays/active-learning-in-professional-health-education/.

IvyPanda . (2024) 'Active Learning in Professional Health Education'. 10 April.

IvyPanda . 2024. "Active Learning in Professional Health Education." April 10, 2024. https://ivypanda.com/essays/active-learning-in-professional-health-education/.

1. IvyPanda . "Active Learning in Professional Health Education." April 10, 2024. https://ivypanda.com/essays/active-learning-in-professional-health-education/.

Bibliography

IvyPanda . "Active Learning in Professional Health Education." April 10, 2024. https://ivypanda.com/essays/active-learning-in-professional-health-education/.

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Learning Behaviors

This essay about the intricate dynamics of learning behaviors, exploring their role in shaping cognitive evolution and understanding the world. It discusses how learning is a communal process, emphasizing observation and imitation as key components. Furthermore, it highlights the challenges and resilience inherent in the learning process, emphasizing the importance of perseverance and open-mindedness. Ultimately, the essay underscores the significance of learning behaviors in fostering individual growth and collective progress, advocating for a culture of curiosity and exploration to unlock the full potential of the human mind.

How it works

In the vast expanse of the mind, learning behaviors emerge as the silent architects of our cognitive evolution, shaping our understanding of the world and ourselves. It’s akin to a symphony, where each note represents a moment of insight, a spark of knowledge that illuminates the darkness of ignorance. From the delicate dance of neurons in the brain to the intricate web of social interactions, learning behaviors weave a tapestry of experience that defines who we are and who we can become.

At the heart of learning lies the ancient art of adaptation, a process as old as life itself. It’s a dance of survival, where organisms must learn to navigate the ever-changing landscape of their environment, anticipating dangers and seizing opportunities with precision and grace. Whether it’s a predator honing its hunting skills or a prey species mastering the art of evasion, learning behaviors are the key to survival in a world fraught with uncertainty.

But learning is not a solitary endeavor; it’s a communal journey that we undertake together, sharing our knowledge and experiences with one another. Through the power of observation and imitation, we learn from those around us, absorbing their wisdom and integrating it into our own understanding of the world. It’s a process of collective learning, where each individual contributes to the greater pool of knowledge, enriching our shared experience and shaping the course of human history.

Yet, learning is not without its challenges; it requires dedication, perseverance, and an open mind. It’s a journey filled with obstacles and setbacks, where failure is as much a part of the process as success. But it’s through these struggles that we grow and evolve, emerging stronger and wiser than before. It’s a testament to the resilience of the human spirit, a reminder that even in the face of adversity, we have the power to overcome and thrive.

In conclusion, learning behaviors are the cornerstone of our cognitive evolution, shaping our understanding of the world and our place within it. They’re the building blocks of knowledge, the foundation upon which our society is built. By embracing the challenges of learning and fostering a culture of curiosity and exploration, we can unlock the full potential of the human mind and chart a course towards a brighter future.

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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Call for High School Projects

Machine learning for social impact .

The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. 

This year, we invite high school students to submit research papers on the topic of machine learning for social impact.  A subset of finalists will be selected to present their projects virtually and will have their work spotlighted on the NeurIPS homepage.  In addition, the leading authors of up to five winning projects will be invited to attend an award ceremony at NeurIPS 2024 in Vancouver.  

Each submission must describe independent work wholly performed by the high school student authors.  We expect each submission to highlight either demonstrated positive social impact or the potential for positive social impact using machine learning. Application areas may include but are not limited to the following:

• Agriculture
• Climate change
• Homelessness
• Food security
• Mental health
• Water quality

Authors will be asked to confirm that their submissions accord with the NeurIPS code of conduct and the NeurIPS code of ethics .

Submission deadline: All submissions must be made by June 27th, 4pm EDT. The system will close after this time, and no further submissions will be possible.

We are using OpenReview to manage submissions. Papers should be submitted here . Submission will open June 1st.  Submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. 

Formatting instructions:   All submissions must be in PDF format. Submissions are limited to four content pages , including all figures and tables; additional pages containing only references are allowed. You must format your submission using the NeurIPS 2024 LaTeX style file using the “preprint” option for non-anonymous submission. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review.  Papers may be rejected without consideration of their merits if they fail to meet the submission requirements, as described in this document. 

Mentorship and collaboration:  The submitted research can be a component of a larger research endeavor involving external collaborators, but the submission should describe only the authors’ contributions.  The authors can also have external mentors but must disclose the nature of the mentorship.  At the time of submission, the authors will be asked to describe the involvement of any mentors or external collaborators and to distinguish mentor and collaborator contributions from those of the authors.  In addition, the authors may (optionally) to include an acknowledgements section acknowledging the contributions of others following the content sections of the submission. The acknowledgements section will not count toward the submission page limit.

Proof of high school attendance: Submitting authors will also be asked to upload a signed letter, on school letterhead, from each author’s high school confirming that the author was enrolled in high school during the 2023-2024 academic year.

Supplementary artifacts:  In their submission, authors may link to supplementary artifacts including videos, working demonstrations, digital posters, websites, or source code.  Please do not link to additional text.  All such supplementary material should be wholly created by the authors and should directly support the submission content. 

Review process:   Each submission will be reviewed by anonymous referees.  The authors, however, should not be anonymous.  No written feedback will be provided to the authors.  

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology.  Any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, or proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

Dual submissions:  Submissions that are substantially similar to papers that the authors have previously published or submitted in parallel to other peer-reviewed venues with proceedings or journals may not be submitted to NeurIPS. Papers previously presented at workshops or science fairs are permitted, so long as they did not appear in a conference proceedings (e.g., CVPRW proceedings), a journal, or a book.  However, submissions will not be published in formal proceedings, so work submitted to this call may be published elsewhere in the future. Plagiarism is prohibited by the NeurIPS Code of Conduct .

Paper checklist: In order to improve the rigor and transparency of research submitted to and published at NeurIPS, authors are required to complete a paper checklist . The paper checklist is intended to help authors reflect on a wide variety of issues relating to responsible machine learning research, including reproducibility, transparency, research ethics, and societal impact. The checklist does not count towards the page limit and will be entered in OpenReview.

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In Defense of Never Learning How to Cook

I hated domesticity so much that for years, I lived happily without a kitchen. This $19 device helped me survive.

By Iva Dixit

Iva Dixit is a staff editor for the magazine.

I found it while walking through the home-goods section of T.J. Maxx, the American retail equivalent of the Garden of Earthly Delights, at 8:00 on a Tuesday night in 2015. It was two days after Easter, and in this Hieronymus Bosch land of shopping anarchy, the shelves were stocked with pastel-colored objects of uncertain usefulness: sacks of fruit-medley popcorn dyed green and purple; a giant tub of millennial-pink Himalayan crystal salt. Somewhere among these novelties I spotted a carelessly abandoned gadget calling itself the Dash Rapid Egg Cooker. The cashier who rang me up did not share my enthusiasm for the cheery cockiness of its packaging, which proclaimed that it “Perfectly Cooks 6 Eggs at a Time!” Baffled, she asked me a question, the answer to which would have embarrassed anyone but me: “Don’t you know how to boil water?”

No. I didn’t.

And at 22, not only did I not know how to boil water, I didn’t even know how to turn on a stove. Now, these may both seem like gaps in knowledge that could have been easily rectified with a 60-second trip to the kitchen, but you see: I did not have one.

Earlier that day, I had finally moved into my first solo “apartment”: the garden-level basement of a Manhattan brownstone that was rented to me by an absentee owner, which, in lieu of a real kitchen, came outfitted with a minifridge, a hot plate and a microwave. That evening, after a long day of unpacking, I sat down on the building’s stoop, ate my way through a bag of discounted Cadbury Mini Eggs and, after 20 minutes spent wallowing in disbelief at where life had deposited me, broke into a series of earthquake-size sobs. But it wasn’t misery making me dry-heave — it was relief.

At 22, not only did I not know how to boil water, I didn’t even know how to turn on a stove.

In 2013, I fled my old life for New York, the promised land for stunted young adults evading responsibility. I had spent my childhood, teenhood and earliest adulthood consumed with daydreams of an imaginary future in which I lived alone — my only ambition in life. In these painstakingly detailed fantasies, the greatest luxury I could imagine was that my space and my empty hours all belonged to me and me only. In these visions, there was no one snatching “storybooks” (the beloved Indian-parent euphemism even if you read adult fiction) from my hands and barking at me to get up and make tea whenever guests came to visit, or grating at me to bring out hot rotis straight from the stove and put them onto the plates of fathers and uncles. The milieu I was raised in tried to drill into me the idea that keeping a home, and the domestic labor it entails — the cooking, the serving, the dusting, the wiping — were acts of profound nobility. That they were crucial to the formation of the only life I was predestined for, one that came prepackaged with a husband and children, two species, I had been warned, that were equally incapable of feeding themselves, and whose supervision would fall to me.

In rebellion, I refused to learn even a single tenet of good housekeeping. If I remained useless in the kitchen and egregiously incompetent at household chores, then I could at least retain some control over my life — and no amount of yelling, berating or shaming from parents, elders or concerned strangers could sway me from this zealotry.

At no point during this teenage mutiny, however, had I considered what I would do if these prolonged daydreams were ever granted. It escaped me that actually living alone as an adult involves being in possession of some basic skills I had avoided acquiring. Yes, now I was finally king of my kitchenless fief. But what was I going to eat? Cinnamon Toast Crunch and rubbery takeout every day, for eternity? That night I paid the skeptical cashier $19 for the spaceship-shaped device and took it home, feeling the first cracks of doubt emerging in my lifelong belligerence toward domesticity.

It escaped me that actually living alone as an adult involves being in possession of some basic skills I had avoided acquiring.

The Dash Rapid Egg Cooker is exactly what the name declares, a device that has precisely one purpose: It cooks eggs, rapidly. In the rare case of reality’s matching up with an advertising slogan, they are indeed perfect. I followed the instructions, starting by placing just one egg and pouring in the few centimeters of water it needed to cook. Through some magic of steam and electrical engineering, the Dash magically conjured an egg of ideal consistency in less time than it took me to brush my teeth, wash my face and apply my acne cream (I did thankfully have a bathroom).

As French chefs and inept bachelors of various nationalities can attest, mastering a perfect egg is the gateway to mastering a cuisine altogether. A good egg is breakfast, lunch, dinner and all snacks in between. A good egg is the foundation of bigger cooking ambitions, now that you have mastered the trickiest basic of them all. A good egg is the start of complete self-sufficiency, because it is a meal and an accompaniment all in itself. On that April night nine years ago, giddy and drunk on my own invincibility, I ate the first thing I had ever “cooked” by myself, for myself: a half-boiled egg, sliced neatly in half on top of plain supermarket white bread that I lathered with cold scrapes of salted butter and thin slivers of red onion .

Until then, mine was a life that often felt cobbled together from accidents and gambles. That immaculate half-boiled egg, with its semi-liquefied insides roiling on my tongue, was the first thing I felt I’d actually earned on my own. I still didn’t know how to boil water. I had a job that paid me the queenly sum of $30,000 per year, yet it was still more money than I’d ever conceived of.

More important, I finally — finally — had the only thing I ever really wanted: my independence, my time.

Iva Dixit is a staff editor at the magazine. She has previously written about the joys of eating raw onions , the evergreen popularity of Sean Paul and why “Oppenheimer” is for the girlies .

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