metacognitive strategies research paper

Change Password

Your password must have 8 characters or more and contain 3 of the following:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

• Sign in / Register

Request Username

Can't sign in? Forgot your username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

Fostering Metacognition to Support Student Learning and Performance

• Julie Dangremond Stanton
• Amanda J. Sebesta
• John Dunlosky

*Address correspondence to: Julie Dangremond Stanton ( E-mail Address: [email protected] ).

Department of Cellular Biology, University of Georgia, Athens, GA 30602

Search for more papers by this author

Department of Biology, Saint Louis University, St. Louis, MO 63103

Department of Psychological Sciences, Kent State University, Kent, OH 44240

Metacognition is awareness and control of thinking for learning. Strong metacognitive skills have the power to impact student learning and performance. While metacognition can develop over time with practice, many students struggle to meaningfully engage in metacognitive processes. In an evidence-based teaching guide associated with this paper ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition ), we outline the reasons metacognition is critical for learning and summarize relevant research on this topic. We focus on three main areas in which faculty can foster students’ metacognition: supporting student learning strategies (i.e., study skills), encouraging monitoring and control of learning, and promoting social metacognition during group work. We distill insights from key papers into general recommendations for instruction, as well as a special list of four recommendations that instructors can implement in any course. We encourage both instructors and researchers to target metacognition to help students improve their learning and performance.

INTRODUCTION

Supporting the development of metacognition is a powerful way to promote student success in college. Students with strong metacognitive skills are positioned to learn more and perform better than peers who are still developing their metacognition (e.g., Wang et al. , 1990 ). Students with well-developed metacognition can identify concepts they do not understand and select appropriate strategies for learning those concepts. They know how to implement strategies they have selected and carry out their overall study plans. They can evaluate their strategies and adjust their plans based on outcomes. Metacognition allows students to be more expert-like in their thinking and more effective and efficient in their learning. While collaborating in small groups, students can also stimulate metacognition in one another, leading to improved outcomes. Ever since metacognition was first described ( Flavell, 1979 ), enthusiasm for its potential impact on student learning has remained high. In fact, as of today, the most highly cited paper in CBE—Life Sciences Education is an essay on “Promoting Student Metacognition” ( Tanner, 2012 ).

Despite this enthusiasm, instructors face several challenges when attempting to harness metacognition to improve their students’ learning and performance. First, metacognition is a term that has been used so broadly that its meaning may not be clear ( Veenman et al. , 2006 ). We define metacognition as awareness and control of thinking for learning ( Cross and Paris, 1988 ). Metacognition includes metacognitive knowledge , which is your awareness of your own thinking and approaches for learning. Metacognition also includes metacognitive regulation , which is how you control your thinking for learning ( Figure 1 ). Second, metacognition includes multiple processes and skills that are named and emphasized differently in the literature from various disciplines. Yet upon examination, the metacognitive processes and skills from different fields are closely related, and they often overlap (see Supplemental Figure 1). Third, metacognition consists of a person’s thoughts, which may be challenging for that person to describe. The tacit nature of metacognitive processes makes it difficult for instructors to observe metacognition in their students, and it also makes metacognition difficult for researchers to measure. As a result, classroom intervention studies of metacognition—those that are necessary for making the most confident recommendations for promoting student metacognition—have lagged behind foundational and laboratory research on metacognitive processes and skills.

FIGURE 1. Metacognition framework commonly used in biology education research (modified from Schraw and Moshman, 1995 ). This theoretical framework divides metacognition into two components: metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes what you know about your own thinking and what you know about strategies for learning. Declarative knowledge involves knowing about yourself as a learner, the demands of the task, and what learning strategies exist. Procedural knowledge involves knowing how to use learning strategies. Conditional knowledge involves knowing when and why to use particular learning strategies. Metacognitive regulation involves the actions you take in order to learn. Planning involves deciding what strategies to use for a future learning task and when you will use them. Monitoring involves assessing your understanding of concepts and the effectiveness of your strategies while learning. Evaluating involves appraising your prior plan and adjusting it for future learning.

How do undergraduate students develop metacognitive skills?

To what extent do active learning and generative work 1 promote metacognition?

To what extent do increases in metacognition correspond to increases in achievement in science courses?

FIGURE 2. (A) Landing page for the Student Metacognition guide. The landing page provides a map with sections an instructor can click on to learn more about how to support students’ metacognition. (B) Example paper summary showing instructor recommendations. At the end of each summary in our guide, we used italicized text to point out what instructors should know based on the paper’s results.

The organization of this essay reflects the organization of our evidence-based teaching guide. In the guide, we first define terms and provide important background from papers that highlight the underpinnings and benefits of metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/benefits-definitions-underpinnings ). We then explore metacognition research by summarizing both classic and recent papers in the field and providing links for readers who want to examine the original studies. We consider three main areas related to metacognition: 1) student strategies for learning, 2) monitoring and control of learning, and 3) social metacognition during group work.

SUPPORTING STUDENTS TO USE EFFECTIVE LEARNING STRATEGIES

What strategies do students use for learning.

First our teaching guide examines metacognition in the context of independent study ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/supporting-student
-learning-strategies ). When students transition to college, they have increased responsibility for directing their learning, which includes making important decisions about how and when to study. Students rely on their metacognition to make those decisions, and they also use metacognitive processes and skills while studying on their own. Empirical work has confirmed what instructors observe about their own students’ studying—many students rely on passive strategies for learning. Students focus on reviewing material as it is written or presented, as opposed to connecting concepts and synthesizing information to make meaning. Some students use approaches that engage their metacognition, but they often do so without a full understanding of the benefits of these approaches ( Karpicke et al. , 2009 ). Students also tend to study based on exam dates and deadlines, rather than planning out when to study ( Hartwig and Dunlosky, 2012 ). As a result, they tend to cram, which is also known in the literature as massing their study. Students continue to cram because this approach is often effective for boosting short-term performance, although it does not promote long-term retention of information.

Which Strategies Should Students Use for Learning?

Here, we make recommendations about what students should do to learn, as opposed to what they typically do. In our teaching guide, we highlight three of the most effective strategies for learning: 1) self-testing, 2) spacing, and 3) interleaving ( https://lse.ascb.org/evidence-based-teaching-guides/student
-metacognition/supporting-student-learning-strategies/
#whatstudentsshould ). These strategies are not yet part of many students’ metacognitive knowledge, but they should know about them and be encouraged to use them while metacognitively regulating their learning. Students self-test when they use flash cards and answer practice questions in an attempt to recall information. Self-testing provides students with opportunities to monitor their understanding of material and identify gaps in their understanding. Self-testing also allows students to activate relevant knowledge and encode prompted information so it can be more easily accessed from their memory in the future ( Dunlosky et al. , 2013 ).

Students space their studying when they spread their learning of the same material over multiple sessions. This approach requires students to intentionally plan their learning instead of focusing only on what is “due” next. Spacing can be combined with retrieval practice , which involves recalling information from memory. For example, self-testing is a form of retrieval practice. Retrieval practice with spacing encourages students to actively recall the same content across several study sessions, which is essential for consolidating information from prior study periods ( Dunlosky et al. , 2013 ). Importantly, when students spread their learning over multiple sessions, they are less susceptible to superficial familiarity with concepts, which can mislead them into thinking they have learned concepts based on recognition alone ( Kornell and Bjork, 2008 ).

Students interleave when they alternate studying of information from one category with studying of information from another category. For example, when students learn categories of amino acid side groups, they should alternate studying nonpolar amino acids with polar amino acids. This allows students to discriminate across categories, which is often critical for correctly solving problems ( Rohrer et al. , 2020 ). Interleaving between categories also supports student learning because it usually results in spacing of study.

How are students enacting specific learning strategies, and do different students enact them in different ways?

To what extent do self-testing, spacing, and interleaving support achievement in the context of undergraduate science courses?

What can instructors do to increase students’ use of effective learning strategies?

What Factors Affect the Strategies Students Should Use to Learn?

Next, we examined the factors that affect what students should do to learn. Although we recommend three well-established strategies for learning, other appropriate strategies can vary based on the learning context. For example, the nature of the material, the type of assessment, the learning objectives, and the instructional methods can render some strategies more effective than others ( Scouller, 1998 ; Sebesta and Bray Speth, 2017 ). Strategies for learning can be characterized as deep if they involve extending and connecting ideas or applying knowledge and skills in new ways ( Baeten et al. , 2010 ). Strategies can be characterized as surface if they involve recalling and reproducing content. While surface strategies are often viewed negatively, there are times when these approaches can be effective for learning ( Hattie and Donoghue, 2016 ). For example, when students have not yet gained background knowledge in an area, they can use surface strategies to acquire the necessary background knowledge. They can then incorporate deep strategies to extend, connect, and apply this knowledge. Importantly, surface and deep strategies can be used simultaneously for effective learning. The use of surface and deep strategies ultimately depends on what students are expected to know and be able to do, and these expectations are set by instructors. Openly discussing these expectations with students can enable them to more readily select effective strategies for learning.

What Challenges Do Students Face in Using Their Metacognition to Enact Effective Strategies?

How can students address challenges they will face when using effective—but effortful—strategies for learning?

What approaches can instructors take to help students overcome these challenges?

ENCOURAGING STUDENTS TO MONITOR AND CONTROL THEIR LEARNING FOR EXAMS

Metacognition can be investigated in the context of any learning task, but in the sciences, metacognitive processes and skills are most often investigated in the context of high-stakes exams. Because exams are a form of assessment common to nearly every science course, in the next part of our teaching guide, we summarized some of the vast research focused on monitoring and control before, during, and after an exam ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/encouraging-students-monitor-control-learning ). In the following section, we demonstrate the kinds of monitoring and control decisions learners make by using an example of introductory biology students studying for an exam on cell division. The students’ instructor has explained that the exam will focus on the stages of mitosis and cytokinesis, and the exam will include both multiple-choice and short-answer questions.

How Should Students Use Metacognition while Preparing for and Taking an Exam?

As students prepare for an exam, they can use metacognition to inform their learning. Students can consider how they will be tested, set goals for their learning, and make a plan to meet their goals. It is expected that students who set specific goals while planning for an exam will be more effective in their studying than students who do not make specific goals. For example, a student who sets a specific goal to identify areas of confusion each week by answering end-of-chapter questions each weekend is expected to do better than a student who sets a more general goal of staying up-to-date on the material. Although some studies include goal setting and planning as one of many metacognitive strategies introduced to students, the influence of task-specific goal setting on academic achievement has not been well studied on its own in the context of science courses.

As students study, it is critical that they monitor both their use of learning strategies and their understanding of concepts. Yet many students struggle to accurately monitor their own understanding ( de Carvalho Filho, 2009 ). In the example we are considering, students may believe they have already learned mitosis because they recognize the terms “prophase,” “metaphase,” “anaphase,” and “telophase” from high school biology. When students read about mitosis in the textbook, processes involving the mitotic spindle may seem familiar because of their exposure to these concepts in class. As a result, students may inaccurately predict that they will perform well on exam questions focused on the mitotic spindle, and their overconfidence may cause them to stop studying the mitotic spindle and related processes ( Thiede et al. , 2003 ). Students often rate their confidence in their learning based on their ability to recognize, rather than recall, concepts.

Instead of focusing on familiarity, students should rate their confidence based on how well they can retrieve relevant information to correctly answer questions. Opportunities for practicing retrieval, such as self-testing, can improve monitoring accuracy. Instructors can help students monitor their understanding more accurately by encouraging students to complete practice exams and giving students feedback on their answers, perhaps in the form of a key or a class discussion ( Rawson and Dunlosky, 2007 ). Returning to the example, if students find they can easily recall the information needed to correctly answer questions about cytokinesis, they may wisely decide to spend their study time on other concepts. In contrast, if students struggle to remember information needed to answer questions about the mitotic spindle, and they answer these questions incorrectly, then they can use this feedback to direct their efforts toward mastering the structure and function of the mitotic spindle.

While taking a high-stakes exam, students can again monitor their performance on a single question, a set of questions, or an entire exam. Their monitoring informs whether they change an answer, with students tending to change answers they judge as incorrect. Accordingly, the accuracy of their monitoring will influence whether their changes result in increased performance ( Koriat and Goldsmith, 1996 ). In some studies, changing answers on an exam has been shown to increase student performance, in contrast to the common belief that a student’s first answer is usually right ( Stylianou-Georgiou and Papanastasiou, 2017 ). Changing answers on an exam can be beneficial if students return to questions they had low confidence in answering and make a judgment on their answers based on the ability to retrieve the information from memory, rather than a sense of familiarity with the concepts. Two important open questions are:

What techniques can students use to improve the accuracy of their monitoring, while preparing for an exam and while taking an exam?

How often do students monitor their understanding when studying on their own?

How Should Students Use Metacognition after Taking an Exam?

How do students develop metacognitive regulation skills such as evaluation?

To what extent does the ability to evaluate affect student learning and performance?

When students evaluate the outcome of their studying and believe their preparation was lacking, to what degree do they adopt more effective strategies for the next exam?

PROMOTING SOCIAL METACOGNITION DURING GROUP WORK

Next, our teaching guide covers a relatively new area of inquiry in the field of metacognition called social metacognition , which is also known as socially shared metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student
-metacognition/promoting-social-metacognition
-group-work ). Science students are expected to learn not only on their own, but also in the context of small groups. Understanding social metacognition is important because it can support effective student learning during collaborations both inside and outside the classroom. While individual metacognition involves awareness and control of one’s own thinking, social metacognition involves awareness and control of others’ thinking. For example, social metacognition happens when students share ideas with peers, invite peers to evaluate their ideas, and evaluate ideas shared by peers ( Goos et al. , 2002 ). Students also use social metacognition when they assess, modify, and enact one another’s strategies for solving problems ( Van De Bogart et al. , 2017 ). While enacting problem-solving strategies, students can evaluate their peers’ hypotheses, predictions, explanations, and interpretations. Importantly, metacognition and social metacognition are expected to positively affect one another ( Chiu and Kuo, 2009 ).

How do social metacognition and individual metacognition affect one another?

How can science instructors help students to effectively use social metacognition during group work?

CONCLUSIONS

We encourage instructors to support students’ success by helping them develop their metacognition. Our teaching guide ends with an Instructor Checklist of actions instructors can take to include opportunities for metacognitive practice in their courses ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Student-Metacognition-Instructor-Checklist.pdf ). We also provide a list of the most promising approaches instructors can take, called Four Strategies to Implement in Any Course ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Four
-Strategies-to-Foster-Student-Metacognition.pdf ). We not only encourage instructors to consider using these strategies, but given that more evidence for their efficacy is needed from classroom investigations, we also encourage instructors to evaluate and report how well these strategies are improving their students’ achievement. By exploring and supporting students’ metacognitive development, we can help them learn more and perform better in our courses, which will enable them to develop into lifelong learners.

1 Generative work “involves students working individually or collaboratively to generate ideas and products that go beyond what has been presented to them” ( Andrews et al. , 2019 , p2). Generative work is often stimulated by active-learning approaches.

ACKNOWLEDGMENTS

We are grateful to Cynthia Brame, Kristy Wilson, and Adele Wolfson for their insightful feedback on this paper and the guide. This material is based upon work supported in part by the National Science Foundation under grant number 1942318 (to J.D.S.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

• Andrews, T. C., Auerbach, A. J. J., & Grant, E. F. ( 2019 ). Exploring the relationship between teacher knowledge and active-learning implementation in large college biology courses . CBE—Life Sciences Education , 18 (4), ar48. Link ,  Google Scholar
• Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. ( 2010 ). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness . Educational Research Review , 5 (3), 243–260. Google Scholar
• Carr, M., & Taasoobshirazi, G. ( 2008 ). Metacognition in the gifted: Connections to expertise . In Shaughnessy, M. F.Veenman, M.Kleyn-Kennedy, C. (Eds.), Meta-cognition: A recent review of research, theory and perspectives (pp. 109–125). Hauppauge, NY: Nova Sciences Publishers. Google Scholar
• Choi, M. M., & Kuo, S. W. ( 2009 ). Social metacognition in groups: Benefits, difficulties, learning and teaching . In Larson, C. B. (Ed.), Metacognition: New research developments (pp. 117–136). Hauppauge, NY: Nova Science Publishers. Google Scholar
• Cross, D. R., & Paris, S. G. ( 1988 ). Developmental and instructional analyses of children’s metacognition and reading comprehension . Journal of Educational Psychology , 80 (2), 131–142. Google Scholar
• de Carvalho Filho, M. K. ( 2009 ). Confidence judgments in real classroom settings: Monitoring performance in different types of tests . International Journal of Psychology , 44 (2), 93–108. Medline ,  Google Scholar
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. ( 2013 ). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology . Psychological Science in the Public Interest , 14 (1), 4–58. Medline ,  Google Scholar
• Dye, K. M., & Stanton, J. D. ( 2017 ). Metacognition in upper-division biology students: Awareness does not always lead to control . CBE—Life Sciences Education , 16 (2), ar31. Link ,  Google Scholar
• Flavell, J. H. ( 1979 ). Meta-cognition and cognitive monitoring—New area of cognitive-developmental inquiry . American Psychologist , 34 (10), 906–911. Google Scholar
• Goos, M., Galbraith, P., & Renshaw, P. ( 2002 ). Socially mediated metacognition: Creating collaborative zones of proximal development in small group problem solving . Educational Studies in Mathematics , 49 (2), 193–223. Google Scholar
• Hartwig, M. K., & Dunlosky, J. ( 2012 ). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin & Review , 19 (1), 126–134. Medline ,  Google Scholar
• Hattie, J. A., & Donoghue, G. M. ( 2016 ). Learning strategies: A synthesis and conceptual model . Nature Partner Journal Science of Learning , 1 (1), 1–13. Google Scholar
• Karpicke, J. D., Butler, A. C., & Roediger, H. L. , III . ( 2009 ). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory , 17 (4), 471–479. Medline ,  Google Scholar
• Koriat, A., & Bjork, R. A. ( 2005 ). Illusions of competence in monitoring one’s knowledge during study . Journal of Experimental Psychology: Learning, Memory, Cognition , 31 (2), 187–194. Medline ,  Google Scholar
• Koriat, A., & Goldsmith, M. ( 1996 ). Monitoring and control processes in the strategic regulation of memory accuracy . Psychological Review , 103 (3), 490–517. Medline ,  Google Scholar
• Kornell, N., & Bjork, R. A. ( 2008 ). Learning concepts and categories is spacing the “enemy of induction”? Psychological Science , 19 (6), 585–592. Medline ,  Google Scholar
• Kuhbandner, C., & Emmerdinger, K. J. ( 2019 ). Do students really prefer repeated rereading over testing when studying textbooks? A reexamination . Memory , 27 (7), 952–961. Medline ,  Google Scholar
• McGuire, S. Y. ( 2006 ). The impact of supplemental instruction on teaching students how to learn . New Directions for Teaching and Learning , 2006 (106), 3–10. Google Scholar
• Miller, M., & Hadwin, A. F. ( 2015 ). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL . Computers in Human Behavior , 52 , 573–588. Google Scholar
• Nelson, T., & Narens, L. ( 1990 ). Metamemory: A theoretical framework and new findings . Psychology of Learning and Motivation , 26 , 125–141. Google Scholar
• Rawson, K. A., & Dunlosky, J. ( 2007 ). Improving students’ self-evaluation of learning for key concepts in textbook materials . European Journal of Cognitive Psychology , 19 (4–5), 559–579. Google Scholar
• Roediger, H. L., III, & Karpicke, J. D. ( 2006 ). Test-enhanced learning: Taking memory tests improves long-term retention . Psychological Science , 17 (3), 249–255. Medline ,  Google Scholar
• Rohrer, D., Dedrick, R. F., Hartwig, M. K., & Cheung, C.-N. ( 2020 ). A randomized controlled trial of interleaved mathematics practice . Journal of Educational Psychology , 112 (1), 40. Google Scholar
• Sabel, J. L., Dauer, J. T., & Forbes, C. T. ( 2017 ). Introductory biology students’ use of enhanced answer keys and reflection questions to engage in metacognition and enhance understanding . CBE—Life Sciences Education , 16 (3), ar40. Link ,  Google Scholar
• Schraw, G., & Moshman, D. ( 1995 ). Metacognitive theories . Educational Psychology Review , 7 (4), 351–371. Google Scholar
• Scouller, K. ( 1998 ). The influence of assessment method on students’ learning approaches: Multiple choice question examination versus assignment essay . Higher Education , 35 (4), 453–472. Google Scholar
• Sebesta, A. J., & Bray Speth, E. ( 2017 ). How should I study for the exam? Self-regulated learning strategies and achievement in introductory biology . CBE—Life Sciences Education , 16 (2), ar30. Link ,  Google Scholar
• Stanton, J. D., Dye, K. M., & Johnson, M. S. ( 2019 ). Knowledge of learning makes a difference: A comparison of metacognition in introductory and senior-level biology students . CBE—Life Sciences Education , 18 (2), ar24. Link ,  Google Scholar
• Stanton, J. D., Neider, X. N., Gallegos, I. J., & Clark, N. C. ( 2015 ). Differences in metacognitive regulation in introductory biology students: When prompts are not enough . CBE—Life Sciences Education , 14 (2), ar15. Link ,  Google Scholar
• Stylianou-Georgiou, A., & Papanastasiou, E. C. ( 2017 ). Answer changing in testing situations: The role of metacognition in deciding which answers to review . Educational Research and Evaluation , 23 (3–4), 102–118. Google Scholar
• Tanner, K. D. ( 2012 ). Promoting student metacognition . CBE—Life Sciences Education , 11 (2), 113–120. Link ,  Google Scholar
• Thiede, K. W., Anderson, M., & Therriault, D. ( 2003 ). Accuracy of metacognitive monitoring affects learning of texts . Journal of Educational Psychology , 95 (1), 66–73. Google Scholar
• Van De Bogart, K. L., Dounas-Frazer, D. R., Lewandowski, H., & Stetzer, M. R. ( 2017 ). Investigating the role of socially mediated metacognition during collaborative troubleshooting of electric circuits . Physical Review Physics Education Research , 13 (2), 020116. Google Scholar
• Veenman, M. V. J., van Hout-Wolters, B. H., & Afflerbach, P. ( 2006 ). Metacognition and learning: Conceptual and methodological considerations . Metacognition and Learning , 1 (1), 3–14. Google Scholar
• Wang, M. C., Haertel, G. D., & Walberg, H. J. ( 1990 ). What influences learning? A content analysis of review literature . Journal of Educational Research , 84 (1), 30–43. Google Scholar
• Stephanie M. Halmo ,
• Kira A. Yamini , and
• Elisabeth Schussler, Monitoring Editor
• Stephanie M. Gardner ,
• Aakanksha Angra , and
• Joseph A. Harsh
• Kristy J Wilson, Monitoring Editor
• Diane K. Angell ,
• Sharon Lane-Getaz ,
• Taylor Okonek , and
• Stephanie Smith
• Jennifer Knight, Monitoring Editor
• Jennifer K. Knight ,
• Daniel C. Weaver ,
• Melanie E. Peffer , and
• Zachary S. Hazlett
• Tessa C. Andrews, Monitoring Editor
• Emily K. Bremers ,
• Sammantha Fuller , and
• Stephanie Gardner, Monitoring Editor
• Undergraduates' Experiences with Online and in-Person Courses Provide Opportunities for Improving Student-Centered Biology Laboratory Instruction Journal of Microbiology & Biology Education, Vol. 23, No. 1
• Student attitudes in an innovative active learning approach in calculus 4 February 2022 | International Journal of Mathematical Education in Science and Technology, Vol. 5
• Lauren Hensley ,
• Amy Kulesza ,
• Joshua Peri ,
• Anna C. Brady ,
• Christopher A. Wolters ,
• David Sovic , and
• Caroline Breitenberger
• Joel K. Abraham, Monitoring Editor
• Persistent Miscalibration for Low and High Achievers despite Practice Test Feedback in an Introductory Biology Course Journal of Microbiology & Biology Education, Vol. 22, No. 2

© 2021 J. D. Stanton et al. CBE—Life Sciences Education © 2021 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 10 July 2023

Metacognitive reading strategies and its relationship with Filipino high school students’ reading proficiency: insights from the PISA 2018 data

• Allan B. I. Bernardo   ORCID: orcid.org/0000-0003-3938-266X 1 &
• Ma. Joahna Mante-Estacio   ORCID: orcid.org/0000-0002-5394-1475 1  

Humanities and Social Sciences Communications volume  10 , Article number:  400 ( 2023 ) Cite this article

5289 Accesses

3 Altmetric

Metrics details

Learners’ metacognitive reading strategies support their attempts to draw meaning from texts and to overcome comprehension difficulties. For second language readers, such strategies may compensate for lack of language proficiency while reading. Taking a sample from a country that ranked last in the PISA 2018 reading assessment, this study aims to investigate potential discrepancies in how students evaluate the usefulness of specific reading strategies and how these conceptions are associated with related to the students’ reading proficiency. We explored the association between metacognitive reading strategies with reading proficiency by analysing data from a nationally representative sample of 15-year-old students who participated in the PISA 2018 ( N  = 6591). Awareness of different reading strategies was compared using repeated measures ANOVA; relationships with reading proficiency were examined using regression analysis. Self-reports on metacognitive reading strategies accounted for a significant portion of the variation in Filipino students’ English reading proficiency, after controlling for SES, sex, and number of books at home. The reading strategies perceived as most useful were not the most strongly associated with reading proficiency, suggesting that students may not be aware of which reading strategies are helpful in learning to read in English. The results indicate variations in the students’ awareness of which strategies aid in their reading comprehension and point to the need to better understand how effective reading strategy instruction is taught to and is engaged by Filipino students in their reading classes.

Similar content being viewed by others

A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions

Impact of artificial intelligence on human loss in decision making, laziness and safety in education

Sleep quality, duration, and consistency are associated with better academic performance in college students

Introduction.

In 2018, Filipino students participated in the OECD’s Programme for International Student Assessment (PISA) for the first time, and the results revealed that Filipino students ranked last among 79 countries/economies in the domain of reading (Organisation for Economic Co-operation and Development 2019b ). Around 80% of the Filipino students who participated in the assessment failed to meet the minimum reading proficiency level. A few studies (e.g., Bernardo 2023 ; Bernardo et al. 2021 ; Haw et al. 2021 ) have attempted to explore the factors that may be related to variations in Filipino students reading proficiency in PISA, and in this study, we focus on one factor, metacognitive awareness of reading strategies, which has been associated with reading proficiency of readers in different parts of the world (Alkhateeb et al. 2021 ; Pinninti 2016 ; Sheikh et al. 2019 ). We inquire into metacognitive strategies considering that the Filipino students were tested on their reading proficiency in English, the official medium of instruction for most high school subjects in Philippine schools, but a language that was not used at the homes of 94% of the students. Researchers have noted how reading strategies play a very important role in learning to read in a second language or foreign language (Chen and Chen 2015 ; Friesen and Haigh 2018 ); in particular, metacognitive strategies help students attain better reading proficiency even as they have low language proficiency (Kolić-Vehovec and Bajšanski 2007 ). In this study, we use data from the PISA 2018 database drawn from a nationally representative sample of 15-year-old Filipino students to explore how awareness of 11 different reading strategies relate to Filipino students’ reading proficiency in English.

Reading strategies of second language learners

Among the variables that affect the process of reading in the second language, reading strategy use is one of the most studied (Chen and Chen 2015 ; Friesen and Haigh 2018 ). Second language and foreign language readers need to be proficient in the use of reading strategies to be able to understand a text (Hong-Nam and Page 2014 ; Schiff and Calif 2004 ; Sheorey and Mokhtari 2001 ; Zhang et al. 2014 ). Reading strategies are actions undertaken by readers to support their comprehension and attempts to draw meaning from texts (Garner 1987 ; Yoshikawa and Leung 2020 ). Reading strategies also involve readers’ perceptions of the reading tasks and how they respond to difficulties encountered while reading (Singhal 2001 ), as the students’ reading strategies indicate how they are attempting to overcome their comprehension difficulties (Tercanlioglu 2004 ). Without the use of the appropriate reading strategies, comprehension difficulties are likely to arise among second or foreign language readers, and the difficulties might result in detachment from reading activities (Kasemsap and Lee 2015 ). On the other hand, the use of appropriate reading strategies can compensate for second language readers’ lack of language proficiency while engaged in reading tasks (Carell 1989 ; Kolić-Vehovec and Bajšanski 2007 ).

Reading strategies have been classified by experts as low-level and high-level strategies, and both types need to be activated and coordinated by a reader throughout the reading process (Grabe Stoller 2013 ). Low-level strategies refer to the basic strategies for literal interpretation of texts that include skimming, underlining, and rereading. On the other hand, high-level strategies are those essential to be able to regulate and monitor one’s understanding of a text like interpreting, summarizing, and evaluating the text. In the case of second and foreign language readers who are not yet skilled in the target language, many are forced to use low-level reading strategies which reduces their employment of the higher-level strategies (Zhang 2001 ).

The importance of reading strategies does not simply depend on the quantity (range and frequency) of strategies. While studies show that learners with higher reading proficiency tend to use more strategies (Sheorey and Mokhtari 2001 ), some non-proficient readers also use many strategies (Hong-Nam and Page 2014 ). Instead, awareness and appropriate use of reading strategies seems more important in predicting reading proficiency (Hong-Nam and Page 2014 ), which has shifted the focus on learners’ metacognitive reading strategies (Fitrisia et al. 2015 ; Hong-Nam and Page 2014 ). Metacognitive reading strategies refers to learners’ knowledge of their reading processes, and in particular, the self-controlled techniques they use while monitoring their reading comprehension (Ahmadi et al. 2013 ; Mokhtari and Reichard 2002 ). Research has consistently shown that metacognitive reading strategies differentiates highly proficient readers from less skilled ones (Mohseni et al. 2020 ; Pinninti 2016 ; Sheikh et al. 2019 ) particularly among second language readers (Meniado 2016 ; Sheorey and Mokhtari 2001 ; Singhal 2001 ; Tavakoli 2014 ). Second language learners who know and understand their strengths and weaknesses and who know which controlled learning strategies work for them are better able to overcome the difficulties they encounter in second language reading tasks.

PISA cognitive framework for reading

The PISA 2018 assessment framework also underscored the importance of metacognitive research strategies in the overall assessment of reading proficiency (Organisation for Economic Co-operation and Development 2019a ). The PISA 2018 framework for reading proficiency features a “typology of cognitive processes involved in purposeful reading activities as they unfold in single or multiple text environments” (Organisation for Economic Co-operation and Development 2019a , p. 36). Three categories of cognitive processes are defined with specific subprocesses specified in each category: (a) locate information (access and retrieve information within a text, search for and select relevant text), (b) understand (represent literal meaning, integrate and generate inferences), and (c) evaluate and reflect (assess quality and credibility, reflect on content and form, detect and handle conflict). But in addition to the cognitive processes associated with text processing, the PISA reading framework also emphasizes the goal-driven and intertextual nature of reading proficiency (McCrudden and Schraw 2007 ; White et al. 2010 ). As such, the framework also highlights the role of the learners’ strategies and motivations that drive the management processes of the reading task (Vidal-Abarca et al. 2010 ). In this regard, PISA 2018 also assessed a range of non-cognitive variables associate with the learners’ beliefs, motivations, engagement, practices, and experiences in the reading classroom; one of the variables they assessed was metacognitive awareness of reading strategies related to two important cognitive processes: (a) understanding and memorizing the text, and (b) summarizing the text.

Various measures have been developed to measure metacognitive reading strategies including the Metacognitive Awareness of Reading Strategies Inventory (MARSI, Mokhtari and Reichard 2002 ), and the Survey of Reading Strategies (SORS, Sheorey and Mokhtari 2001 ) which was developed for second language learners. In the PISA reading assessment, the measure of metacognitive reading strategies was measured using two reading scenarios (Organisation for Economic Co-operation and Development 2019a ). The first reading scenario involved understanding and remembering a text, and the second scenario involved summarizing information in a text. For each scenario, the students were asked to evaluate a list of reading strategies and to indicate how effective each was to fulfill the goal of the scenario. Two indexes of metacognitive awareness of reading strategies were computed from responses to a list of strategies for each scenario.

The current study

The main objective of the current study is to explore how Filipino students’ metacognitive reading strategies is related to their reading proficiency in English as a second language. There have not been many previous studies that inquired into Filipino students’ reading strategies. Mante-Estacio ( 2016 ) surveyed students from one Philippine university using the SORS (Sheorey and Mokhtari 2001 ) and found overall high use of reading strategies, with problem-solution strategies used more than support and global strategies. Cirocki et al. ( 2019 ) surveyed high school students from a school in a rural region of the Philippines, and also found them preferring to use more problem-solving strategies than the global and support types. But Filipino students’ responses to these quantitative scales do not always converge with qualitative inquiries into the reading strategies. For example, Mante ( 2009 ) administered the MARSI (Mokhtari and Reichard 2002 ) among university students and found that the top reported reading strategies were reading the sentence again, relating an unknown word to something you already know, using context clues, and reading the text until it is clarified. But when given a reading task and then asked to respond to an open-ended question on what strategies they used in the task, the students reported a different set of strategies: making previews, identifying relevant and useful learning strategies, relating one’s prior knowledge, and double-checking on comprehension. The study also showed that the students reported using some ineffective reading strategies and not using some effective strategies (e.g., doing close reading as an initial strategy and throughout the reading process despite difficulties encountered, and paraphrasing and checking comprehension during reading) (Mante 2009 ).

The above studies merely describe what strategies tended to be used by Filipino students, but two studies inquired into how the students’ strategies related to their reading comprehension performance. Ilustre ( 2011 ) used the SORS and a researcher-made reading comprehension test with university students. Only problem-solving strategies subscale was positively associated with text comprehension; text comprehension was negatively correlated with support reading strategies. Mante ( 2013 ) administered the MARSI among Filipino high school students and two comprehension tests after reading four reading materials. Similar to previous studies, the students reported frequent use of all three types of metacognitive reading strategies, problem solving, global reading strategies, and support reading strategies, and that the last two were strongly correlated with each other. But only support reading strategies predicted the reading scores of the students’ unaided written recall.

While these few studies seem to suggest the use of metacognitive strategies (particularly, problem solving strategies) based on the quantitative scales, at least one study (Mante 2009 ) showed that the responses to the scale did not correspond to the students’ self-reports of actual strategies use after completing a reading task, and there is inconsistency in results showing which of these strategies relate to better reading performance. We note that these studies all involved small sample sizes of students drawn from one school or university.

As the Filipino students’ performance in the reading domain of PISA 2018 was disappointing, the PISA assessment provides data on reading proficiency and on metacognitive awareness of reading strategies from a nationally representative sample. The PISA 2018 database provides a good dataset to inquire into Filipino students’ reading strategies and proficiency in ways that previous Philippine research studies were unable to. More importantly, the inquiry allows for an investigation of a potentially important factor that explains reading proficiency, when that proficiency is very low. We note that there are previous studies that attempt to identify factors to explain the low reading proficiency (e.g., Bernardo 2023 ; Bernardo et al. 2021 ; Haw et al. 2021 ), those studies did focus on reading strategies. In the current study, we explored two related questions: (1) What strategies are perceived to be more useful by Filipino high school students? (2) What strategies are associated with Filipino high school students’ total reading proficiency and with each of the three cognitive subscales of reading proficiency?

We will seek answers to these questions using data from the PISA 2018 survey, and the PISA definition and framework for assessing reading proficiency is adopted. In particular, the PISA 2018 framework for reading proficiency features a “typology of cognitive processes involved in purposeful reading activities as they unfold in single or multiple text environments” (Organisation for Economic Co-operation and Development 2019a , p. 36). Thus, aside from the overall reading proficiency, we also explore how the perceived usefulness of the strategies related to the three broad categories of cognitive processes described earlier: locate information, understand, and evaluate and reflect. As regards metacognitive reading strategies, we explore each of the 11 strategies measured in PISA 2018 instead of using the two indexes of metacognitive strategies computed in the database. We believe that using the 11 strategies will provide more detailed analysis and answers to the main research questions.

Previous preliminary analysis of the Philippines PISA 2018 data (Besa 2019 ) indicated significant sex differences (i.e., girls outperform the boys), and across different socioeconomic statuses (socioeconomically advantaged students outperformed socioeconomically disadvantaged ones). In this regard, we decided to include sex and socioeconomic status of the student as control variables. We included one other home background variable from the PISA survey as another control variable in the analysis; students were asked the number of books in their home, and this factor has been consistently identified as an important home variable that predicts reading proficiency in many different countries (Chiu and McBride-Chang 2006 ; Park 2008 ).

Data and participants

We use data from the Philippine sample in the OECD PISA 2018 database. The complete nationally representative sample comprised 7233 15-year-old Filipino students, who were randomly selected using a two-stage stratified random selection system. First, stratified sampling was used to select 187 schools from the country’s 17 regions, and then students were randomly sampled from each school to participate in the PISA assessment (Besa 2019 ). Because English is the official medium of instruction in most subjects in high school, reading proficiency was assessed in English, although only 408 (or 5.64%) reported that the main language they used at home was English.

Reading proficiency

To assess reading proficiency, we referred to the plausible values provided in the PISA 2018 dataset. To clarify, the PISA 2018 assessment does not provide actual achievement scores for each student; instead, it assesses cognitive learning in the reading domain using ten plausible values that represent ten random values drawn from the posterior distribution of the student’s scores for reading (Organisation for Economic Co-operation and Development 2019b ). In addition to the plausible value for the overall reading proficiency, PISA 2018 also provided plausible values for three cognitive process subscales of reading: (a) locate information, (b) understand, and (c) evaluate and reflect. For the current study, we used the first plausible for the overall reading proficiency and for the three cognitive subscales. Previous studies on PISA data have used only one plausible value (e.g., Bernardo et al. 2023 ; Gomez and Suarez 2020 ; Spiezia 2010 ; Trinidad 2020 ) based on the assumption that one plausible value is said to provide unbiased estimates of population parameters. Prior to deciding to use only one plausible value, we examined the distribution and correlations among the ten plausible values for overall reading proficiency and for the three cognitive subscales and we found the means and standard deviations of the ten distributions are almost identical and are highly correlated with each other. Thus, it is unlikely that an analysis with only one of ten plausible values would lead to loss of information.

Metacognitive reading strategies

The student questionnaire of PISA included 11 items that referred to different strategies that students use in their reading and writing tasks (see Table 1 for the items); 6 items referred to strategies to help them understand and memorize the text that they read, and five items referred to strategies to help them write summaries of the text that they read. The students were asked whether they perceived each strategy as being useful for the different reading tasks indicated, and they answered using a scale from 1 ( not useful at all ) to 6 ( very useful ).

Economic, social, and cultural status

Several indexes of the students’ SES were computed in the PISA 2018, and for this study the index of economic, social, and cultural status (henceforth, ESCS) was used. The ESCS was derived from the students’ report on the availability of 16 household items (e.g., a room of one’s own, air-conditioning unit, and three country-specific items), other possessions in the students’ homes (e.g., cell phones with internet access, computers), education and work status of the students’ parents.

Number of books at home

The students were also asked to estimate how many books there were in their home. They were instructed to exclude magazines, newspapers, and their schoolbooks, and they responded by ticking one of six options: 0–10 books, 11–25, 26–100, 101–200, 201–500, and more than 500 books.

Data analysis

There were numerous missing data across the different variables, and we conducted analysis only on participants with complete data on all the main variables and control variables. The final sample for the analysis comprised 6591 students (53.86% were girls). To answer the first research question, mean scores of the 11 metacognitive reading strategies were computed and then analyzed using a completely repeated analysis of variance. The analysis of the main effect of type of learning strategy was followed by a post hoc multiple comparison of means using Bonferroni test. To answer the second set of questions, four hierarchical regression analyses were conducted. In each analysis, the reading proficiency score or subscale score was first regressed to three control variables (sex, ESCS, and number of books at home). In the second step of each hierarchical regression, the 11 learning strategies were added to the model. At each step, the overall model and change in R 2 of the model was assessed.

The first research question of the study investigates which learning strategies for reading are perceived to be most useful by Filipino 15-year-old learners. The results are summarized in Table 1 , which lists the different learning strategies according to their perceived usefulness. As the standard deviations indicate, the students varied in their perceptions of the different strategies, and the students’ responses spanned the full range of options for each strategy. The repeated measures ANOVA (with learning strategy as within group factor) indicated a statistically significant difference across the means, F (10, 6649) = 56463.08, p  < 0.001, partial η 2  = 0.90. The Bonferroni test used in the post hoc multiple comparison of means indicated that the first three strategies were perceived most useful by the Filipino students compared to the other eight strategies, although the means of the three were not statistically different from each other. The top two strategies are similar as both include underlining, with the first involving the additional strategy of writing the underlined texts in their own words. The two strategies rated least useful are low-level reading strategies commonly chosen by readers whose repertoire of strategies is limited. Underlining involves separating content based on importance, which is not always enough promote learning (Dunlosky et al. 2013 ) and is typically effective in reading comprehension when used in combination with other strategies (Pugalee 2007 ).

For the second question, we first investigated the learning strategies that predicted overall reading proficiency. The results of the hierarchical regression analysis are summarized in Table 2 . First, we note that adding the perceived usefulness of the 11 strategies in the regression model explained an additional 18% of the variations in overall reading proficiency, relative to the variation explained by sex, socioeconomic status, and number of books at home. Therefore, we can infer that the Filipino students’ metacognitive reading strategies are relevant factors in understanding their reading achievement. Second, we note (as the learning strategies are listed according to their perceived usefulness following Table 1 ) that the strategies perceived most useful are not always the ones most strongly associated with the students’ overall reading proficiency. As shown in Table 2 , perceiving the first two strategies as useful was positively associated with reading proficiency, but perceiving some lower ranked strategies as useful (i.e., ranked 3, 4, and also 6, 7, and 9) was more strongly associated with reading proficiency. In contrast, the perceived usefulness of the last two strategies was strongly negatively associated with the students’ overall reading proficiency. That is, the students who perceived these two strategies as useful were more likely to have lower reading proficiency. Finally, the perceived usefulness of two strategies (see, ranked 5 and 8) was not significantly associated with the students’ reading proficiency.

We then ask, was the pattern of results similar when we examined the three cognitive subscales of reading? Generally, the results in Table 3 suggest yes, except for a few interesting differences. Again, we note that adding the 11 metacognitive reading strategies in the regression model explained an additional portion of the variations in overall reading proficiency, relative to the variation explained by the three control variables. For locate information, adding the 11 strategies in the model resulted in Δ R 2  = 0.16, F (11, 6576) = 149.77, p  < 0.001; for understand, Δ R 2  = 0.18, F (11, 6576) = 170.06, p  < 0.001; and for evaluate and reflect, Δ R 2  = 0.14, F (11, 6576) = 123.75, p  < 0.001. Across these three cognitive subscales, there are two notable differences. In both cases, the perceived usefulness of strategy is associated with the more basic cognitive processing but not for the higher-level processes. As shown in Table 3 , perceiving “underlining important parts of the texts” [see (2)] as useful is positively associated with scores for locating information and for understanding, but not for evaluate and reflect. Similarly, perceiving “reading the text as many times as possible” [see (5)] as useful is positively associated with scores for locate information, but not for understand nor for evaluate and reflect.

This study was conducted to explore whether Filipino students’ metacognitive reading strategies is associated with their reading proficiency. As the data analyzed were from the Philippines’ PISA 2018, where the Filipino students performed rather poorly, ranking last among all the participating countries/economies, the inquiry investigated whether the reading strategies used (or not used) by the Filipino students could explain the poor reading performance. The results indicate that metacognitive reading strategies explains a significant portion of the variation in Filipino students’ overall reading proficiency and also in each of the three cognitive subscales. The specific results point to some useful observations about the Filipino students’ metacognitive strategies, which we briefly discuss below.

First, the two reading strategies that Filipino students perceived as very useful were not the strongest predictors of reading proficiency. These two strategies were “I underline important parts of the text” and “I read through the text, underlining the most important sentences, [t]hen I write them in my own words as a summary,” which were rated “very useful” by 32.3% and 30.7% of the Filipino students, respectively. Both strategies involve underlining parts that are considered important. This selective highlighting allows readers to focus on important parts of a given text, thereby enabling them to organize the material being read. Effective highlighting allows readers to discriminate between minor and major details of a text. These highlighting strategies may lead to an “isolation effect” (Hunt 1995 ) wherein the highlighted sections of a text are better remembered and the text, in general, is better processed (Cashen and Leicht 1970 ). The use of both strategies was positively associated with reading proficiency, but the relationships were weak ( β coefficients ≤0.05) and the first of these strategies was not associated with the scores in the higher cognitive subscale of evaluate and reflect. This result might indicate some overestimation of the usefulness of the two strategies in aiding reading comprehension; the overestimation may be in reference to their appreciation of the usefulness of the other strategies.

In contrast, the two strategies that were perceived least useful on the average were both strongly negatively associated with reading proficiency and its cognitive subscales. These two strategies were, “I try to copy out accurately as many sentences as possible” and “I read the text aloud to another person,” which both involve repeating the encoding of the text. Copying sentences exactly does not engage readers to think and might be a waste of their time as it does not involve higher level processing like what is used in selective note taking. On the other hand, reading aloud may develop reading fluency but is not likely to help in reading comprehension tasks like understanding and summarizing. Yet, these two strategies were rated as “very useful” by 12.4% and 13.3% of the Filipino students, respectively; around 25% of the Filipino students actually rated these two strategies using the two highest points in the 6-point scale of usefulness. This set of results indicate a sizable proportion of the students’ lacking awareness of the efficacy of two strategies, which are strongly and negatively associated with reading proficiency according to the data. In this case, the students might be underestimating the usefulness of the two strategies relative to their actual relationship with reading proficiency.

The results point to another concern regarding two strategies that were not significantly associated with reading proficiency. These two strategies are “Before writing the summary, I read the text as many times as possible” and “I write a summary [t]hen I check that each paragraph is covered in the summary, because the content of each paragraph should be included.” Around 40% of the Filipino students rated the first strategy as very useful in 6-point scale of usefulness, and it was only weakly positively associated with the most basic cognitive subscale (locate information). Around a third of the students rated the second strategy as useful, suggesting the students’ lack of awareness regarding the usefulness of the two strategies which were not predictive of reading proficiency according to the results.

In contrast, consider the two strategies that were most strongly associated with higher achievement. The strategy “I summarize the text in my own words” was rated as “very useful” by 27.1% of the students, yet another 27.3% rated the same strategy as not useful. A student who uses this strategy is engaging in many different processes such as differentiating minor from major details, getting the main idea, identifying the author’s purpose, integrating several ideas into one message or theme among others. The strategy “I carefully check whether the most important facts in the text are represented in the summary” was rated as very useful by 27.8% of the students, but 31.19% indicated that they perceived this strategy as not useful. This strategy entails examining the accuracy of their output against the given reading material, and students must evaluate if the ideas presented in the summary are complete and are all crucial in restating the original text. Therefore, it includes judging and critiquing their own work based on how they understood the reading material. These two strategies help not only to understand the text but also to analyze, evaluate, and critique the text; yet a significant proportion of Filipino students consider them not useful.

These results taken together suggest possible gaps in Filipino students’ awareness of and appreciation of metacognitive reading strategies. For both sets of strategies for understanding and for writing, their awareness of the usefulness of the strategies varied across students; and some strategies that are associated with reading proficiency were not rated as useful on the average, while other strategies not associated with reading proficiency were rated as useful on the average. The data cannot provide for explanations for these patterns, and we should use caution in attributing these patterns to students’ lack of understanding of such strategies. Indeed, students’ responses regarding the usefulness of the strategies might reflect what they were told by their teachers, or what their classmates shared to them, or they might indicate a limited exposure to the possible reading strategies available to them. The students’ perceived usefulness of the strategies might also reference their own understanding of what reading proficiency means, which might not align with the PISA reading assessment definitions. We should also consider that the students’ understanding of reading proficiency might also reflect their experiences in their reading class instruction and assessment.

Indeed, one possible important implication relates to the question of whether the Filipino students are being taught these reading strategies in their reading education, and what competencies of reading proficiency they are assessed in their reading classes. An analysis of the Philippine high school reading curriculum in comparison to the PISA 2018 reading framework (Romero and Papango 2020 ) found that the task management skills (which include reading strategies) are found in the Philippine reading curriculum for Grades 7 to 10. But their analysis suggests that these task management skills are taught as discreet topics independent of the teaching of the text processing skills. More importantly, Romero and Papango ( 2020 ) noted that the task management skills found in the Philippine curriculum did not fully align with those indicated in the PISA 2018 reading framework (Organisation for Economic Co-operation and Development 2019a ).

The results and preceding discussions point to the need for an efficient and integrated reading strategy instruction that will focus on knowledge, skills, and experience development across the grade levels in the Philippine reading curriculum. Strategy instruction should also present a wide range of strategies from the simple strategies to the more complex, which Filipino learners can explore and use in various types of texts in both print and online modes of reading. For instance, visualizing and connecting-to-oneself strategies are likely to be developed when the texts are culturally relevant to the reader. Culturally relevant texts in turn are found to positively affect one’s reading comprehension (Tan and Mante-Estacio, 2021 ). Related to this point, further research could inquire into how reading teachers in the Philippines actually teach reading strategies and how students engage such instructional activities intended to develop metacognition in reading.

It is also important to acknowledge that there is a threshold of reading proficiency which needs to be met before metacognitive strategies can become a significant factor in one’s reading performance (Schoonen et al. 1998 ). Thus, it is possible that the 15-year-old Filipino participants in PISA may not have reached the threshold level of reading proficiency in English for metacognitive reading strategy to make a difference in their reading performance. While it is the case that the positive statistical relationship between some of the reading strategies and reading proficiency suggests that metacognitive reading strategies statistically predicts the Filipino students’ reading proficiency, this notion of a threshold level of reading proficiency might be relevant to consider in deciding how early metacognitive reading strategies is introduced in the task management components of the reading curriculum in English as a second language. Consider the rather low overall levels of reading proficiency assess in the PISA 2018, it is possible that Filipino reading teachers might need to focus on other more basic components of reading competencies before focusing on metacognitive strategies for reading.

In this regard, it is also important to underscore that the reading proficiency analyzed in this study refers to reading in a second language. Second language research has underscored how reading in a second language is essentially a cross-linguistic process, which means that the students’ reading in their first language has an important role in developing skills in reading in their second language and vice versa (Koda 2005 ). The reading processes in the two languages can mutually facilitate and accelerate learners’ overall reading abilities. It is quite likely that the Filipino students’ proficiency, strategies, and other cognitive and noncognitive factors related to reading in their first language also affect their reading proficiency in English. Thus, it is probable that various other factors contribute to the differences in the reading performance of Filipino students in PISA reading assessment, and future research needs to inquire into these, as well.

The preceding arguments point to some important limitations in the study. As our analysis solely focussed on metacognitive strategies as main predictors of reading proficiency, we included only a few control variables to those known to be very strongly associated with the PISA reading outcomes. This limitation in the scope of the analysis did not allow us to explore the relative importance of metacognitive reading strategies together with other cognitive and non-cognitive student-level variables, family background, classroom and school experiences, among others. The analysis was also limited to those strategies included in the PISA 2018 measure, and as such did not allow for the investigation of other reading strategies that may have been important and/or useful for Filipino readers in English. Future research that analyze a wider range of relevant factors and reading strategies would be very useful in deepening the findings of the current study.

Even with the limitations of the study, we believe that this exploration of how metacognitive reading strategies among 15-year-old Filipino readers in English extends the very limited empirical research on reading strategies of Filipino readers in English. As most previous research on Filipino readers’ reading strategies typically relied on small sample sizes of students in selected schools, the nationally representative sample analyzed in the study allow for greater confidence in the conclusions regarding the role of reading strategies in second language reading of Filipinos. The results also contribute further evidence to the growing research on strategies in second language reading in general and to the continuing need for discussions on effective reading strategy instruction among second language learners. In particular, as the results show that Filipino students’ perceptions on the usefulness of the strategies might not always align with the strategies’ usefulness as indicated by their associations with reading proficiency, we could reflect further on what reading competencies in the second language are actually understood and experienced by Filipino students in their classrooms. Understanding the students’ notions of reading proficiency might help us to better understand why they perceive some strategies as more useful than others. The results also point to the need to inquire into how metacognitive reading strategies are taught and modeled by teachers and how students engage these strategies in reading classrooms, because such experiences are likely to also shape how the students perceive the effectiveness of the strategies.

Data availability

The data analyzed in this study are available in the PISA 2018 Database page on the website of the Organisation for Economic Co-operation and Development 1 .

https://www.oecd.org/pisa/data/2018database/ , accessed on 17 February 2020

Ahmadi MR, Ismail HN, Abdullah et al. (2013) The importance of metacognitive reading strategy awareness in reading comprehension. Eng Lang Teach 6(10):235–244

Google Scholar  

Alkhateeb HM, Abushihab EF, Alkhateeb, RH et al. (2021) Reading strategies used by undergraduate university general education courses for students in US and Qatar. Read Psychol. https://doi.org/10.1080/02702711.2021.1912967

Bernardo ABI (2023) Growth mindset and reading proficiency of ESL learners: examining the role of students’ socioeconomic status using PISA 2018 Philippine data. Euro J Psychol Educ 38:675–693. https://doi.org/10.1007/s10212-022-00629-6

Article   Google Scholar  

Bernardo ABI, Cordel MO, Lucas RI et al. (2021) Using machine learning approaches to explore non-cognitive variables influencing reading proficiency in English among Filipino learners. Educ Sci 11(10):628. https://doi.org/10.3390/educsci11100628

Bernardo ABI, Cordel MO, Calleja MO et al. (2023) Profiling low-proficiency science students in the Philippines using machine learning. Humanit Soc Sci Commun 10:192. https://doi.org/10.1057/s41599-023-01705-y

Article   PubMed   PubMed Central   Google Scholar  

Besa F (2019) Philippines country note. Programme for International Student Assessment (PISA). Results from PISA 2018. OECD. http://www.oecd.org/pisa/publications/PISA2018_CN_PHL.pdf

Carell P (1989) Metacognitive awareness and second language reading. Mod Lang J 73(2):121–134. https://doi.org/10.1111/j.1540-4781.1989.tb02534.x

Cashen VM, Leicht KL (1970) Role of the isolation effect in a formal educational setting. J Educ Psychol 61(6):484–486. https://doi.org/10.1037/h0030286

Chen KTC, Chen SCL (2015) The use of EFL reading strategies among high school students in Taiwan. Read Matrix 15(2):156–166

MathSciNet   Google Scholar  

Chiu MM, McBride-Chang C (2006) Gender, context, and reading: a comparison of students in 43 countries. Sci Stud Read 10(4):331–362. https://doi.org/10.1207/s1532799xssr1004_1

Cirocki A, Parba J, Caparoso J et al. (2019) Metacognitive reading strategies in the Filipino ESL classroom: use and instruction. Asian J Eng Lang Teach 28:29–60

Dunlosky J, Rawson KA, Marsh EJ, Nathan MJ, Willingham DT (2013) Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychol Sci Public Interest Suppl 14(1):4–58. https://doi.org/10.1177/1529100612453266

Fitrisia D, Tan KE, Yusuf YQ (2015) Investigating metacognitive awareness of reading strategies to strengthen students’ performance in reading comprehension. Asia Pac J Educ Educ 30:15–30

Friesen D, Haigh C (2018) How and why strategy instruction can improve second language reading comprehension: a Review. Read Matrix 18(1):1–18

Garner R (1987) Strategies for reading and studying expository text. Educ Psychol 22(3–4):299–312. https://doi.org/10.1080/00461520.1987.9653054

Gomez R, Suarez AM (2020) Do inquiry-based teaching and school climate influence science achievement and critical thinking? Evidence from PISA 2015. Int J STEM Educ 7(43) https://doi.org/10.1186/s40594-020-00240-5

Grabe, W, & Stoller, FL (2013) Teaching and Researching: Reading. Routledge, London. https://doi.org/10.4324/9781315833743

Haw JY, King RB, Trinidad JER (2021) Need supportive teaching is associated with greater reading achievement: wWhat the Philippines can learn from PISA 2018. Int J Educ Res 110:101864. https://doi.org/10.1016/j.ijer.2021.101864

Hong-Nam K, Page L (2014) Investigating metacognitive awareness and reading strategy use of EFL Korean university students. Read Psychol 35(3):195–220. https://doi.org/10.1080/02702711.2012.675418

Hunt RR (1995) The subtlety of distinctiveness: what von Restorff really did. Psychon Bull Rev 2(1):105–112

Article   CAS   PubMed   Google Scholar  

Ilustre CAP (2011) Beliefs about reading, metacognitive reading strategies and text comprehension among college students in a private university. Philipp ESL J 7:28–47

Kasemsap B, Yu-Hsiu Lee H (2015) L2 reading in Thailand: vocational college students’ application of reading strategies to their reading of english texts. Read Matrix 15(2):101–117

Koda K (2005) Insights into Second language reading: a cross-linguistic approach. Cambridge University Press, London

Book   Google Scholar  

Kolić-Vehovec S, Bajšanski I (2007) Comprehension monitoring and reading comprehension in bilingual students. J Res Read 30(2):198–211. https://doi.org/10.1111/j.1467-9817.2006.00319.x

Mante MJS (2009) College students’ awareness and use of metacognitive reading strategies. Read Assoc Philipp J 32:44–53

Mante MJS (2013) Bilingual readers’ metacognitive strategies as predictors of reading comprehension. Philipp ESL J 10(1):179–199

Mante-Estacio MJS (2016) College students’ internet use and offline/online reading motivation. In: Bernardo ABI (ed), Counseling, psychology, and education, De La Salle University Publishing House, p 245-252

McCrudden MT, Schraw G (2007) Relevance and goal-focusing in text processing. Educ Psychol Rev 19:113–139. https://doi.org/10.1007/s10648-006-9010-7

Meniado JC (2016) Metacognitive reading strategies, motivation, and reading comprehension performance of Saudi EFL Students. Eng Lang Teach 9(3):117–129

Mohseni F, Seifoori Z, Ahangari S et al. (2020) The impact of metacognitive strategy training and critical thinking awareness-raising on reading comprehension. Cog Educ 7(1). https://doi.org/10.1080/2331186X.2020.1720946

Mokhtari K, Reichard CA (2002) Assessing students’ metacognitive awareness of reading strategies. J Educ Psychol 94(2):249–259. https://doi.org/10.1037//0022-0663.94.2.249

Organisation for Economic Co-operation and Development. (2019a) PISA 2018. Assessment and analytic framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en

Organisation for Economic Co-operation and Development. (2019b) PISA 2018 results (Vol I): What students know and can do. OECD Publishing. https://doi.org/10.1787/5f07c754-en

Park H (2008) Home literacy environments and children’s reading performance: a comparative study of 25 countries. J Educ Res 15(6):489–505

Pinninti LR (2016) Metacognitive awareness of reading strategies: an Indian context. Read Matrix 16(1):179–193

Pugalee DK (2007) Developing mathematical and scientific literacy: effective content reading practices. Christopher-Gordon, Norwood, MA

Romero AD, Papango MC (2020) PISA reading literacy vis-à-vis K to12 English curriculum. In: Balagtas MU, Montealegre MC (eds) Challenges of PISA: The PNU Report. Philippine Normal University and Rex Institute for Student Excellence, Inc., p 33-56

Schiff R, Calif S (2004) An academic intervention program for EFL university students with reading disabilities. J Adolesc Adult Lit 48(2):102–113. https://www.jstor.org/stable/40009160

Sheikh I, Soomro K, Kamal A et al. (2019) Metacognitive awareness of reading strategies, reading practices and academic attainments of university students. J Educ Educ Dev 6(1):126–137

Sheorey R, Mokhtari K (2001) Differences in the metacognitive awareness of reading strategies among native and non-native readers. System 29(4):431–449. https://doi.org/10.1016/S0346-251X(01)00039-2

Schoonen R, Hulstijn J, Bossers B (1998) Metacognitive and language-specific knowledge in native and foreign language reading comprehension: an empirical study among Dutch students in grades 6, 8, and 10. Lang Learn 48(1):71–106. https://doi.org/10.1111/1467-9922.00033

Singhal M (2001) Reading proficiency, reading strategies, metacognitive awareness and L2 readers. Read Matrix 1(1). https://readingmatrix.com/articles/singhal/index.html

Spiezia V (2010) Does computer use increase educational achievements? Student-level evidence from PISA. OECD J Econ Stud 2010(1):1–22. https://doi.org/10.1787/eco_studies-2010-5km33scwlvkf

Tan DNL, Mante-Estacio MJ (2021) Reader-text connection: reporting the engagement of high school students with culturally-relevant texts. TEFLIN Journal 32(2):342–361

Tavakoli H (2014) The effectiveness of metacognitive strategy awareness in reading comprehension: the case of Iranian university EFL students. Read Matrix 14(2):314–336

Tercanlioglu L (2004) Postgraduate students’ use of reading strategies in L1 and ESL contexts: links to success. Int Educ J 5(4):562–570

Trinidad JE (2020) Material resources, school climate, and achievement variations in the Philippines: Insights from PISA 2018. Int J Educ Dev 75. https://doi.org/10.1016/j.ijedudev.2020.102174

Vidal-Abarca E, Mañá A, Gil L (2010) Individual differences for self-regulating task-oriented reading activities. J Educ Psychol 102(4):817–826. https://doi.org/10.1037/a0020062

White S, Chen J, Forsyth B (2010) Reading-related literacy activities of American adults: time spent, task types, and cognitive skills used. J Lit Res 42:276–307. https://doi.org/10.1080/1086296X.2010.503552

Yoshikawa L, Leung CY (2020) Transitional shift of metacognitive awareness of reading strategy along with L2-English reading proficiency. Read Matrix 20(1):36–44

Zhang LJ (2001) Awareness in reading: EFL students’ metacognitive knowledge of reading strategies in an acquisition-poor environment. Lang Aware 10(4):268–288. https://doi.org/10.1080/09658410108667039

Zhang L, Goh CCM, Kunnan AJ (2014) Analysis of test takers’ metacognitive and cognitive strategy use and EFL reading test performance: a multi-sample SEM approach. Lang Assess Quat 11(1):76–102. https://doi.org/10.1080/15434303.2013.853770

Article   CAS   Google Scholar  

Download references

Acknowledgements

This research was funded by a Research Fellowship from the National Academy of Science and Technology, Philippines to the first author. The APC was funded by the De La Salle University Science Foundation, Inc.

Author information

Authors and affiliations.

De La Salle University, Manila, Philippines

Allan B. I. Bernardo & Ma. Joahna Mante-Estacio

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: ABIB, MJME; Data analysis: ABIB; Writing—original draft preparation, review, and editing: ABIB, MJME; Funding acquisition: ABIB

Corresponding author

Correspondence to Allan B. I. Bernardo .

Ethics declarations

Competing interests.

The authors declare no competing of interest.

Ethics approval

The study involved secondary analyses of the officially published PISA 2018 dataset; as such ethics review and approval and informed consent does not apply. This dataset was downloaded as a public use file from the OECD website Footnote 1 .

Informed consent

The study involved secondary analyses of the officially published PISA 2018 dataset; as such ethics review and approval and informed consent does not apply.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bernardo, A.B.I., Mante-Estacio, M.J. Metacognitive reading strategies and its relationship with Filipino high school students’ reading proficiency: insights from the PISA 2018 data. Humanit Soc Sci Commun 10 , 400 (2023). https://doi.org/10.1057/s41599-023-01886-6

Download citation

Received : 12 February 2023

Accepted : 21 June 2023

Published : 10 July 2023

DOI : https://doi.org/10.1057/s41599-023-01886-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Reflections on the field of metacognition: issues, challenges, and opportunities

• Published: 03 June 2020
• Volume 15 , pages 91–98, ( 2020 )

Cite this article

• Roger Azevedo 1  

14k Accesses

50 Citations

12 Altmetric

Explore all metrics

Avoid common mistakes on your manuscript.

There have been major advances in the field of metacognition since I took over as the editor-in-chief of the Metacognition and Learning journal in 2011. As the former editor of the journal, I have had the honor and privilege to witness such advances and am proud that this work has been published in the journal. Since the journal’s inception by Marcel Veenman, I significantly expanded the editorial board to ensure the journal covered the various multidisciplinary fields that contribute to studying the complex nature of metacognition, enticing interdisciplinary researchers to publish their scientific and innovative work in the journal. After eight years at the helm, I express my gratitude to my Associate Editors (Anastasia Efklides, Jeffrey Greene, and Sabina Kleitman), editorial board members, and editorial assistants (Jason Harley and Michelle Taub) profusely for their invaluable contributions to our journal. I also thank the co-editors of several special issues of the journal for publishing significant work. In this editorial, I reflect on the major issues, challenges, and opportunities that have arisen in the field of metacognition.

Reflections and major issues, challenges and opportunities in the field

The first article published in the journal, by Veenman, Van Hout-Wolters, and Afflerbach in 2006, described and outlined the most important issues dominating the field fourteen years ago. Looking back, I will reflect on how far we have advanced in addressing each of the issues.

One of the major issues is the abundance of definitions for metacognition found in the journal and other journals in the fields of educational psychology, cognitive psychology, developmental psychology, cognitive science, learning sciences, STEM education, and computational sciences. The plethora of definitions describe various constructs, assumptions, processes, mechanisms, and so forth that impede a unified definition of metacognition. Despite numerous advances in the field, more theoretical work needs to be done for attaining a unified definition of metacognition and its interrelated components (see Dunlosky and Rawson 2019 ; Panadero 2017 ; Schunk and Greene 2018 ). An excellent example is Norman et al. ( 2019 ) review of the major advances in metacognition within different sub-fields of psychology. As researchers and educators across different fields become increasingly interested in studying metacognition based on its relevance toward understanding learning, problem solving, reasoning, conceptual understanding across learners of all ages, topics, domains, tasks, and contexts, the challenge becomes even more pronounced.

In addition to definitional issues, more research is needed to distinguish between metacognitive knowledge from skills . One of the best examples of this distinction is the work of Tarricone ( 2011 ) where the most comprehensive conceptual framework of metacognition is provided and clearly distinguishes between metacognitive knowledge and skills. To avoid the common interchangeable use between metacognition and self-regulated learning (SRL) that is found widespread across the various literatures and fields, clear distinctions are imperative for advancing our understanding of metacognition and SRL. As such, it is critical for the future advancement of the field that further research studies the complex relations between Theory of Mind, metamemory, metacognitive awareness, experiences, judgments, evaluations, metacognitive knowledge and skills, decision-making, and reflection. These relations should then be further distinguished based on the self, others, task, context, and so forth. This issue could be partially addressed by integrating several models, frameworks, and theories of metacognition. While several comparisons between models, theories, and frameworks have been published (e.g., Panadero 2017 ), there has been no significant attempt at integrating them into a unified theory of metacognition. For example, imagine integrating metacognitive knowledge and skills, while also accounting for varying levels of granularity in metacognitive processes that describe the temporal dynamics. We can predict the timing of metacognitive processes and distinguish between the types of metacognitive knowledge (e.g., declarative metacognitive knowledge vs. procedural metacognitive knowledge) and skills (e.g., regulating cognitive strategies), valence associated with metacognitive judgments (e.g., a positive judgment of learning [JOL+] vs. a negative judgment of learning [JOL-]), feedback loops associated with cycles of metacognitive monitoring and control, and so forth across frameworks, models, and theories (e.g., Ackerman and Thompson 2017 ; Efklides et al. 2018 ; Hacker and Bol 2019 ; Koriat 2015 ; Metcalfe 2009 ; Nelson and Narens 1990 ; Winne 2018 ). However, a unified theory would be incomplete without accounting for emotions, motivation, and social processes depending on the context in which the learner is learning. Can we press forward as researchers by including models of emotions, motivation (e.g., Linnenbrink-Garcia et al. 2016 ; Renninger and Hidi 2019 ), and social processes (e.g., Hadwin et al. 2017 ) into a unified theory of metacognition? How generalizable would this unified theory be to all learners, cultures, learning systems, and contexts (e.g., classroom versus remote instruction)? What adaptations would researchers have to make to account for a myriad of factors and variables, especially related to context? What would be its limitations?

Another notable issue of discussion in the field has been the complex interaction between cognition and metacognition that continues to challenge researchers, as it is hard to distinguish between them since each of the constructs rely and influence each other and share processes (Winne 2018 ). This represents a dilemma of having a higher-order agent overlooking and governing the cognitive system while also simultaneously being part of it. Advances in the cognitive sciences, computational sciences, robotics, and artificial intelligence (AI) provide excellent tools and techniques for detecting, measuring, and modeling how metacognition and cognition complexly interact with one another. For example, computational modeling holds the key to answering critical questions about the interaction between cognition and metacognition by modeling and testing theoretically-based assumptions such as the acquisition and optimal use of metacognitive knowledge and skills in solving complex tasks in AI-based agents including intelligent virtual humans, robots, cyberhumans, etc. (e.g., Kralik et al. 2020 ). Other relevant research addressing this issue would be the extensive work in the cognitive sciences, embodied cognition, and cognitive neuroscience disciplines, especially as intelligent, immersive virtual systems (e.g., virtual reality, augmented reality, mixed reality, extended reality) become ubiquitous, offering new opportunities for researchers and educators to explore key areas of metacognition outside of the traditional methodology affordances.

Researchers also continue to consider the role of conscious versus automatic metacognitive processes . For example, does metacognition, by definition, require conscious processing, or can metacognitive activities also appear on a less conscious level? How can researchers capture automatic metacognitive processes? How does drawing attention to one’s own automatic metacognitive processes impact their cognition and metacognition? This issue needs to be addressed by advancing new measurement methods and techniques where multiple approaches (e.g., self-reports, concurrent and retrospective verbalizations) may need to be combined in order to capture metacognitive processes during learning, problem solving, task performance, and even their transfer to other topics, domains, and contexts. For example, if through repeated practice, metacognitive processes became automatic and then learners are faced with an atypical, or non-isomorphic, complex transfer task, would they revert to more effortful processing and therefore reveal conscious metacognitive processes? Or contrarily, would they reveal otherwise hidden misconceptions or errors in their metacognition? Additionally, what is the time-based (in the scale of minutes, days, months, years) transition between conscious and automatic activities, and are the changes based on individual differences (e.g., expertise level), familiarity with the task, learning context, goal of the task (e.g., an expert whose automated metacognitive processes used in their domain reverts to consciously and overtly modeling these same metacognitive processes when teaching a novice how to monitor one’s understanding during problem solving)?

Another persistent issue is distinguishing between domain-generality versus domain-specificity of metacognition . It is argued that general metacognition may be instructed concurrently in different learning situations and thus expected to transfer to new learning situations, whereas specific metacognition must be taught for each task or domain separately (Veenman et al. 2006 ). This is a major issue that deserves priority given national and international focus on twenty-first Century skills, automation in the workforce, and the need for learners and workers to be flexible, adaptive, and capable of transferring metacognitive knowledge and skills to a variety of evolving challenges (e.g., remote learning during pandemics, automation in the workplace) and in real-world applications (Greene et al. 2015 ; Kleitman and Narciss 2019 ). Aside from the developmental trajectory in acquiring metacognitive knowledge and skills, the question remains of how to best teach and train learners to develop both domain-general and domain-specific metacognition. This challenge extends, not only to learners and workers, but also to teacher preparation programs where teaching teachers to learn, use, model, instruct, and foster metacognition in their students is paramount for enhancing teacher preparation for future learning (Callan and Shim 2019 ; Dignath and Buttner 2018 ; Kramarski 2018 ). A related issue that deserves increased research is the fostering metacognitive training for teachers themselves (i.e., how can they teach it without understanding it themselves).

This issue raises several important questions about the (1) sequencing of training (e.g., declarative followed by procedural and then conditional knowledge and skills?); (2) length of training regiments (e.g., declarative will presumably take less time to master than conditional knowledge and skills); (3) tasks to use and embed in the teaching and training regiments (e.g., same tasks within topic or across domains, isomorphic tasks across topics or domains); (4) who or what should deliver the teaching and training (e.g., teacher, parent, peer, expert, or artificial agent such as a virtual human or robot, or a combination of human and artificial agents); (5) instructional model used to guide the training regiments (e.g., modeling by expert while vicarious learning by teacher or learner, then practice using acquired metacognitive knowledge and skills with adaptive scaffolding by expert, and then once mastery shows fade all scaffolding; then repeat cycle for next domain-general and domain-specific metacognitive knowledge and skills); (6) measuring the quantitative and qualitative metacognitive knowledge and skills (e.g., use advanced learning technology such as immersive virtual learning environments to detect, track, model, and prompt while using data visualizations to illustrate the multi-faced nature of domain-general vs. domain-specific metacognition) with problems from the same domain and other domains, and (7) developing new transfer tasks and assessments capable of detecting learners acquiring, internalizing, retaining, retrieving, using, and transferring their metacognitive knowledge and skills competently.

The previous issue is intimately tied to the conditions for acquiring and instructing metacognition . While most learners acquire metacognitive knowledge and skills at a varying level of proficiency from their parents, peers, and teachers, they still show considerable varying metacognitive adequacy (Dunlosky and Lipko 2007 ). The literature on metacognitive instruction clearly indicates that three conditions for acquiring and instructing metacognition must include (1) embedding metacognitive instruction in the content matter to ensure connectivity, (2) informing learners about the usefulness of metacognitive activities to make them exert the initial extra effort, and (3) prolonging training to guarantee the smooth and maintained application of metacognitive activity. Despite these global principles, there are a few notable examples in math (Kramarski and Michalsky 2013 ), writing (Hacker 2018 ; Harris and Graham 2017 ) and reading (Griffin et al. 2019 ), but the instruction of metacognition remains largely unexplored. Lastly, many of the issues presented above related to distinction between domain-generality versus domain-specificity of metacognition apply to this issue as well.

The developmental processes in metacognition remain an important area in the field led mostly by developmental psychologists. Despite the clear picture emerging from the literature on metacognition development (e.g., Hoyle and Dent 2018 ; Schneider and Löffler 2016 ) involving the development of Theory-of-Mind somewhere between the age of 3 to 5 years, followed by the development of metamemory and metacognitive knowledge and skills that continue to develop throughout the lifespan there is still much to be explored (Roebers and Spiess 2017 ). Also, it seems that metacognitive skills initially develop in separate domains, and later become generalized across domains (Veenman and Spaans 2005 ). However, future research needs to determine the processes that are responsible for this transfer across domains along the developmental trajectory. These processes raise issues related to transfer, connection between instruction and feedback provided by teachers, parents, and peers, and the development of these processes in formal and informal educational and workplace settings. The ubiquitous accessibility to different types of learning technologies (e.g., serious games, simulations, immersive virtual systems) raises the question as to whether the development of metacognition can be accelerated based on technology use in our society. Similarly, how does more ubiquitous technological accessibility impact the structure and development of metacognition?

Over the last decade major advances have been made in assessing metacognition . While self-report questionnaires, interviews, and observations are still widely used by researchers, there has been a recent surge in research using process-oriented methods such as combining concurrent think-louds, log files, eye tracking, screen recordings of learner-system interactions and so forth (Azevedo et al. 2019 ; Järvelä and Bannert 2020 ; Lajoie et al. 2020 ). These obtrusive and unobtrusive methods have been used extensively in laboratory studies but are slowly transitioning to real-world settings such as classrooms, homes, informal settings (e.g., museums, summer camps, after school programs), workplaces, and embedded in advanced learning technologies (e.g., instrumented participant using an intelligent tutoring system to learn about human body systems; Taub and Azevedo 2019 ), etc., where metacognitive processes can now be detected, measured, tracked, and modeled with more precision and accuracy. Despite the strengths of these new techniques and methods, researchers continue to grapple with issues of validity, reliability, missing data, instrument error, inferences about specific metacognitive processes based on multimodal data channels, temporally aligning data, mapping metacognitive processes to theoretically-based assumptions, etc. (Azevedo and Gasevic 2019 ; Graesser 2020 ; Hadwin 2020 ; Reimann 2020 ; Winne 2019 ). In addition to resolving these challenges, work also needs to be augmented by using multi-method designs that converge multiple sources of metacognitive data with studies that last longer than a few minutes, hours, or days (i.e., longitudinal studies would be ideal but are not always practical and are expensive to carry out). Lastly, the recent emergence in using data mining and machine learning to study metacognition (Biswas et al. 2018 ) has contributed immensely to understanding the nature of metacognitive processes, especially with advanced learning technologies.

There is a need to continue examining the relations between metacognition and individual differences . While there is abundant research on prior knowledge, metacognitive experiences, epistemological beliefs, metacognitive knowledge, and self-regulation, there is a need for more research on the role of motivational processes (e.g., self-efficacy and regulatory skills), emotional processes (e.g., emotion regulation skills), personality traits, working memory capacity, etc. Individual differences need to be considered when measuring, understanding, and predicting metacognition across learners of all ages as well as domains and tasks, and how this interacts with contextual variables.

Lastly, neuropsychological research on metacognition continues to be limited and focuses on very specific metacognitive processes (e.g., Rahnev and Fleming 2019 ). The field would benefit tremendously by increasing this kind of research extended to other components of metacognition and SRL such as planning, reflection, and so forth. Understanding the neural substrates of metacognition based on the localization and activation related to metacognitive functioning could significantly enhance our understanding of learners developing metacognition, augmenting the explanatory adequacy of a unified theory of metacognition, potentially having instructional and clinical benefits as well as providing real-time neural data to that can be used both by humans (e.g., learners, teachers, trainers) and machines to modify, enhance, or augment learning and performance through the use of sophisticated devices such as brain-computer interfaces.

In conclusion, the field of metacognition has and continues to make significant strides. There are many conceptual, theoretical, methodological, and analytical issues that still need to be addressed for the field to grow and be impactful. This is an exciting time as so many researchers across various disciplines focus on studying metacognition, but as exciting as that is, we must remain cautious to not repeat the above-mentioned issues so we can advance the field. We are living in an exhilarating time where, for example, using advanced learning technologies (e.g., virtual reality, augmented reality, extended reality, collaborative games, tangible computing) as they become increasingly more sophisticated and intelligent research and learning tools, are becoming capable of addressing most of the issues raised in this editorial.

Here is a brief scenario into the future of metacognition research using advanced learning technologies both as a research and learning tool. Imagine a collaborative intelligent virtual reality system with multiple intelligent virtual humans modeled after a new unified theory of metacognition. Each intelligent virtual human embodies the assumptions of the theory and can model several domain-general and domain-specific metacognitive processes. While the system provides the learner with complex and challenging STEM problems, each intelligent virtual human is capable of providing developmentally-appropriate metacognitive scaffolding based on each learner’s individual differences, and metacognitive developmental competencies. While they can each automatically detect and intelligently model temporally unfolding metacognitive processes (as well as other processes such as cognitive reappraisal), they can also consciously instruct and teach metacognitive knowledge and skills to learners using a multitude of approaches (e.g., modeling metacognitive conditional knowledge). Since the system is used throughout semester- or year-long interactions, the intelligent virtual humans can self-modify their own cognitive architectures based on their understanding of learners’ metacognition and their own metacognition. The methodological tools component of the VR system and its vast amount of trace and other data collected over time and across learners and problems can be used to address definitional, conceptual, theoretical, methodological, and instructional issues in the field. Given the importance of metacognition the field will continue to flourish as metacognition researchers adopt contemporary and emerging interdisciplinary methods, techniques, and tools to significantly advance the field.

Ackerman, R., & Thompson, V. (2017). Meta-reasoning: Monitoring and control of thinking and reasoning. Trends in Cognitive Sciences, 21 , 607–617.

Article   Google Scholar  

Azevedo, R., & Gasevic, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: issues and challenges. Computers in Human Behavior, 96 , 207–210.

Azevedo, R., Mudrick, N. V., Taub, M., & Bradbury, A. (2019). Self-regulation in computer-assisted learning systems. In J. Dunlosky & K. Rawson (Eds.), Handbook of cognition and education (pp. 587–618). Cambridge: Cambridge University Press.

Chapter   Google Scholar  

Biswas, G., Baker, R., & Paquette, L. (2018). Data mining methods for assessing self-regulated learning. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 388–404). New York: Routledge.

Google Scholar  

Callan, G., & Shim, S. (2019). How teachers define and identify self-regulated learning. The Teacher Educator, 54 , 295–312.

Dignath, C., & Buttner, G. (2018). Teachers’ direct and indirect promotion of self-regulated learning in primary and secondary school mathematics classes—Insights from video-based classroom observations and teacher interviews. Metacognition and Learning, 13 , 127–157.

Dunlosky, J., & Lipko, A. (2007). Metacomprehension: a brief history and how to improve its accuracy. Current Directions in Psychological Science, 16 , 228–232.

Dunlosky, J., & Rawson, K. (2019). (Eds.). The Cambridge handbook of cognition and education . Cambridge, MA: Cambridge University Press.

Efklides, A., Schwartz, B., & Brown, V. (2018). Motivation and affect in self-regulated learning: Does metacognition play a role? In D. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 64–82). New York: Routledge.

Graesser, A. C. (2020). Learning science principles and technologies with agents that promote deep learning. In R. S. Feldman (Ed.), Learning science: Theory, research, and practice (pp. 2–33). New York: McGraw-Hill.

Greene, J. A., Bolick, C. M., Jackson, W. P., Caprino, A. M., Oswald, C., & McVea, M. (2015). Domain-specificity of self-regulated learning processing in science and history digital libraries. Contemporary Educational Psychology, 42 , 111–128.

Griffin, T. D., Mielicki, M. K., & Wiley, J. (2019). Improving students’ metacomprehension accuracy. In J. Dunlosky, K. Rawson, & K. (Eds.), The Cambridge handbook of cognition and education (pp. 619–646). Cambridge: Cambridge University Press.

Hacker, D. (2018). A metacognitive model of writing: an updated from a development perspective. Educational Psychologist, 53 , 220–237.

Hacker, D., & Bol, L. (2019). Calibration and self-regulated learning: Making the connections. In J. Dunlosky & K. Rawson (Eds.), The Cambridge handbook of cognition and education (pp. 647–677). Cambridge: Cambridge University Press.

Hadwin, A. (2020). Commentary and future directions: what can multimodal data reveal about temporal and adaptive processes in self-regulated learning? Learning and Instruction .

Hadwin, A. F., Järvelä, S., & Miller, M. (2017). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 83–106). New York: Routledge.

Harris, K. R., & Graham, S. (2017). Self-regulated strategy development: theoretical bases, critical instructional elements, and future research. In R. Fidalgo, K. R. Harris, & M. Braaksma (Eds.), Design principles for teaching effective writing: Theoretical and empirical grounded principles (pp. 119–151). Leiden: Brill.

Hoyle, R., & Dent, A. (2018). Developmental trajectories of skills and abilities relevant for self-regulated learning and performance. In D. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 49–63). New York: Routledge.

Järvelä, S., & Bannert, M. (2020). Temporal and adaptive processes of regulated learning—What can multimodal data tell? Learning and Instruction .

Kleitman, S., & Narciss, S. (2019). Introduction to the special issue “applied metacognition: real-world applications beyond learning”. Metacognition and Learning, 14 , 335–342.

Koriat, A. (2015). Metacognition: decision-making processes in self-monitoring and self-regulation. In G. Keren & G. Wu (Eds.), The Wiley Blackwell handbook of judgment and decision making (Vol. 1, pp. 356–379). Malden: Wiley–Blackwell.

Kralik, J., Lee , J., Rosenbloom, P., Jackson, P., Epstein, S., Romero, O., Sanz , R., Larue, O., Schmidtke, H., Lee, S., & McGreggor, K. (2020). Metacognition for a common model of cognition. Procedia of Computer Science .

Kramarski, B. (2018). Teachers as agents in prompting students’ SRL and performance: applications for teachers’ dual role training programs. In D. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 223–240). New York: Routledge.

Kramarski, B., & Michalsky, T. (2013). Student and teacher perspectives on IMPROVE self-regulation prompts in web-based learning. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 35–51). Amsterdam: Springer.

Lajoie, S. P., Azevedo, R., Pekrun, R., & Leighton, J. (2020). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction .

Linnenbrink-Garcia, L., Patall, E., & Pekrun, R. (2016). Adaptive motivation and emotion in education: research and principles for instructional design. Policy Insights From the Behavioral and Brain Sciences, 3 , 228–236.

Metcalfe, J. (2009). Metacognitive judgments and control of study. Current Directions in Psychological Science, 18 , 159–163.

Nelson, T., & Narens, L. (1990). Metamemory: a theoretical framework and new findings. The psychology of learning and motivation (pp. 125–173). New York: Academic press.

Norman, E., Pfuhl, G., Saele, R., Svartdal, F., Låg, T., & Dahl, T. (2019). Metacognition in psychology. Review of General Psychology, 23 , 403–424.

Panadero, E. (2017). A review of self-regulated learning: six models and four directions for research. Frontiers in Psychology, 8 , 1–28.

Rahnev, D., & Fleming, S. M. (2019). How experimental procedures influence estimates of metacognitive ability. Neuroscience of Consciousness, 5 , 1–9.

Reimann, P. (2020). Methodological progress in the study of self-regulated learning enables theory advancement. Learning and Instruction .

Renninger, K. A., & Hidi, S. (2019). The Cambridge handbook of motivation and learning . Cambridge: Cambridge University Press.

Book   Google Scholar  

Roebers, C., & Spiess, M. (2017). The development of metacognitive monitoring and control in second graders: a short-term longitudinal study. Journal of Cognition and Development, 18 , 110–128.

Schneider, W., & Löffler, E. (2016). The development of metacognitive knowledge in children and adolescents. In J. Dunlosky & S. K. Tauber (Eds.), The Oxford handbook of metamemory (pp. 491–518). Oxford: Oxford University Press.

Schunk, D., & Greene, J. (2018). Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.

Tarricone, P. (2011). A taxonomy of metacognition . New York: Academic Press.

Taub, M., & Azevedo, R. (2019). How does prior knowledge influence fixations on and sequences of cognitive and metacognitive SRL processes during learning with an ITS? International Journal of Artificial Intelligence in Education, 29 , 1–28.

Veenman, M. V. J., & Spaans, M. A. (2005). Relation between intellectual and metacognitive skills: Age and task differences. Learning and Individual Differences, 15 , 159–176.

Veenman, M. V. J., Van Hout-Wolters, B., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1 , 3–14.

Winne, P. H. (2018). Cognition and metacognition within self-regulated learning. In D. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 254–270). New York: Routledge.

Winne, P. H. (2019). Paradigmatic dimensions of instrumentation and analytic methods in research on self-regulated learning. Computers in Human Behavior, 96 , 285–289.

Download references

Acknowledgements

The author would like to thank Elizabeth Cloude, Daryn Dever, Megan Wiedbusch, and Michelle Taub for comments on a previous version of this manuscript. The author would also like to thank the staff at Springer for their continued support of the journal.

This manuscript was supported by funding from the National Science Foundation (DRL#1661202, DUE#1761178, DRL#1916417, and IIS#1917728), Institute of Education Sciences (R305A170441), Social Sciences and Humanities Research Council of Canada (SSHRC 895–2011-1006), and the University of Central Florida (IR2). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation, Social Sciences and Humanities Research Council of Canada, Institute of Education Sciences or the University of Central Florida.

Author information

Authors and affiliations.

Department of Learning Sciences & Educational Research, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL, 32816, USA

Roger Azevedo

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Roger Azevedo .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Azevedo, R. Reflections on the field of metacognition: issues, challenges, and opportunities. Metacognition Learning 15 , 91–98 (2020). https://doi.org/10.1007/s11409-020-09231-x

Download citation

Published : 03 June 2020

Issue Date : August 2020

DOI : https://doi.org/10.1007/s11409-020-09231-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• CBE Life Sci Educ
• v.20(2); Summer 2021

Fostering Metacognition to Support Student Learning and Performance

Julie dangremond stanton.

† Department of Cellular Biology, University of Georgia, Athens, GA 30602

Amanda J. Sebesta

‡ Department of Biology, Saint Louis University, St. Louis, MO 63103

John Dunlosky

§ Department of Psychological Sciences, Kent State University, Kent, OH 44240

Associated Data

Metacognition is awareness and control of thinking for learning. Strong metacognitive skills have the power to impact student learning and performance. While metacognition can develop over time with practice, many students struggle to meaningfully engage in metacognitive processes. In an evidence-based teaching guide associated with this paper ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition ), we outline the reasons metacognition is critical for learning and summarize relevant research on this topic. We focus on three main areas in which faculty can foster students’ metacognition: supporting student learning strategies (i.e., study skills), encouraging monitoring and control of learning, and promoting social metacognition during group work. We distill insights from key papers into general recommendations for instruction, as well as a special list of four recommendations that instructors can implement in any course. We encourage both instructors and researchers to target metacognition to help students improve their learning and performance.

INTRODUCTION

Supporting the development of metacognition is a powerful way to promote student success in college. Students with strong metacognitive skills are positioned to learn more and perform better than peers who are still developing their metacognition (e.g., Wang et al. , 1990 ). Students with well-developed metacognition can identify concepts they do not understand and select appropriate strategies for learning those concepts. They know how to implement strategies they have selected and carry out their overall study plans. They can evaluate their strategies and adjust their plans based on outcomes. Metacognition allows students to be more expert-like in their thinking and more effective and efficient in their learning. While collaborating in small groups, students can also stimulate metacognition in one another, leading to improved outcomes. Ever since metacognition was first described ( Flavell, 1979 ), enthusiasm for its potential impact on student learning has remained high. In fact, as of today, the most highly cited paper in CBE—Life Sciences Education is an essay on “Promoting Student Metacognition” ( Tanner, 2012 ).

Despite this enthusiasm, instructors face several challenges when attempting to harness metacognition to improve their students’ learning and performance. First, metacognition is a term that has been used so broadly that its meaning may not be clear ( Veenman et al. , 2006 ). We define metacognition as awareness and control of thinking for learning ( Cross and Paris, 1988 ). Metacognition includes metacognitive knowledge , which is your awareness of your own thinking and approaches for learning. Metacognition also includes metacognitive regulation , which is how you control your thinking for learning ( Figure 1 ). Second, metacognition includes multiple processes and skills that are named and emphasized differently in the literature from various disciplines. Yet upon examination, the metacognitive processes and skills from different fields are closely related, and they often overlap (see Supplemental Figure 1). Third, metacognition consists of a person’s thoughts, which may be challenging for that person to describe. The tacit nature of metacognitive processes makes it difficult for instructors to observe metacognition in their students, and it also makes metacognition difficult for researchers to measure. As a result, classroom intervention studies of metacognition—those that are necessary for making the most confident recommendations for promoting student metacognition—have lagged behind foundational and laboratory research on metacognitive processes and skills.

Metacognition framework commonly used in biology education research (modified from Schraw and Moshman, 1995 ). This theoretical framework divides metacognition into two components: metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes what you know about your own thinking and what you know about strategies for learning. Declarative knowledge involves knowing about yourself as a learner, the demands of the task, and what learning strategies exist. Procedural knowledge involves knowing how to use learning strategies. Conditional knowledge involves knowing when and why to use particular learning strategies. Metacognitive regulation involves the actions you take in order to learn. Planning involves deciding what strategies to use for a future learning task and when you will use them. Monitoring involves assessing your understanding of concepts and the effectiveness of your strategies while learning. Evaluating involves appraising your prior plan and adjusting it for future learning.

We have created a teaching guide to address these challenges so that instructors can readily provide their students with evidence-based opportunities for practicing metacognition ( Figure 2 ). In an interdisciplinary collaboration, we drew heavily on robust metacognitive research from cognitive science, as well as studies from higher education and discipline-based education research. We worked to align the common aspects of two major metacognition frameworks ( Nelson and Narens, 1990 ; Schraw and Moshman, 1995 ) to guide our selections (see Figure 1 and Supplemental Figure 1). Our goal was to offer unifying terminology and the reasoning behind metacognition’s benefits to allow instructors to capitalize on the most promising findings from several disciplines. In this essay, we highlight the features of our interactive guide, which can be accessed at: https://lse.ascb.org/evidence-based -teaching-guides/student-metacognition . We also point to some of the important open questions in metacognition in each section. For example, as we think about metacognition generally, we encourage researchers to continue investigating the following questions:

• How do undergraduate students develop metacognitive skills?
• To what extent do active learning and generative work 1 promote metacognition?
• To what extent do increases in metacognition correspond to increases in achievement in science courses?

(A) Landing page for the Student Metacognition guide. The landing page provides a map with sections an instructor can click on to learn more about how to support students’ metacognition. (B) Example paper summary showing instructor recommendations. At the end of each summary in our guide, we used italicized text to point out what instructors should know based on the paper’s results.

The organization of this essay reflects the organization of our evidence-based teaching guide. In the guide, we first define terms and provide important background from papers that highlight the underpinnings and benefits of metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/benefits-definitions-underpinnings ). We then explore metacognition research by summarizing both classic and recent papers in the field and providing links for readers who want to examine the original studies. We consider three main areas related to metacognition: 1) student strategies for learning, 2) monitoring and control of learning, and 3) social metacognition during group work.

SUPPORTING STUDENTS TO USE EFFECTIVE LEARNING STRATEGIES

What strategies do students use for learning.

First our teaching guide examines metacognition in the context of independent study ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/supporting-student -learning-strategies ). When students transition to college, they have increased responsibility for directing their learning, which includes making important decisions about how and when to study. Students rely on their metacognition to make those decisions, and they also use metacognitive processes and skills while studying on their own. Empirical work has confirmed what instructors observe about their own students’ studying—many students rely on passive strategies for learning. Students focus on reviewing material as it is written or presented, as opposed to connecting concepts and synthesizing information to make meaning. Some students use approaches that engage their metacognition, but they often do so without a full understanding of the benefits of these approaches ( Karpicke et al. , 2009 ). Students also tend to study based on exam dates and deadlines, rather than planning out when to study ( Hartwig and Dunlosky, 2012 ). As a result, they tend to cram, which is also known in the literature as massing their study. Students continue to cram because this approach is often effective for boosting short-term performance, although it does not promote long-term retention of information.

Which Strategies Should Students Use for Learning?

Here, we make recommendations about what students should do to learn, as opposed to what they typically do. In our teaching guide, we highlight three of the most effective strategies for learning: 1) self-testing, 2) spacing, and 3) interleaving ( https://lse.ascb.org/evidence-based-teaching-guides/student -metacognition/supporting-student-learning-strategies/ #whatstudentsshould ). These strategies are not yet part of many students’ metacognitive knowledge, but they should know about them and be encouraged to use them while metacognitively regulating their learning. Students self-test when they use flash cards and answer practice questions in an attempt to recall information. Self-testing provides students with opportunities to monitor their understanding of material and identify gaps in their understanding. Self-testing also allows students to activate relevant knowledge and encode prompted information so it can be more easily accessed from their memory in the future ( Dunlosky et al. , 2013 ).

Students space their studying when they spread their learning of the same material over multiple sessions. This approach requires students to intentionally plan their learning instead of focusing only on what is “due” next. Spacing can be combined with retrieval practice , which involves recalling information from memory. For example, self-testing is a form of retrieval practice. Retrieval practice with spacing encourages students to actively recall the same content across several study sessions, which is essential for consolidating information from prior study periods ( Dunlosky et al. , 2013 ). Importantly, when students spread their learning over multiple sessions, they are less susceptible to superficial familiarity with concepts, which can mislead them into thinking they have learned concepts based on recognition alone ( Kornell and Bjork, 2008 ).

Students interleave when they alternate studying of information from one category with studying of information from another category. For example, when students learn categories of amino acid side groups, they should alternate studying nonpolar amino acids with polar amino acids. This allows students to discriminate across categories, which is often critical for correctly solving problems ( Rohrer et al. , 2020 ). Interleaving between categories also supports student learning because it usually results in spacing of study.

Most research has focused on what strategies students select and use for learning, but more work is needed to understand how students use those strategies ( Kuhbandner and Emmerdinger, 2019 ), and why they use them, which involves both metacognitive knowledge and metacognitive regulation ( Figure 1 ). The ways students enact the same learning strategy can differ greatly. For example, two students may report reading the textbook. The first student may be passively rereading entire textbook chapters, whereas the second student may be selectively reading passages of the text to clarify areas of confusion. Using open-ended instruments for collecting data will allow researchers to resolve contradictory findings on whether certain learning strategies support academic achievement. Three open research questions are:

• How are students enacting specific learning strategies, and do different students enact them in different ways?
• To what extent do self-testing, spacing, and interleaving support achievement in the context of undergraduate science courses?
• What can instructors do to increase students’ use of effective learning strategies?

What Factors Affect the Strategies Students Should Use to Learn?

Next, we examined the factors that affect what students should do to learn. Although we recommend three well-established strategies for learning, other appropriate strategies can vary based on the learning context. For example, the nature of the material, the type of assessment, the learning objectives, and the instructional methods can render some strategies more effective than others ( Scouller, 1998 ; Sebesta and Bray Speth, 2017 ). Strategies for learning can be characterized as deep if they involve extending and connecting ideas or applying knowledge and skills in new ways ( Baeten et al. , 2010 ). Strategies can be characterized as surface if they involve recalling and reproducing content. While surface strategies are often viewed negatively, there are times when these approaches can be effective for learning ( Hattie and Donoghue, 2016 ). For example, when students have not yet gained background knowledge in an area, they can use surface strategies to acquire the necessary background knowledge. They can then incorporate deep strategies to extend, connect, and apply this knowledge. Importantly, surface and deep strategies can be used simultaneously for effective learning. The use of surface and deep strategies ultimately depends on what students are expected to know and be able to do, and these expectations are set by instructors. Openly discussing these expectations with students can enable them to more readily select effective strategies for learning.

What Challenges Do Students Face in Using Their Metacognition to Enact Effective Strategies?

Students may encounter challenges in using metacognition to inform their learning. For instance, students may believe that evidence-based strategies do not work for them personally. Students can be provided with data showing increased performance after use of evidence-based strategies; however, instructors should note that the belief that evidence-based strategies do not work may persist even in the face of a student’s own data ( Roediger and Karpicke, 2006 ). In other cases, students continue to use approaches for learning that they know are not currently effective, especially if those approaches brought them success in the past. Students may be willing to change how they study, but they may need to develop accurate procedural knowledge , which involves knowing how to enact a strategy, or they may need to develop conditional knowledge , which involves knowing when and why a strategy is appropriate for a learning task ( Stanton et al. , 2015 ). To help students develop metacognitive knowledge in the form of conditional and procedural knowledge, instructors can model strategies that align with a learning task and give students opportunities to practice those strategies. In other cases, students may know how, when, and why they should use effective strategies, but they may decide not to use them because those strategies cause them discomfort ( Dye and Stanton, 2017 ). For example, self-testing may cause students discomfort because it requires greater cognitive effort compared with passively reviewing material for familiarity. Self-testing can also reveal gaps in understanding, which can cause stress for students who do not want to be confronted with what they do not know. Two important open questions are:

• How can students address challenges they will face when using effective—but effortful—strategies for learning?
• What approaches can instructors take to help students overcome these challenges?

ENCOURAGING STUDENTS TO MONITOR AND CONTROL THEIR LEARNING FOR EXAMS

Metacognition can be investigated in the context of any learning task, but in the sciences, metacognitive processes and skills are most often investigated in the context of high-stakes exams. Because exams are a form of assessment common to nearly every science course, in the next part of our teaching guide, we summarized some of the vast research focused on monitoring and control before, during, and after an exam ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/encouraging-students-monitor-control-learning ). In the following section, we demonstrate the kinds of monitoring and control decisions learners make by using an example of introductory biology students studying for an exam on cell division. The students’ instructor has explained that the exam will focus on the stages of mitosis and cytokinesis, and the exam will include both multiple-choice and short-answer questions.

How Should Students Use Metacognition while Preparing for and Taking an Exam?

As students prepare for an exam, they can use metacognition to inform their learning. Students can consider how they will be tested, set goals for their learning, and make a plan to meet their goals. It is expected that students who set specific goals while planning for an exam will be more effective in their studying than students who do not make specific goals. For example, a student who sets a specific goal to identify areas of confusion each week by answering end-of-chapter questions each weekend is expected to do better than a student who sets a more general goal of staying up-to-date on the material. Although some studies include goal setting and planning as one of many metacognitive strategies introduced to students, the influence of task-specific goal setting on academic achievement has not been well studied on its own in the context of science courses.

As students study, it is critical that they monitor both their use of learning strategies and their understanding of concepts. Yet many students struggle to accurately monitor their own understanding ( de Carvalho Filho, 2009 ). In the example we are considering, students may believe they have already learned mitosis because they recognize the terms “prophase,” “metaphase,” “anaphase,” and “telophase” from high school biology. When students read about mitosis in the textbook, processes involving the mitotic spindle may seem familiar because of their exposure to these concepts in class. As a result, students may inaccurately predict that they will perform well on exam questions focused on the mitotic spindle, and their overconfidence may cause them to stop studying the mitotic spindle and related processes ( Thiede et al. , 2003 ). Students often rate their confidence in their learning based on their ability to recognize, rather than recall, concepts.

Instead of focusing on familiarity, students should rate their confidence based on how well they can retrieve relevant information to correctly answer questions. Opportunities for practicing retrieval, such as self-testing, can improve monitoring accuracy. Instructors can help students monitor their understanding more accurately by encouraging students to complete practice exams and giving students feedback on their answers, perhaps in the form of a key or a class discussion ( Rawson and Dunlosky, 2007 ). Returning to the example, if students find they can easily recall the information needed to correctly answer questions about cytokinesis, they may wisely decide to spend their study time on other concepts. In contrast, if students struggle to remember information needed to answer questions about the mitotic spindle, and they answer these questions incorrectly, then they can use this feedback to direct their efforts toward mastering the structure and function of the mitotic spindle.

While taking a high-stakes exam, students can again monitor their performance on a single question, a set of questions, or an entire exam. Their monitoring informs whether they change an answer, with students tending to change answers they judge as incorrect. Accordingly, the accuracy of their monitoring will influence whether their changes result in increased performance ( Koriat and Goldsmith, 1996 ). In some studies, changing answers on an exam has been shown to increase student performance, in contrast to the common belief that a student’s first answer is usually right ( Stylianou-Georgiou and Papanastasiou, 2017 ). Changing answers on an exam can be beneficial if students return to questions they had low confidence in answering and make a judgment on their answers based on the ability to retrieve the information from memory, rather than a sense of familiarity with the concepts. Two important open questions are:

• What techniques can students use to improve the accuracy of their monitoring, while preparing for an exam and while taking an exam?
• How often do students monitor their understanding when studying on their own?

How Should Students Use Metacognition after Taking an Exam?

After completing any learning task, such as an exam, students can use the metacognitive regulation skill of evaluation , which entails assessing the effectiveness of their individual strategies and their overall plans for learning. Students generally do not need to evaluate in high school because they are able to perform well in many of their classes without studying ( McGuire, 2006 ). College science courses provide opportunities for developing evaluation skills because students use metacognition when they find learning tasks both challenging and important ( Carr and Taasoobshirazi, 2008 ). Undergraduates evaluate in response to novel challenges that occur when they encounter new learning experiences ( Dye and Stanton, 2017 ). For example, life science students report that non-math-based problem solving in organic chemistry courses caused them to carefully consider their strategies and plans for learning. When it comes to evaluating individual strategies for learning, senior-level students may use their knowledge of how people learn to evaluate their strategies (e.g., they may refer to neuroscience research to explain strategy effectiveness), whereas introductory students tend to evaluate strategies based on the similarity of study tools to exam questions ( Stanton et al. , 2019 ). When it comes to evaluating overall study plans, students tend to evaluate their plans based solely on their performance, rather than considering how well their plans met other goals they had for learning (e.g., being able to connect concepts). Providing students with answer keys that include explanations of the correct ideas and reflection questions can support students in evaluating their learning ( Sabel et al. , 2017 ). Students also tend to use their feelings of confidence or preparedness to evaluate their plans, but these feelings are subject to distortion ( Koriat and Bjork, 2005 ). Providing students with specific questions to answer about their study plans can help them focus on other aspects of effectiveness. Three open questions include:

• How do students develop metacognitive regulation skills such as evaluation?
• To what extent does the ability to evaluate affect student learning and performance?
• When students evaluate the outcome of their studying and believe their preparation was lacking, to what degree do they adopt more effective strategies for the next exam?

PROMOTING SOCIAL METACOGNITION DURING GROUP WORK

Next, our teaching guide covers a relatively new area of inquiry in the field of metacognition called social metacognition , which is also known as socially shared metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student -metacognition/promoting-social-metacognition -group-work ). Science students are expected to learn not only on their own, but also in the context of small groups. Understanding social metacognition is important because it can support effective student learning during collaborations both inside and outside the classroom. While individual metacognition involves awareness and control of one’s own thinking, social metacognition involves awareness and control of others’ thinking. For example, social metacognition happens when students share ideas with peers, invite peers to evaluate their ideas, and evaluate ideas shared by peers ( Goos et al. , 2002 ). Students also use social metacognition when they assess, modify, and enact one another’s strategies for solving problems ( Van De Bogart et al. , 2017 ). While enacting problem-solving strategies, students can evaluate their peers’ hypotheses, predictions, explanations, and interpretations. Importantly, metacognition and social metacognition are expected to positively affect one another ( Chiu and Kuo, 2009 ).

Students are likely to need structured guidance from instructors on how to be socially metacognitive while collaborating with their peers. Scripts for guiding collaboration provide students with metacognitive questions and prompts to support their work in groups. These scripts have been developed for undergraduate computer science and social science courses ( Miller and Hadwin, 2015 ). Yet, because social metacognition is context dependent, additional work is needed to evaluate the degree to which these scripts are effective in science courses, and if they are not effective, how to improve their efficacy. Given that social metacognition is a relatively new area of research, several open questions remain. For example,

• How do social metacognition and individual metacognition affect one another?
• How can science instructors help students to effectively use social metacognition during group work?

CONCLUSIONS

We encourage instructors to support students’ success by helping them develop their metacognition. Our teaching guide ends with an Instructor Checklist of actions instructors can take to include opportunities for metacognitive practice in their courses ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Student-Metacognition-Instructor-Checklist.pdf ). We also provide a list of the most promising approaches instructors can take, called Four Strategies to Implement in Any Course ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Four -Strategies-to-Foster-Student-Metacognition.pdf ). We not only encourage instructors to consider using these strategies, but given that more evidence for their efficacy is needed from classroom investigations, we also encourage instructors to evaluate and report how well these strategies are improving their students’ achievement. By exploring and supporting students’ metacognitive development, we can help them learn more and perform better in our courses, which will enable them to develop into lifelong learners.

Supplementary Material

Acknowledgments.

We are grateful to Cynthia Brame, Kristy Wilson, and Adele Wolfson for their insightful feedback on this paper and the guide. This material is based upon work supported in part by the National Science Foundation under grant number 1942318 (to J.D.S.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

1 Generative work “involves students working individually or collaboratively to generate ideas and products that go beyond what has been presented to them” ( Andrews et al. , 2019 , p2). Generative work is often stimulated by active-learning approaches.

• Andrews, T. C., Auerbach, A. J. J., Grant, E. F. (2019). Exploring the relationship between teacher knowledge and active-learning implementation in large college biology courses . CBE—Life Sciences Education , 18 ( 4 ), ar48. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baeten, M., Kyndt, E., Struyven, K., Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness . Educational Research Review , 5 ( 3 ), 243–260. [ Google Scholar ]
• Carr, M., Taasoobshirazi, G. (2008). Metacognition in the gifted: Connections to expertise . In Shaughnessy, M. F., Veenman, M., Kleyn-Kennedy, C. (Eds.), Meta-cognition: A recent review of research, theory and perspectives (pp. 109–125). Hauppauge, NY: Nova Sciences Publishers. [ Google Scholar ]
• Choi, M. M., Kuo, S. W. (2009). Social metacognition in groups: Benefits, difficulties, learning and teaching . In Larson, C. B. (Ed.), Metacognition: New research developments (pp. 117–136). Hauppauge, NY: Nova Science Publishers. [ Google Scholar ]
• Cross, D. R., Paris, S. G. (1988). Developmental and instructional analyses of children’s metacognition and reading comprehension . Journal of Educational Psychology , 80 ( 2 ), 131–142. [ Google Scholar ]
• de Carvalho Filho, M. K. (2009). Confidence judgments in real classroom settings: Monitoring performance in different types of tests . International Journal of Psychology , 44 ( 2 ), 93–108. [ PubMed ] [ Google Scholar ]
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology . Psychological Science in the Public Interest , 14 ( 1 ), 4–58. [ PubMed ] [ Google Scholar ]
• Dye, K. M., Stanton, J. D. (2017). Metacognition in upper-division biology students: Awareness does not always lead to control . CBE—Life Sciences Education , 16 ( 2 ), ar31. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Flavell, J. H. (1979). Meta-cognition and cognitive monitoring—New area of cognitive-developmental inquiry . American Psychologist , 34 ( 10 ), 906–911. [ Google Scholar ]
• Goos, M., Galbraith, P., Renshaw, P. (2002). Socially mediated metacognition: Creating collaborative zones of proximal development in small group problem solving . Educational Studies in Mathematics , 49 ( 2 ), 193–223. [ Google Scholar ]
• Hartwig, M. K., Dunlosky, J. (2012). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin & Review , 19 ( 1 ), 126–134. [ PubMed ] [ Google Scholar ]
• Hattie, J. A., Donoghue, G. M. (2016). Learning strategies: A synthesis and conceptual model . Nature Partner Journal Science of Learning , 1 ( 1 ), 1–13. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Karpicke, J. D., Butler, A. C., Roediger, H. L. , III. (2009). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory , 17 ( 4 ), 471–479. [ PubMed ] [ Google Scholar ]
• Koriat, A., Bjork, R. A. (2005). Illusions of competence in monitoring one’s knowledge during study . Journal of Experimental Psychology: Learning, Memory, Cognition , 31 ( 2 ), 187–194. [ PubMed ] [ Google Scholar ]
• Koriat, A., Goldsmith, M. (1996). Monitoring and control processes in the strategic regulation of memory accuracy . Psychological Review , 103 ( 3 ), 490–517. [ PubMed ] [ Google Scholar ]
• Kornell, N., Bjork, R. A. (2008). Learning concepts and categories is spacing the “enemy of induction”? Psychological Science , 19 ( 6 ), 585–592. [ PubMed ] [ Google Scholar ]
• Kuhbandner, C., Emmerdinger, K. J. (2019). Do students really prefer repeated rereading over testing when studying textbooks? A reexamination . Memory , 27 ( 7 ), 952–961. [ PubMed ] [ Google Scholar ]
• McGuire, S. Y. (2006). The impact of supplemental instruction on teaching students how to learn . New Directions for Teaching and Learning , 2006 ( 106 ), 3–10. [ Google Scholar ]
• Miller, M., Hadwin, A. F. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL . Computers in Human Behavior , 52 , 573–588. [ Google Scholar ]
• Nelson, T., Narens, L. (1990). Metamemory: A theoretical framework and new findings . Psychology of Learning and Motivation , 26 , 125–141. [ Google Scholar ]
• Rawson, K. A., Dunlosky, J. (2007). Improving students’ self-evaluation of learning for key concepts in textbook materials . European Journal of Cognitive Psychology , 19 ( 4–5 ), 559–579. [ Google Scholar ]
• Roediger, H. L., III, Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention . Psychological Science , 17 ( 3 ), 249–255. [ PubMed ] [ Google Scholar ]
• Rohrer, D., Dedrick, R. F., Hartwig, M. K., Cheung, C.-N. (2020). A randomized controlled trial of interleaved mathematics practice . Journal of Educational Psychology , 112 ( 1 ), 40. [ Google Scholar ]
• Sabel, J. L., Dauer, J. T., Forbes, C. T. (2017). Introductory biology students’ use of enhanced answer keys and reflection questions to engage in metacognition and enhance understanding . CBE—Life Sciences Education , 16 ( 3 ), ar40. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schraw, G., Moshman, D. (1995). Metacognitive theories . Educational Psychology Review , 7 ( 4 ), 351–371. [ Google Scholar ]
• Scouller, K. (1998). The influence of assessment method on students’ learning approaches: Multiple choice question examination versus assignment essay . Higher Education , 35 ( 4 ), 453–472. [ Google Scholar ]
• Sebesta, A. J., Bray Speth, E. (2017). How should I study for the exam? Self-regulated learning strategies and achievement in introductory biology . CBE—Life Sciences Education , 16 ( 2 ), ar30. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Stanton, J. D., Dye, K. M., Johnson, M. S. (2019). Knowledge of learning makes a difference: A comparison of metacognition in introductory and senior-level biology students . CBE—Life Sciences Education , 18 ( 2 ), ar24. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Stanton, J. D., Neider, X. N., Gallegos, I. J., Clark, N. C. (2015). Differences in metacognitive regulation in introductory biology students: When prompts are not enough . CBE—Life Sciences Education , 14 ( 2 ), ar15. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Stylianou-Georgiou, A., Papanastasiou, E. C. (2017). Answer changing in testing situations: The role of metacognition in deciding which answers to review . Educational Research and Evaluation , 23 ( 3–4 ), 102–118. [ Google Scholar ]
• Tanner, K. D. (2012). Promoting student metacognition . CBE—Life Sciences Education , 11 ( 2 ), 113–120. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Thiede, K. W., Anderson, M., Therriault, D. (2003). Accuracy of metacognitive monitoring affects learning of texts . Journal of Educational Psychology , 95 ( 1 ), 66–73. [ Google Scholar ]
• Van De Bogart, K. L., Dounas-Frazer, D. R., Lewandowski, H., Stetzer, M. R. (2017). Investigating the role of socially mediated metacognition during collaborative troubleshooting of electric circuits . Physical Review Physics Education Research , 13 ( 2 ), 020116. [ Google Scholar ]
• Veenman, M. V. J., van Hout-Wolters, B. H., Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations . Metacognition and Learning , 1 ( 1 ), 3–14. [ Google Scholar ]
• Wang, M. C., Haertel, G. D., Walberg, H. J. (1990). What influences learning? A content analysis of review literature . Journal of Educational Research , 84 ( 1 ), 30–43. [ Google Scholar ]

ORIGINAL RESEARCH article

Metacognitive strategies and development of critical thinking in higher education.

• 1 Departamento de Psicología Básica, Psicobiología y Metodología de CC, Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain
• 2 Departamento de Ciencias de la Educación, Facultad de Educación y Humanidades, Universidad del Bío-Bío, Sede Chillán, Chile

More and more often, we hear that higher education should foment critical thinking. The new skills focus for university teaching grants a central role to critical thinking in new study plans; however, using these skills well requires a certain degree of conscientiousness and its regulation. Metacognition therefore plays a crucial role in developing critical thinking and consists of a person being aware of their own thinking processes in order to improve them for better knowledge acquisition. Critical thinking depends on these metacognitive mechanisms functioning well, being conscious of the processes, actions, and emotions in play, and thereby having the chance to understand what has not been done well and correcting it. Even when there is evidence of the relation between metacognitive processes and critical thinking, there are still few initiatives which seek to clarify which process determines which other one, or whether there is interdependence between both. What we present in this study is therefore an intervention proposal to develop critical thinking and meta knowledge skills. In this context, Problem-Based Learning is a useful tool to develop these skills in higher education. The ARDESOS-DIAPROVE program seeks to foment critical thinking via metacognition and Problem-Based Learning methodology. It is known that learning quality improves when students apply metacognition; it is also known that effective problem-solving depends not only on critical thinking, but also on the skill of realization, and of cognitive and non-cognitive regulation. The study presented hereinafter therefore has the fundamental objective of showing whether instruction in critical thinking (ARDESOS-DIAPROVE) influences students’ metacognitive processes. One consequence of this is that critical thinking improves with the use of metacognition. The sample was comprised of first-year psychology students at Public University of the North of Spain who were undergoing the aforementioned program; PENCRISAL was used to evaluate critical thinking skills and the Metacognitive Activities Inventory (MAI) for evaluating metacognition. We expected an increase in critical thinking scores and metacognition following this intervention. As a conclusion, we indicate actions to incentivize metacognitive work among participants, both individually via reflective questions and decision diagrams, and at the interactional level with dialogues and reflective debates which strengthen critical thinking.

Introduction

One of the principal objectives which education must cover is helping our students become autonomous and effective. Students’ ability to use strategies which help them direct their motivation toward action in the direction of the meta-proposal is a central aspect to keep at the front of our minds when considering education. This is where metacognition comes into play—knowledge about knowledge itself, a component which is in charge of directing, monitoring, regulating, organizing, and planning our skills in a helpful way, once these have come into operation. Metacognition helps form autonomous students, increasing consciousness about their own cognitive processes and their self-regulation so that they can regulate their own learning and transfer it to any area of their lives. As we see, it is a conscious activity of high-level thinking which allows us to look into and reflect upon how we learn and to control our own strategies and learning processes. We must therefore approach a problem which is increasing in our time, that of learning and knowledge from the perspective of active participation by students. To achieve these objectives of “learning to learn” we must use adequate cognitive learning strategies, among which we can highlight those oriented toward self-learning, developing metacognitive strategies, and critical thinking.

Metacognition is one of the research areas, which has contributed the most to the formation of the new conceptions of learning and teaching. In this sense, it has advanced within the constructivist conceptions of learning, which have attributed an increasing role to student consciousness and to the regulation which they exercise over their own learning ( Glaser, 1994 ).

Metacognition was initially introduced by John Flavell in the early 1970s. He affirmed that metacognition, on one side, refers to “the knowledge which one has about his own cognitive processes products, or any other matter related with them” and on the other, “to the active supervision and consequent regulation and organization of these processes in relation with the objects or cognitive data upon which they act” ( Flavell, 1976 ; p. 232). Based on this, we can differentiate two components of metacognition: one of a declarative nature, which is metacognitive knowledge, referring to knowledge of the person and the task, and another of a procedural nature, which is metacognitive control or self-regulated learning, which is always directed toward a goal and controlled by the learner.

Different authors have pointed out that metacognition presents these areas of thought or skills, aimed knowledge or toward the regulation of thought and action, mainly proposing a binary organization in which attentional processes are oriented, on occasions, toward an object or subject, and the other hand, toward to interact with objects and/or subjects ( Drigas and Mitsea, 2021 ). However, it is possible to understand metacognition from another approach that establishes more levels of use of metacognitive thinking to promote knowledge, awareness, and intelligence, known as the eight pillars of metacognition model ( Drigas and Mitsea, 2020 ). These pillars allow thought to promote the use of deep knowledge, cognitive processes, self-regulation, functional adaptation to society, pattern recognition and operations, and even meaningful memorization ( Drigas and Mitsea, 2020 ).

In addition to the above, Drigas and Mitsea’s model establishes different levels where metacognition could be used, in a complex sequence from stimuli to transcendental ideas, in which each of the pillars could manifest a different facet of the process metacognitive, thus establishing a dialectical and integrative approach to learning and knowledge, allowing it to be understood as an evolutionary and complex process in stages ( Drigas and Mitsea, 2021 ).

All this clarifies the importance of and need for metacognition, not only in education but also in our modern society, since this need to “teach how to learn” and the capacity to “learn how to learn” in order to achieve autonomous learning and transfer it to any area of our lives will let us face problems more successfully. This becomes a relevant challenge, especially today where it is required to have a broad view regarding reflection and consciousness, and to transcend simplistic and reductionist models that seek to center the problem of knowledge only around the neurobiological or the phenomenological scope ( Sattin et al., 2021 ).

Critical thinking depends largely on these mechanisms functioning well and being conscious of the processes used, since this gives us the opportunity to understand what has not been done well and correct it in the future. Consciousness for critical thinking would imply a continuous process of reuse of thought, in escalations that allow thinking to be oriented both toward the objects of the world and toward the subjective interior, allowing to determine the ideas that give greater security to the person, and in that perspective, the metacognitive process, represents this use of Awareness, also allowing the generation of an identity of knowing being ( Drigas and Mitsea, 2021 ).

We know that thinking critically involves reasoning and deciding to effectively solve a problem or reach goals. However, effective use of these skills requires a certain degree of consciousness and regulation of them. The ARDESOS-DIAPROVE program seeks precisely to foment critical thinking, in part, via metacognition ( Saiz and Rivas, 2011 , 2012 , 2016 ).

However, it is not only centered on developing cognitive components, as this would be an important limitation. Since the 1990s, it has been known that non-cognitive components play a crucial role in developing critical thinking. However, there are few studies focusing on this relation. This intervention therefore considers both dimensions, where metacognitive processes play an essential role by providing evaluation and control mechanisms over the cognitive dimension.

Metacognition and Critical Thinking

Critical Thinking is a concept without a firm consensus, as there have been and still are varying conceptions regarding it. Its nature is so complex that it is hard to synthesize all its aspects in a single definition. While there are numerous conceptions about critical thinking, it is necessary to be precise about which definition we will use. We understand that “ critical thinking is a knowledge-seeking process via reasoning skills to solve problems and make decisions which allows us to more effectively achieve our desired results” ( Saiz and Rivas, 2008 , p. 131). Thinking effectively is desirable in all areas of individual and collective action. Currently, the background of the present field of critical thinking is also based in argumentation. Reasoning is used as the fundamental basis for all activities labeled as thinking. In a way, thinking cannot easily be decoupled from reasoning, at least if our understanding of it is “deriving something from another thing.” Inference or judgment is what we essentially find behind the concept of thinking. The question, though, is whether it can be affirmed that thinking is only reasoning. Some defend this concept ( Johnson, 2008 ), while others believe the opposite, that solving problems and making decisions are activities which also form part of thinking processes ( Halpern, 2003 ; Halpern and Dunn, 2021 , 2022 ). To move forward in this sense, we will return to our previous definition. In that definition, we have specified intellectual activity with a goal intrinsic to all mental processes, namely, seeking knowledge. Achieving our ends depends not only on the intellectual dimension, as we may need our motor or perceptive activities, so it contributes little to affirm that critical thinking allows us to achieve our objectives as we can also achieve them by doing other activities. It is important for us to make an effort to identify the mental processes responsible for thinking and distinguish them from other things.

Normally, we think to solve our problems. This is the second important activity of thought. A problem can be solved by reasoning, but also by planning course of action or selecting the best strategy for the situation. Apart from reasoning, we must therefore also make decisions to resolve difficulties. Choosing is one of the most frequent and important activities which we do. Because of this, we prefer to give it the leading role it deserves in a definition of thinking. Solving problems demands multiple intellectual activities, including reasoning, deciding, planning, etc. The final characteristic goes beyond the mechanisms peculiar to inference. What can be seen at the moment of delineating what it means to think effectively is that concepts are grouped together which go beyond the nuclear ideas of what has to do with inferring or reasoning. The majority of theoreticians in the field ( APA, 1990 ; Ennis, 1996 ; Halpern, 1998 , 2003 ; Paul and Elder, 2001 ; Facione, 2011 ; Halpern and Dunn, 2021 , 2022 ) consider that, in order to carry out this type of thinking effectively, apart from having this skill set, the intervention of other types of components is necessary, such as metacognition and motivation. This is why we consider it necessary to speak about the components of critical thinking, as we can see in Figure 1 :

Figure 1 . Components of critical thinking ( Saiz, 2020 ).

In the nature of thinking, there are two types of components: the cognitive and the non-cognitive. The former include perception, learning, and memory processes. Learning is any knowledge acquisition mechanism, the most important of which is thinking. The latter refer to motivation and interests (attitudes tend to be understood as dispositions, inclinations…something close to motives); with metacognition remaining as a process which shares cognitive and non-cognitive aspects as it incorporates aspects of both judgment (evaluation) and disposition (control/efficiency) about thoughts ( Azevedo, 2020 ; Shekhar and Rahnev, 2021 ). Both the cognitive and non-cognitive components are essential to improve critical thinking, as one component is incomplete without the other, that is, neither cognitive skills nor dispositions on their own suffice to train a person to think critically. In general, relations are bidirectional, although for didactic reasons only unidirectional relations appear in Figure 1 ( Rivas et al., 2017 ). This is because learning is a dynamic process which is subject to all types of influence. For instance, if a student is motivated, they will work more and better—or at least, this is what is hoped for. If they can achieve good test scores as well, it can be supposed that motivation is reinforced, so that they will continue existing behaviors in the same direction that is, working hard and well on their studies. This latter point appears to arise at least because of an adjustment between expectations and reality which the student achieves thanks to metacognition, which allows them to effectively attribute their achievements to their efforts ( Ugartetxea, 2001 ).

Metacognition, which is our interest in this paper, should also have bidirectional relations with critical thinking. Metacognition tends to be understood as the degree of consciousness which we have about our own mental processes and similar to the capacity for self-regulation, that is, planning and organization ( Mayor et al., 1993 ). We observe that these two ideas have very different natures. The former is simpler, being the degree of consciousness which we reach about an internal mechanism or process. The latter is a less precise idea, since everything which has to do with self-regulation is hard to differentiate from a way of understanding motivation, such as the entire tradition of intrinsic motivation and self-determination from Deci, his collaborators, and other authors of this focus (see, e.g., Deci and Ryan, 1985 ; Ryan and Deci, 2000 ). The important thing is to emphasize the executive dimension of metacognition, more than the degree of consciousness, for practical reasons. It can be expected that this dimension has a greater influence on the learning process than that of consciousness, although there is little doubt that we have to establish both as necessary and sufficient conditions. However, the data must speak in this regard. Due to all of this, and as we shall see hereinafter, the intervention designed incorporates both components to improve critical thinking skills.

We can observe, though, that the basic core of critical thinking continues to be topics related to skills, in our case, reasoning, problem-solving, and decision-making. The fact that we incorporate concepts of another nature, such as motivation, in a description of critical thinking is justified because it has been proven that, when speaking about critical thinking, the fact of centering solely on skills does not allow for fully gathering its complexity. The purpose of the schematic in Figure 2 is to provide conceptual clarity to the adjective “critical” in the expression critical thinking . If we understand critical to refer to effective , we should also consider that effectiveness is not, as previously mentioned, solely achieved with skills. They must be joined together with other mechanisms during different moments. Intellectual skills alone cannot achieve the effectiveness assumed within the term “critical.” First, for said skills to get underway, we must want to do so. Motivation therefore comes into play before skills and puts them into operation. For its part, metacognition allows us to take advantage of directing, organizing, and planning our skills and act once they have begun to work. Motivation thus activates our abilities, while metacognition lets them be more effective. The final objective should always be to gain proper knowledge of reality to resolve our problems.

Figure 2 . Purpose of critical thinking ( Saiz, 2020 , p.27).

We consider that the fact of referring to components of critical thinking while differentiating the skills of motivation and metacognition aids with the conceptual clarification we seek. On one side, we specify the skills which we discuss, and on another, we mention which other components are related to, and even overlap with them. We must be conscious of how difficult it is to find “pure” mental processes. Planning a course of action, an essential trait of metacognition, demands reflection, prediction, choice, comparison, and evaluation… And this, evidently, is thinking. The different levels or dimensions of our mental activity must be related and integrated. Our aim is to be able to identify what is substantial in thinking to know what we are able to improve and evaluate.

It is widely known that for our personal and professional functioning, thinking is necessary and useful. When we want to change a situation or gain something, all our mental mechanisms go into motion. We perceive the situation, identify relevant aspects of the problem, analyze all the available information, and appraise everything we analyze. We make judgments about the most relevant matters, decide about the options or pathways for resolution, execute the plan, obtain results, evaluate the results, estimate whether we have achieved our purpose and, according to the level of satisfaction following this estimation, consider our course of action good, or not.

The topic we must pose now is what things are teachable. It is useful to specify that what is acquired is clearly cognitive and some of the non-cognitive, because motivation can be stimulated or promoted, but not taught. The concepts of knowledge and wisdom are its basis. Mental representation and knowledge only become wisdom when we can apply it to reality, when we take it out of our mind and adequately situate it in the world. For our teaching purposes, we only have to take a position about whether knowledge is what makes critical thinking develop, or vice versa. For us, skills must be directly taught, and dominion is secondary. Up to now, we have established the components of critical thinking, but these elements still have to be interrelated properly. What we normally find are skills or components placed side by side or overlapping, but not the ways in which they influence each other. Lipman (2003) may have developed the most complete theory of critical and creative thinking, along Paul and his group, in second place, with their universal thought structures ( Paul and Elder, 2006 ). However, a proposal for the relation between the elements is lacking.

To try to explain the relation between the components of thought, we will use Figure 2 as an aid.

The ultimate goal of critical thinking is change that is, passing from one state of wellbeing into a better state. This change is only the fruit of results, which must be the best. Effectiveness is simple achieving our goals in the best way possible. There are many possible results, but for our ends, there are always some which are better than others. Our position must be for effectiveness, the best response, the best solution. Reaching a goal is resolving or achieving something, and for this, we have mechanisms available which tell us which are the best course of action. Making decisions and solving problems are fundamental skills which are mutually interrelated. Decision strategies come before a solution. Choosing a course of action always comes before its execution, so it is easy to understand that decisions contribute to solutions.

Decisions must not come before reflection, although this often can and does happen. As we have already mentioned, the fundamental skills of critical thinking, in most cases, have been reduced to reasoning, and to a certain degree, this is justified. There is an entire important epistemological current behind this, within which the theory of argumentation makes no distinction, at least syntactically, between argumentation and explanation. However, for us this distinction is essential, especially in practice ( Saiz, 2020 ). We will only center on an essential difference for our purpose. Argumentation may have to do with values and realities, but explanation only has to do with the latter. We can argue about beliefs, convictions, and facts, but we can only explain realities. Faced with an explanation of reality, any argumentation would be secondary. Thus, explanation will always be the central skill in critical thinking.

The change which is sought is always expressed in reality. Problems always are manifested and resolved with actions, and these are always a reality. An argument about realities aids in explaining them. An argument about values upholds a belief or a conviction. However, beliefs always influence behavior; thus, indirectly, the argument winds up being about realities. One may argue, for example, only for or against the death penalty, and reach the conviction that it is good or bad and ultimately take a position for or against allowing it. This is why we say that deciding always comes before resolving; furthermore, resolution always means deciding about something in a particular direction—it always means choosing and taking an option; furthermore, deciding is often only from two possibilities, the better or that which is not better, or which is not as good. Decisions are made based on the best option possible of all those which can be presented. Resolution is a dichotomy. Since our basic end lies within reality, explanation must be constituted as the basic pillar to produce change. Argumentation must therefore be at the service of causality (explanation), and both must be in the service of solid decisions leading us to the best solution or change of situation. We now believe that the relation established in Figure 2 can be better understood. From this relation, we propose that thinking critically means reaching the best explanation for an event, phenomenon, or problem in order to know how to effectively resolve it ( Saiz, 2017 , p.19). This idea, to our judgment, is the best summary of the nature of critical thinking. It clarifies details and makes explicit the components of critical thinking.

Classroom Activities to Develop Metacognition

We will present a set of strategies to promote metacognitive work in the classroom in this section, aimed at improving critical thinking skills. These strategies can be applied both at the university level and the secondary school level; we will thus focus on these two levels, although metacognitive strategies can be worked on from an earlier age ( Jaramillo and Osses, 2012 ; Tamayo-Alzate et al., 2019 ) and some authors have indicated that psychological maturity has a greater impact on effectively achieving metacognition ( Sastre-Riba, 2012 ; García et al., 2016 ).

At the individual level, metacognition can be worked on via applying questions aimed at the relevant tasks which must be undertaken regarding a task (meta-knowledge questions), for example:

- Do I know how much I know about this subject?

- Do I have clear instructions and know what action is expected from me?

- How much time do I have?

- Am I covering the proper and necessary subjects, or is there anything important left out?

- How do I know that my work is right?

- Have I covered every point of the rubric for the work to gain a good grade or a sufficient level?

These reflective questions facilitate supervising knowledge level, resource use, and the final product achieved, so that the decisions taken for said activities are the best and excellent learning results are achieved.

Graphs or decision diagrams can also be used to aid in organizing these questions during the different phases of executing a task (planning, progress, and final evaluation), which is clearly linked with the knowledge and control processes of metacognition ( Mateos, 2001 ). These diagrams are more complex and elaborate strategies than the questions, but are effective when monitoring the steps considered in the activity ( Ossa et al., 2016 ). Decision diagrams begin from a question or task, detailing the principal steps to take, and associating an alternative (YES or NO) to each step, which leads to the next step whenever the decision is affirmative, or to improve or go further into the step taken if the decision is negative.

Finally, we can work on thinking aloud, a strategy which facilitates making the thoughts explicit and conscious, allowing us to monitor their knowledge, decisions, and actions to promote conscious planning, supervision and evaluation ( Ávila et al., 2017 ; Dahik et al., 2019 ). For example:

- While asking a question, the student thinks aloud: I am having problems with this part of the task, and I may have to ask the teacher to know whether I am right.

Thinking aloud can be done individually or in pairs, allowing for active monitoring of decisions and questions arising from cognitive and procedural work done by the student.

Apart from the preceding strategies, it is also possible to fortify metacognitive development via personal interactions based on dialogue between both the students themselves and between the teacher and individual students. One initial strategy, similar to thinking out loud in pairs, is reflective dialogue between teacher and student, a technique which allows for exchanging deep questions and answers, where the student becomes conscious of their knowledge and practice thanks to dialogical interventions by the teacher ( Urdaneta, 2014 ).

Reflective dialogue can also be done via reflective feedback implemented by the teacher for the students to learn by themselves about the positive and negative aspects of their performance on a task.

Finally, another activity based on dialogue and interaction is related to metacognitive argumentation ( Sánchez-Castaño et al., 2015 ), a strategy which uses argumentative resources to establish a valid argumentative structure to facilitate responding to a question or applying it to a debate. While argumentative analysis is based on logic and the search for solid reasons, these can have higher or lower confidence and reliability as a function of the data which they provide. Thus, if a reflective argumentative process is performed, via questioning reasons or identifying counterarguments, there is more depth and density in the argumentative structure, achieving greater confidence and validity.

We can note that metacognition development strategies are based on reflective capacity, which allow thought to repeatedly review information and decisions to consider, without immediately taking sides or being carried away by superficial or biased ideas or data. Critical thought benefits strongly from applying this reflective process, which guides both data management and cognitive process use. These strategies can also be developed in various formats (written, graphic, oral, individual, and dialogical), providing teachers a wide range of tools to strengthen learning and thinking.

Metacognitive Strategies to Improve Critical Thinking

In this section, we will describe the fundamental metacognitive strategies addressed in our critical thinking skills development program ARDESOS-DIAPROVE.

First, one of the active learning methodologies applied is Problem-Based Learning (PBL). This pedagogical strategy is student-centered and encourages autonomous and participative learning, orienting students toward more active and decisive learning. In PBL each situation must be approached as a problem-solving task, making it necessary to investigate, understand, interpret, reason, decide, and resolve. It is presented as a methodology which facilitates joint knowledge acquisition and skill learning. It is also good for working on daily problems via relevant situations, considerably reducing the distance between learning context and personal/professional life and aiding the connection between theory and practice, which promote the highly desired transference. It favors organization and the capacity to decide about problem-solving, which also improves performance and knowledge about the students’ own learning processes. Because of all this, this methodology aids in reflection and analysis processes, which in turn promotes metacognitive skill development.

The procedure which we carried out in the classroom with all the activities is based on the philosophy of gradual learning control transference ( Mateos, 2001 ). During instruction, the teacher takes on the role of model and guide for students’ cognitive and metacognitive activity, gradually bringing them into participating in an increasing level of competency, and slowly withdrawing support in order to attain control over the students’ learning process. This methodology develops in four phases: (1) explicit instruction, where the teacher directly explains the skills which will be worked on; (2) guided practice, where the teacher acts as a collaborator to guide and aid students in self-regulation; and (3) cooperative practice, where cooperative group work facilitates interaction with a peer group collaborating to resolve the problem. By explaining, elaborating, and justifying their own points of view and alternative solutions, greater consciousness, reflection, and control over their own cognitive processes is promoted. Finally, (4) individual practice is what allows students to place their learning into practice in individual evaluation tasks.

Regarding the tasks, it is important to highlight that the activities must be aimed not only at acquiring declarative knowledge, but also at procedural knowledge. The objective of practical tasks, apart from developing fundamental knowledge, is to develop CT skills among students in both comprehension and expression in order to favor their learning and its transference. The problems used must be common situations, close to our students’ reality. The important thing in our task of teaching critical thinking is its usefulness to our students, which can only be achieved during application since we only know something when we are capable of applying it. We are not interested in students merely developing critical skills; they must also be able to generalize their intellectual skills, for which they must perceive them as useful in order to want to acquire them. Finally, they will have to actively participate to apply them to solving problems. Furthermore, if we study the different ways of reasoning without context, via overly academic problems, their application to the personal sphere becomes impossible, leading them to be considered hardly useful. This makes it important to contextualize skills within everyday problems or situations which help us get students to use them regularly and understand their usefulness.

Reflecting on how one carries things out in practice and analyzing mistakes are ways to encourage success and autonomy in learning. These self-regulation strategies are the properly metacognitive part of our study. The teacher has various resources to increase these strategies, particularly feedback oriented toward task resolution. Similarly, one of the most effective instruments to achieve it is using rubrics, a central tool for our methodology. These guides, used in student performance evaluations, describe the specific characteristics of a task at various performance levels, in order to clarify expectations for students’ work, evaluate their execution, and facilitate feedback. This type of technique also allows students to direct their own activity. We use them with this double goal in mind; on the one hand, they aid students in carrying out tasks, since they help divide the complex tasks they have to do into simpler jobs, and on the other, they help evaluate the task. Rubrics guide students in the skills and knowledge they need to acquire as well as facilitating self-evaluation, thereby favoring responsibility in their learning. Task rubrics are also the guide for evaluation which teachers carry out in classrooms, where they specify, review, and correctly resolve the tasks which students do according to the rubric criteria. Providing complete feedback to students is a crucial aspect for the learning process. Thus, in all sessions time is dedicated to carrying it out. This is what will allow them to move ahead in self-regulated skill learning.

According to what we have seen, there is a wide range of positions when it comes to defining critical thinking. However, there is consensus in the fact that critical thinking involves cognitive, attitudinal, and metacognitive components, which together favor proper performance in critical thinking ( Ennis, 1987 ; Facione, 1990 ). This important relation between metacognition and critical thinking has been widely studied in the literature ( Berardi-Coletta et al., 1995 ; Antonietti et al., 2000 ; Kuhn and Dean, 2004 ; Black, 2005 ; Coutinho et al., 2005 ; Orion and Kali, 2005 ; Schroyens, 2005 ; Akama, 2006 ; Choy and Cheah, 2009 ; Magno, 2010 ; Arslan, 2014 ) although not always in an applied way. Field studies indicate the existence of relations between teaching metacognitive strategies and progress in students’ higher-order thinking processes ( Schraw, 1998 ; Kramarski et al., 2002 ; Van der Stel and Veenman, 2010 ). Metacognition is thus considered one of the most relevant predictors of achieving a complex higher-order thought process.

Along the same lines, different studies show the importance of developing metacognitive skills among students as it is related not only with developing critical thinking, but also with academic achievement and self-regulated learning ( Klimenko and Alvares, 2009 ; Magno, 2010 ; Doganay and Demir, 2011 ; Özsoy, 2011 ). Klimenko and Alvares (2009) indicated that one way for students to acquire necessary tools to encourage autonomous learning is making cognitive and metacognitive strategies explicit and well-used and that teachers’ role is to be mediators and guides. Inspite of this evidence, there is less research about the use of metacognitive strategies in encouraging critical thinking. The principal reason is probably that it is methodologically difficult to gather direct data about active metacognitive processes which are complex by nature. Self-reporting is also still very common in metacognition evaluation, and there are few studies which have included objective measurements aiding in methodological precision for evaluating metacognition.

However, in recent years, greater importance has been assigned to teaching metacognitive skills in the educational system, as they aid students in developing higher-order thinking processes and improving their academic success ( Flavell, 2004 ; Larkin, 2009 ). Because of this, classrooms have seen teaching and learning strategies emphasizing metacognitive knowledge and regulation. Returning to our objective, which is to improve critical thinking via the ARDESOS-DIAPROVE program, we have achieved our goal in an acceptable way ( Saiz and Rivas, 2011 , 2012 , 2016 ).

However, we need to know which specific factors contribute to this improvement. We have covered significant ground through different studies, one of which we present here. In this one, we attempt to find out the role of metacognition in critical thinking. This is the central objective of the study. Our program includes motivational and metacognitive variables. Therefore, we seek to find out whether metacognition improves after this instruction program focused on metacognition. Therefore, our hypothesis is simple: we expect that the lesson will improve our students’ metacognition. The idea is to know whether applying metacognition helps us achieve improved critical thinking and whether after this change metaknowledge itself improves. In other words, improved critical thinking performance will make us think better about thinking processes themselves. If this can be improved, we can expect that in the future it will have a greater influence on critical thinking. The idea is to be able to demonstrate that applying specifically metacognitive techniques, the processes themselves will subsequently improve in quality and therefore contribute better volume and quality to reasoning tasks, decision-making and problem-solving.

Materials and Methods

Participants.

In the present study, we used a sample of 89 students in a first-year psychology course at Public University of the North of Spain. 82% (73) were women, and the other 18% (16) were men. Participants’ median age was 18.93 ( SD 1.744).

Instruments

Critical thinking test.

To measure critical thinking skills, we applied the PENCRISAL test ( Saiz and Rivas, 2008 ; Rivas and Saiz, 2012 ). The PENCRISAL is a battery consisting of 35 production problem situations with an open-answer format, composed of five factors: Deductive Reasoning , Inductive Reasoning , Practical Reasoning , Decision-Making , and Problem-Solving , with seven items per factor. Items for each factor gather the most representative structures of fundamental critical thinking skills.

The items’ format is open, so that the person has to answer a concrete question, adding a justification for the reasons behind their answer. Because of this, there are standardized correction criteria assigning values between 0 and 2 points as a function of answer quality. This test offers us a total score of critical thinking skills and another five scores referring to the five factors. The value range is located between 0 and 72 points as a maximum limit for total test scoring, and between 0 and 14 for each of the five scales. The reliability measures present adequate precision levels according to the scoring procedures, with the lowest Cronbach’s alpha values at 0.632, and the test–retest correlation at 0.786 ( Rivas and Saiz, 2012 ). PENCRISAL administration was done over the Internet via the evaluation platform SelectSurvey.NET V5: http://24.selectsurvey.net/pensamiento-critico/Login.aspx .

Metacognitive Skill Inventory

Metacognitive skill evaluation was done via the metacognitive awareness inventory from Schraw and Dennison (1994) (MAI; Huertas Bustos et al., 2014 ). This questionnaire has 52 Likert scale-type items with five points. The items are distributed in two general dimensions: cognitive knowledge (C) and regulation of cognition (R). This provides ample coverage for the two aforementioned ideas about metaknowledge. There are also eight defined subcategories within each general dimension. For C, these are: declarative knowledge (DK), procedural knowledge (PK), and conditional knowledge (CK). In R, we find: organization (O), monitoring (M), and evaluation (E). This instrument comprehensively, and fairly clearly, brings together essential aspects of metacognition. On one side, there is the level of consciousness, containing types of knowledge—declarative, procedural, and strategic. On the other, it considers everything important in the processes of self-regulation, planning, organization, direction or control (monitoring), adjustment (troubleshooting), and considering the results achieved (evaluation). It provides a very complete vision of everything important in this dimension. Cronbach’s alpha for this instrument is 0.94, showing good internal consistency.

Intervention Program

As previously mentioned, in this study, we applied the third version of the ARDESOS_DIAPROVE program ( Saiz and Rivas, 2016 ; Saiz, 2020 ), with the objective of improving thinking skills. This program is centered on directly teaching the skills which we consider essential to develop critical thinking and for proper performance in our daily affairs. For this, we must use reasoning and good problem-solving and decision-making strategies, with one of the most fundamental parts of our intervention being the use of everyday situations to develop these abilities.

DIAPROVE methodology incorporates three new and essential aspects: developing observation, the combined use of facts and deduction, and effective management of de-confirmation procedures, or discarding hypotheses. These are the foundation of our teaching, which requires specific teaching–learning techniques.

The intervention took place over 16 weeks and is designed to be applied in classrooms over a timeframe of 55–60 h. The program is applied in classes of around 30–35 students divided into groups of four for classwork in collaborative groups, and organized into six activity blocks: (1) nature of critical thinking, (2) problem-solving and effectiveness, (3) explanation and causality, (4) deduction and explanation, (5) argumentation and deduction, and (6) problem-solving and decision-making. These blocks are assembled maintaining homogeneity, facilitating a global integrated skill focus which helps form comprehension and use of the different structures in any situation as well as a greater degree of ability within the domain of each skill.

Our program made an integrated use of problem-based learning (PBL) and cooperative learning (CL) as didactic teaching and learning strategies in the critical thinking program. These methodologies jointly exert a positive influence on the students, allowing them to participate more actively in the learning process, achieve better results in contextualizing content and developing skills and abilities for problem-solving, and improve motivation.

To carry out our methodology in the classrooms, we have designed a teaching system aligned with these directives. Two types of tasks are done: (1) comprehension and (2) production. The materials we used to carry out these activities are the same for all the program blocks. One key element in our aim of teaching how to think critically must be its usefulness to our students, which is only achieved through application. This makes it important to contextualize reasoning types within common situations or problems, aiding students to use them regularly and understand their usefulness. Our intention with the materials we use is to face the problems of transference, usefulness, integrated skills, and how to produce these things. Accordingly, the materials used for the tasks are: (1) common situations and (2) professional/personal problems.

The tasks which the students perform take place over a week. They work in cooperative groups in class, and then review, correct, and clarify together, promoting reflection on their achievements and errors, which fortifies metacognition. Students get the necessary feedback on the work performed which will help them progressively acquire fundamental procedural contents. Our goal here is that students become conscious of their own thought processes in order to improve them. In this way, via the dialogue achieved between teachers and students as well as between the students themselves in their cooperative work, metacognition is developed. For conscious performance of tasks, the students will receive rubrics for each and every task to guide them in their completion.

Application of the ARDESOS-DIAPROVE program was done across a semester in the Psychology Department of the Public University of the North of Spain. One week before teaching began; critical thinking and metacognition evaluations were done. This was also done 1 week after the intervention ended, in order to gather the second measurement for PENCRISAL and MAI. The timelapse between the pre-treatment and post-treatment measurements was 4 months. The intervention was done by instructors with training and good experience in the program.

To test our objective, we used a quasi-experimental pre-post design with repeated measurements.

Statistical Analysis

For statistical analysis, we used the IBM SPSS Statistics 26 statistical packet. The statistical tools and techniques used were: frequency and percentage tables for qualitative variables, exploratory and descriptive analysis of quantitative variables with a goodness of fit test to the normal Gaussian model, habitual descriptive statistics (median, SD, etc.) for numerical variables, and Student’s t -tests for significance of difference.

To begin, a descriptive analysis of the study variables was carried out. Tables 1 , 2 present the summary of descriptions for the scores obtained by students in the sample, as well as the asymmetry and kurtosis coefficients for their distribution.

Table 1 . Description of critical thinking measurement (PENCRISAL).

Table 2 . Description of metacognition measurement (MAI).

As we see in the description of all study variables, the evidence is that the majority of them adequately fit the normal model, although some present significant deviations which can be explained by sample size.

Next, to verify whether there were significant differences in the metacognition variable based on measurements before and after the intervention, we contrasted medians for samples related with Student’s t -test (see Table 3 ).

Table 3 . Comparison of the METAKNOWLEDGE variable as a function of PRE-POST measurements.

The results show that there are significant differences in the metaknowledge scale total and in most of its dimensions, where all the post medians for both the scale overall and for the three dimensions of the knowledge factor (declarative, procedural, and conditional) are higher than the pre-medians. However, in the cognition regulation dimension, there are only significant differences in the total and in the planning, organization, and monitoring dimensions. The medians are also greater in the post-test than the pre-test. However, the troubleshooting and evaluation dimensions do not differ significantly after intervention.

Finally, for critical thinking skills, the results show significant differences in the scale total and in the five factors regarding the measurement time, where performance medians rise after intervention (see Table 4 ).

Table 4 . Comparison of the CRITICAL THINKING variable as a function of PRE-POST measurements.

These results show how metacognition improves due to CT intervention, as well as how critical thinking also improves with metacognitive intervention and CT skills intervention. Thus, it improves how people think about thinking as well as about the results achieved, since metacognition supports decision-making and final evaluation about proper strategies to solve problems.

Discussion and Conclusions

The general aim of our study was to know whether a critical thinking intervention program can also influence metacognitive processes. We know that our teaching methodology improves cross-sectional skills in argumentation, explanation, decision-making, and problem-solving, but we do not know if this intervention also directly or indirectly influences metacognition. In our study, we sought to shed light on this little-known point. If we bear in mind the centrality of how we think about thinking for our cognitive machinery to function properly and reach the best results possible in the problems we face, it is hard to understand the lack of attention given to this theme in other research. Our study aimed to remedy this deficiency somewhat.

As said in the introduction, metacognition has to do with consciousness, planning, and regulation of our activities. These mechanisms, as understood by many authors, have a blended cognitive and non-cognitive nature, which is a conceptual imprecision; what is known, though, is the enormous influence they exert on fundamental thinking processes. However, there is a large knowledge gap about the factors which make metacognition itself improve. This second research lacuna is what we have partly aimed to shrink here as well with this study. Our guide has been the idea of knowing how to improve metacognition from a teaching initiative and from the improvement of fundamental critical thinking skills.

Our study has shed light in both directions, albeit in a modest way, since its design does not allow us to unequivocally discern some of the results obtained. However, we believe that the data provide relevant information to know more about existing relations between skills and metacognition, something which has seen little contrast. These results allow us to better describe these relations, guiding the design of future studies which can better discern their roles. Our data have shown that this relation is bidirectional, so that metacognition improves thinking skills and vice versa. It remains to establish a sequence of independent factors to avoid this confusion, something which the present study has aided with to be able to design future research in this area.

As the results show, total differences in almost all metaknowledge dimensions are higher after intervention; specifically, we see how in the knowledge factor the declarative, procedural, and conditional dimensions improve in post-measurements. This improvement moves in the direction we predicted. However, the cognitive regulation dimension only shows differences in the total, and in the planning, organization, and regulation dimensions. We can see how the declarative knowledge dimensions are more sensitive than the procedural ones to change, and within the latter, the dimensions over which we have more control are also more sensitive. With troubleshooting and evaluation, no changes are seen after intervention. We may interpret this lack of effects as being due to how everything referring to evaluating results is highly determined by calibration capacity, which is influenced by personality factors not considered in our study. Regarding critical thinking, we found differences in all its dimensions, with higher scores following intervention. We can tentatively state that this improved performance can be influenced not only by interventions, but also by the metacognitive improvement observed, although our study was incapable of separating these two factors, and merely established their relation.

As we know, when people think about thinking they can always increase their critical thinking performance. Being conscious of the mechanisms used in problem-solving and decision-making always contributes to improving their execution. However, we need to go into other topics to identify the specific determinants of these effects. Does performance improve because skills are metacognitively benefited? If so, how? Is it only the levels of consciousness which aid in regulating and planning execution, or do other factors also have to participate? What level of thinking skills can be beneficial for metacognition? At what skill level does this metacognitive change happen? And finally, we know that teaching is always metacognitive to the extent that it helps us know how to proceed with sufficient clarity, but does performance level modify consciousness or regulation level of our action? Do bad results paralyze metacognitive activity while good ones stimulate it? Ultimately, all of these open questions are the future implications which our current study has suggested. We believe them to be exciting and necessary challenges, which must be faced sooner rather than later. Finally, we cannot forget the implications derived from specific metacognitive instruction, as presented at the start of this study. An intervention of this type should also help us partially answer the aforementioned questions, as we cannot obviate what can be modified or changed by direct metacognition instruction.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SR and CS contributed to the conception and design of the study. SR organized the database, performed the statistical analysis, and wrote the first draft of the manuscript. SR, CS, and CO wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.

This study was partly financed by the Project FONDECYT no. 11220056 ANID-Chile.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Akama, K. (2006). Relations among self-efficacy, goal setting, and metacognitive experiences in problem solving. Psychol. Rep. 98, 895–907. doi: 10.2466/pr0.98.3.895-907

PubMed Abstract | CrossRef Full Text | Google Scholar

Antonietti, A., Ignazi, S., and Perego, P. (2000). Metacognitive knowledge about problem solving methods. Br. J. Educ. Psychol. 70, 1–16. doi: 10.1348/000709900157921

APA (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction. Executive Summary “The Delphi Report.”

Google Scholar

Arslan, S. (2014). An investigation of the relationships between metacognition and self-regulation with structural equation. Int. Online J. Educ. Sci. 6, 603–611. doi: 10.15345/IOJES.2014.03.009

CrossRef Full Text | Google Scholar

Ávila, M., Bianchetti, M., and González, A. (2017). Uso del método “Think Aloud” en la investigación cualitativa. Pistas Educ. 39, 26–38.

Azevedo, R. (2020). Reflections on the field of metacognition: issues, challenges, and opportunities. Metacogn. Learn. 15, 91–98. doi: 10.1007/s11409-020-09231-x

Berardi-Coletta, B., Buyer, L. S., Dominowski, R. L., and Rellinger, E. R. (1995). Metacognition and problem solving: a process-oriented approach. J. Consult. Clin. Psychol. 21, 205–223.

Black, S. (2005). Teaching students to think critically. Educ. Digest 70, 42–47.

Choy, S. C., and Cheah, P. K. (2009). Teacher perceptions of critical thinking among students and its influence on higher education. Int. J. Teach. Learn. Higher Educ. 20, 198–206.

Coutinho, S., Wiemer-Hastings, K., Skowronski, J. J., and Britt, M. A. (2005). Metacognition, need for cognition and use of explanations during ongoing learning and problem solving. Learn. Individ. Differ. 15, 321–337. doi: 10.1016/j.lindif.2005.06.001

Dahik, S., Cáneppa, C., Dahik, C., and Feijoò, K. (2019). Estrategias de Think-Aloud para mejorar la habilidad de lectura en estudiantes en el centro de idiomas en la universidad técnica de Babahoyo. Rev. Magaz. Ciencias 4, 65–83. doi: 10.5281/zenodo.3239552

Deci, E. L., and Ryan, R. M. (1985). The general causality orientations scale: self-determination in personality. J. Res. Pers. 19, 109–134. doi: 10.1016/0092-6566(85)90023-6

Doganay, A., and Demir, O. (2011). Comparison of the level of using metacognitive strategies during study between high achieving and low achieving prospective teachers. Educ. Sci. Theor. Pract. 11, 2036–2043.

Drigas, A., and Mitsea, E. (2020). The 8 pillars of metacognition. Int. J. Emerg. Technol. Learn. 15, 162–178. doi: 10.3991/ijet.v15i21.14907

Drigas, A., and Mitsea, E. (2021). 8 pillars X 8 layers model of metacognition: educational strategies, exercises and trainings. Int. J. Online Biomed. Eng. 17, 115–134. doi: 10.3991/ijoe.v17i08.23563

Ennis, R. H. (1987). “A taxonomy of critical thinking dispositions and abilities,” in Teaching Thinking Skills. eds. J. B. Baron and R. J. Sternberg (New York: Freeman and Company), 9–26.

Ennis, R. H. (1996). Critical Thinking. Upper Saddle River, NJ: Prentice-Hall

Facione, P. A. (1990). Critical Thinking: A Statement of expert consensus for Purposes of Educational Assessment and Instruction—Executive Summary of the delphi Report. Millbrae: California Academic Press

Facione, P. A. (2011). Think Critically. New York: Prentice-Hall.

Flavell, J. H. (1976). “Metacognitive aspects of problem solving,” in The Nature of Intelligence. ed. L. B. Resnik (Hillsdale, N.J: Erlbaum), 231–235.

Flavell, J. H. (2004). Theory of the mind development: retrospect and prospect. Merrill-Palmer Q. 50, 274–290. doi: 10.1353/mpq.2004.0018

García, T., Rodríguez, C., González-Castro, P., Álvarez-García, D., and González-Pienda, J.-A. (2016). Metacognición y funcionamiento ejecutivo en Educación Primaria [Metacognition and executive functioning in Elementary School]. Ann. Psychol. 32, 474–483. doi: 10.6018/analesps.32.2.202891

Glaser, R. (1994). “Learning theory and instruction,” in International Perspectives on Psychological Science. Vol. 2. eds. G. D’Ydewalle, P. Eelen, and B. Bertelson (NJ: Erlbaum)

Halpern, D. (1998). Teaching critical thinking for transfer across domains—dispositions, skills, structure training, and metacognitive monitoring. Am. Psychol. 53, 449–455. doi: 10.1037/0003-066X.53.4.449

Halpern, D. (2003). Halpern Critical Thinking Assessment Using Everyday Situations: Background and Scoring Standards. Claremont, CA: Claremont McKenna College.

Halpern, D. F., and Dunn, D. S. (2021). Critical thinking: A model of intelligence for solving real-world problems. J. Intellig. 9:22. doi: 10.3390/jintelligence9020022

Halpern, D. F., and Dunn, D. S. (2022). Thought and Knowledge. An Introduction to Critical-Thinking . 6th Edn. New York: Taylor and Francis.

Huertas Bustos, A. P., Vesga Bravo, G. J., and Gilando León, M. (2014). Validación del instrumento “Inventario de Habilidades Metacognitivas (MAI)” con estudiantes colombianos. Praxis Saber 5, 55–74. doi: 10.19053/22160159.3022

Jaramillo, S., and Osses, S. (2012). Validación de un Instrumento sobre Metacognición para Estudiantes de Segundo Ciclo de Educación General Básica. Estud. Pedag. 38, 117–131. doi: 10.4067/S0718-07052012000200008

Johnson, R. H. (2008). “Critical thinking, logic and argumentation,” in Paper presented at the Conferencia Internacional: Lógica, Argumentación y Pensamiento Crítico. Santiago de Chile. January 8–11.

Klimenko, O., and Alvares, J. L. (2009). Aprender cómo aprendo: la enseñanza de estrategias metacognitivas. Educ. Educ. 12, 11–28.

Kramarski, B., Mevarceh, Z. R., and Arami, M. (2002). The effect of metacognitive instruction on solving mathematical authentic tasks. Educ. Stud. Math. 49, 225–250. doi: 10.1023/A:1016282811724

Kuhn, D., and Dean, D. (2004). Metacognition: a bridge between cognitive psychology and educational practice. Theory Pract. 43, 268–274. doi: 10.1207/s15430421tip4304_4

Larkin, S. (2009). Metacognition in Young Children. New York, NY: Routledge

Lipman, M. (2003). Thinking in Education (2nd Edn.). Cambridge, MA: Cambridge University Press

Magno, C. (2010). The role of metacognitive skills in developing critical thinking. Metacogn. Learn. 5, 137–156. doi: 10.1007/s11409-010-9054-4

Mateos, M. (2001). Metacognición y Educación. Buenos Aires: Aique

Mayor, J., Suengas, A., and González Marqués, J. (1993). Estrategias Metacognitivas. Aprender a Aprendery Aprender a Pensar. Madrid: Síntesis

Orion, N., and Kali, Y. (2005). The effect of an earth-science learning program on students’ scientific thinking skills. J. Geosci. Educ. 53, 387–394. doi: 10.5408/1089-9995-53.4.387

Ossa, C., Rivas, S.F., and Saiz, C. (2016). Estrategias metacognitivas en el desarrollo del análisis argumentativo En IV Seminário Internacional Cognição, aprendizagem e desempenho. eds. J. Casanova, C. Bisinoto, and L. Almeida (Braga: Livro de atas), 30–47.

Özsoy, G. (2011). An investigation of the relationship between metacognition and mathematics achievement. Asia Pac. Educ. Rev. 12, 227–235. doi: 10.1007/s12564-010-9129-6

Paul, R., and Elder, L. (2001). Critical Thinking Handbook: Basic Theory and Instructional Structures. Dillon Beach, CA: Foundation for Critical Thinking

Paul, R., and Elder, A. D. (2006). Critical Thinking. Learn the Tools the Best Thinkers Use. Upper Saddle River, NJ: Pearson/Prentice Hall

Rivas, S. F., and Saiz, C. (2012). Validación y propiedades psicométricas de la prueba de pensamiento crítico PENCRISAL . Rev. Electrón. Metodol. Aplic. 17, 18–34.

Rivas, S. F., Saiz, C., and Ossa, C. (2017). “ Desarrollo de las estrategias metacognitivas mediante el programa de instrucción en pensamiento crítico ARDESOS .” in II Seminario Internacional de 660 Pensamiento Crítico. Manizales (Colombia). de octubre de 11–13, 2017.

Ryan, R. M., and Deci, E. L. (2000). Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol. 21, 54–67. doi: 10.1006/ceps.1999.1020

Saiz, C. (2017). Pensamiento Crítico y Cambio. Madrid: Pirámide.

Saiz, C. (2020). Pensamiento Crítico y Eficacia. 2ª Edn. Madrid: Pirámide.

Saiz, C., and Rivas, S. F. (2008). Evaluación en pensamiento crítico: una propuesta para diferenciar formasde pensar. Ergo. Nueva Época 22-23, 25–66.

Saiz, C., and Rivas, S. F. (2011). Evaluation of the ARDESOS program: an initiative to improve criticalthinking skills. J. Scholar. Teach. Learn. 11, 34–51.

Saiz, C., and Rivas, S. F. (2012). Pensamiento crítico y aprendizaje basado en problemas. Rev. Docenc. Univ. 10, 325–346. doi: 10.4995/redu.2012.6026

Saiz, C., and Rivas, S. F. (2016). New teaching techniques to improve critical thinking. DIAPROVE Methodol. 40, 3–36.

Sánchez-Castaño, J. A., Castaño-Mejía, O. Y., and Tamayo-Alzate, O. E. (2015). La argumentación metacognitiva en el aula de ciencias. Rev. Latin. Cienc. Soc. 13, 1153–1168. doi: 10.11600/1692715x.13242110214

Sastre-Riba, S. (2012). Alta capacidad intelectual: perfeccionismo y regulación metacognitiva. Rev. Neurol. 54, S21–S29. doi: 10.33588/rn.54S01.2012011

Sattin, D., Magnani, F. G., Bartesaghi, L., Caputo, M., Fittipaldo, A. V., Cacciatore, M., et al. (2021). Theoretical models of consciousness: a scoping review. Brain Sci. 11:535. doi: 10.3390/brainsci11050535

Schraw, G. (1998). Promoting general metacognitive awareness. Instr. Sci. 26, 113–125. doi: 10.1023/A:1003044231033

Schraw, G., and Dennison, R. (1994). Assessing metacognitive awareness. Contemp. Educ. Psychol. 19, 460–475. doi: 10.1006/ceps.1994.1033

Schroyens, W. (2005). Knowledge and thought: an introduction to critical thinking. Exp. Psychol. 52, 163–164. doi: 10.1027/1618-3169.52.2.163

Shekhar, M., and Rahnev, D. (2021). Sources of metacognitive inefficiency. Trends Cogn. Sci. 25, 12–23. doi: 10.1016/j.tics.2020.10.007

Tamayo-Alzate, O., Cadavid-Alzate, V., and Montoya-Londoño, D. (2019). Análisis metacognitivo en estudiantes de básica, durante la resolución de dos situaciones experimentales en la clase de Ciencias Naturales. Rev. Colomb. Educ. 76, 117–141. doi: 10.17227/rce.num76-4188

Ugartetxea, J. (2001). Motivación y metacognición, más que una relación. Relieve 7:4442. doi: 10.7203/relieve.7.2.4442

Urdaneta, M. (2014). Diálogo para la reflexión: compartiendo la experiencia de aula desde el proyecto pedagógico. Innov. Educ. 16, 43–49. doi: 10.22458/ie.v16i21.902

Van der Stel, M., and Veenman, M. V. J. (2010). Development of metacognitive skillfulness: a longitudinal study. Learn. Individ. Differ. 20, 220–224. doi: 10.1016/j.lindif.2009.11.005

Keywords: critical thinking, instruction, evaluation, metacognition, problem-solving

Citation: Rivas SF, Saiz C and Ossa C (2022) Metacognitive Strategies and Development of Critical Thinking in Higher Education. Front. Psychol . 13:913219. doi: 10.3389/fpsyg.2022.913219

Received: 05 April 2022; Accepted: 19 May 2022; Published: 15 June 2022.

Reviewed by:

Copyright © 2022 Rivas, Saiz and Ossa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Silvia F. Rivas, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Maintenance work is planned for Wednesday 1st May 2024 from 9:00am to 11:00am (BST).

During this time, the performance of our website may be affected - searches may run slowly and some pages may be temporarily unavailable. If this happens, please try refreshing your web browser or try waiting two to three minutes before trying again.

We apologise for any inconvenience this might cause and thank you for your patience.

Chemistry Education Research and Practice

Factors that influence general chemistry students’ decision making in study strategies.

* Corresponding authors

a Department of Chemistry, University of South Florida, Tampa, FL, USA E-mail: [email protected]

b Department of Mathematics and Science Education, Zonguldak Bülent Ecevit University, Kdz. Ereğli, 67300 Zonguldak, Turkey

This qualitative study delves into the intricate landscape of general chemistry students' study strategy decision-making processes, examining the guiding factors that shape their choices. Past work in chemistry education has shown that students’ study behaviors are dynamic in nature. Employing self-regulation theory, the study aims to provide a deeper understanding of how students decide to maintain or change their study behaviors. Semi-structured interviews were conducted to capture the study processes of nine students enrolled in first-semester general chemistry classroom. The results indicated these students’ study behavior decision-making process was either driven by metacognition or affect. Students who adopted metacognitive decision-making showed evidence of enactment of declarative, procedural, and conditional knowledge which could be influenced by either the nature of the content studied (content-driven), or the time-efficiency of the strategies employed (time-driven) during their self-regulation. On the contrary, students who adopted affective decision-making based their choices regarding their study behaviors on the emotional aspects and the value they attribute to the study strategies (intrinsic-value or instrumental-value driven). The findings of the study are foundational yet highlight the nuanced nature of changes and constancy within the study strategy decision-making process. This suggests a one-size-fits-all approach to improve student study behaviors may not yield fruitful outcomes and therefore, distinct methods should be devised to reach students with different decision-making processes.

Article information

Download citation, permissions.

P. Nayyar, B. Demirdöğen and S. E. Lewis, Chem. Educ. Res. Pract. , 2024, Advance Article , DOI: 10.1039/D4RP00046C

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page .

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page .

Read more about how to correctly acknowledge RSC content .

Social activity

Search articles by author.

This article has not yet been cited.

Advertisements