study habits research

The Science of Habit and Its Implications for Student Learning and Well-being

• Review Article
• Published: 17 March 2020
• Volume 32 , pages 603–625, ( 2020 )

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• Logan Fiorella 1  

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Habits are critical for supporting (or hindering) long-term goal attainment, including outcomes related to student learning and well-being. Building good habits can make beneficial behaviors (studying, exercise, sleep, etc.) the default choice, bypassing the need for conscious deliberation or willpower and protecting against temptations. Yet educational research and practice tends to overlook the role of habits in student self-regulation, focusing instead on the role of motivation and metacognition in actively driving behavior. Habit theory may help explain ostensible failures of motivation or self-control in terms of contextual factors that perpetuate poor habits. Further, habit-based interventions may support durable changes in students’ recurring behaviors by disrupting cues that activate bad habits and creating supportive and stable contexts for beneficial ones. In turn, the unique features of educational settings provide a new area in which to test and adapt existing habit models.

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Ironically, one of the few articles on habits in Educational Psychology Review is an interview with the most productive educational psychologists, who cite consistent work habits as important for maintaining research productivity and work-life balance (Flanigan et al. 2018 ; see also Kiewra and Creswell 2000 ; Patterson-Hazley and Kiewra 2013 ). Accounts of writers, artists, musicians, and scientists concur that habits and ritual set the foundation for creativity and productivity (Currey 2013 , 2019 ).

The amount of repetition ultimately required to form a habit likely depends on the complexity of the habit (Mullan and Novoradovskaya 2018 ) and the suitability of the performance context (Wood 2019 ).

The term “study habits” is often defined broadly to include frequency of using various techniques, without specifying the nature or stability of specific context cues or the automaticity of the behavior. For example, Crede and Kuncel ( 2008 ) define study habits as “sound study routines, including but not restricted to, frequency of studying sessions, review of material, self-testing, rehearsal of learned material, and studying in a conductive environment” (p. 429).

Adriaanse, M. A., Kroese, F. M., Gillebaart, M., & De Ridder, D. T. D. (2014). Effortless inhibition: habit mediates the relation between self-control and healthy snack consumption. Frontiers in Psychology, 5 , 444.

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Acknowledgments

I thank Wendy Wood and one anonymous reviewer for their constuctive feedback and suggestions. I also thank Deborah Barany, Qian Zhang, and Michele Lease for their helpful comments on an earlier draft of this article. Finally, I thank the students from my First Year Odyssey Seminar at the University of Georgia, Applying the Science of Habit, for their valuable insight into the role of habits in their lives.

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Fiorella, L. The Science of Habit and Its Implications for Student Learning and Well-being. Educ Psychol Rev 32 , 603–625 (2020). https://doi.org/10.1007/s10648-020-09525-1

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Examining study habits in undergraduate STEM courses from a situative perspective

• Matthew T. Hora 1 &
• Amanda K. Oleson 2  

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A growing body of research in cognitive psychology and education research is illuminating which study strategies are effective for optimal learning, but little descriptive research focuses on how undergraduate students in STEM courses actually study in real-world settings. Using a practice-based approach informed by situated cognition theory, we analyzed data from 61 STEM students about their study habits.

Results indicate that studying is a multi-faceted process that is initiated by instructor- or self-generated cues, followed by marshaling resources and managing distractions, and then implementing study behaviors that include selecting a social setting and specific strategies. Variations in some study behaviors are also evident according to the timing of their studying (e.g., cramming), course level, discipline, and social setting. Three cases of individual student practices reveal how studying is also shaped by how the course is designed and taught, students’ own beliefs about studying, and aspects of their personal lives.

Conclusions

The results indicate that studying involves various social, digital, and curricular resources, that many students persist in utilizing low-impact study strategies (e.g., re-reading text), and that the use of study strategies varies across different situations. We suggest that the focus on changing teaching behaviors that is dominant within STEM education be broadened to include a focus on instructional design that supports student self-regulatory behaviors and the adoption of high-impact study strategies.

Graphical abstract

The stages of studying across three cases: Brianna, Larry, and Angelica.

As concerns mount regarding the quality of undergraduate education, particularly in the science, technology, engineering, and mathematics (STEM) fields, policymakers, educators, and student affairs professionals are increasingly focusing on how to support student learning throughout their academic careers. Given that students’ academic success is shaped by a complex matrix of psychological, cultural, and organizational factors, scholars are investigating a variety of issues that may impact student success including underlying psychological attributes such as engagement (Carini et al. 2006 ) and perseverance or “grit” (Duckworth et al. 2007 ), what instructors believe about teaching and learning (Hativa and Goodyear 2002 ), and the types of teaching methods used in the classroom (Freeman et al. 2014 ). However, while these areas of research shed light on key aspects of student learning, these foci overlook a key piece of the student learning puzzle—what students actually do when they leave the classroom and study.

A considerable body of literature exists on college student study skills and habits, with foci on students’ cognitive styles and approaches to learning (Biggs 1987 ; Riding and Cheema 1991 ), the use of specific study techniques (Karpicke et al. 2009 ) and the role of study habits and time spent studying on overall student achievement (Nonis and Hudson 2010 ; Robbins et al 2004 ). Investigating the nature of study habits is important because factors related to studying such as motivation and specific study techniques have been linked to academic success. In a meta-analysis of 72, 431 students, Credé and Kuncel ( 2008 ) found that motivation and study skills (e.g., time management) were positively associated with grade point average and grades in individual courses. Furthermore, a comprehensive review of research on specific study strategies found that some (e.g., distributed practice) led to learning gains whereas others (e.g., re-reading text) did not (Dunlosky et al. 2013 ) and that many college students are not employing these study habits (Hartwig and Dunlosky 2012 ), and understanding why students persist in using ineffective study practices and how to change this state of affairs, from a situative perspective, is of particular importance to the field of STEM education. Thus, knowing whether or not (and why) students are using these practices is important information for instructors and student affairs/academic advising professionals.

Yet for the field of postsecondary education in general, and STEM education in particular, relatively little is known about student study habits, largely due to the lack of robust descriptive research that accounts for students’ behaviors in real-world settings. The gaps in the literature are twofold. First, much of the research on studying is based on survey research or experimental studies of specific study strategies, with few qualitative, descriptive studies of how students actually study in real-world situations. Such an approach to research, that focuses on descriptive accounts of naturalistic behaviors in order to inform educational programming and reforms, is becoming increasingly important in research on reform implementation in both K-12 and postsecondary contexts (Hora 2016 ; Coburn and Turner 2012 ; Spillane et al. 2002 ). Second is view of study habits as decontextualized, not shaped by social, curricular, situation; given insights from situated cognition research on how activity and learning itself is “distributed, stretched over (and) not divided among” mind, tools, and social and organizational contexts (Lave 1988 , p. 1), and that decision-making and behavior cannot be properly understood without close attention to the naturalistic settings in which they unfold (Klein 2008 ), the reliance on decontextualized survey research for insights into study habits is no longer tenable.

In this exploratory study, we utilize a practice-based approach to focus on the actual study behaviors of 61 undergraduates at three research universities in the USA and Canada who were enrolled in biology, physics, earth science, and mechanical engineering courses. Drawing upon situated cognition theory to conceptualize studying as a behavior that encompasses individual study strategies as they unfold in specific social, technological, and institutional contexts, we analyze data using inductive thematic analysis from 22 focus groups, and these students provided detailed information about their study habits that allowed us to answer the following research questions: (1) What behaviors do students taking undergraduate STEM courses engage in when studying? (2) What underlying contextual factors, if any, influence these behaviors?

We pursued this line of research because while the question “How can we teach students if we do not know how they learn?” (Coffield et al. 2004 , p. 1) is important, we also wonder “How can we best support student success if we do not understand how they study?” Insights gleaned from the data presented in this paper, which indicate that studying is a complex, multi-dimensional practice that implicates cues, social resources, artifacts, and study strategies, can provide faculty and student affairs professionals with a new way to think about studying that extends the prior focus on specific, decontextualized study strategies.

Discussions regarding the state of undergraduate education in the early twenty-first century often focus on the role of the instructor and their pedagogical acumen in the classroom (e.g., Bok 2009 ). Indeed, much of the focus in the STEM education literature is on how to affect changes in faculty teaching practices and philosophies about student learning (PCAST 2012 ). While instructors certainly play an important role in facilitating student learning by crafting experiences that engage students in these ways (or not), researchers have long questioned whether enough attention has been placed on the other actor involved in the learning enterprise—the student. As Entwistle and Tait ( 1990 , p. 170) observed, student behaviors are “part of a broader academic environment which affects learning probably as much as, if not more than, the classroom skills of the lecturer.” According to this view, the student as an agent actively engaged in his or her own learning and overall experience in college is a central, if not primary, part of the teaching and learning equation that is too often overlooked.

Psychological approaches to understanding study habits and academic success

In early research on the change processes that young people undergo while in college (Pascarella and Terenzini 2005 ) and the factors that contribute to students’ lack of persistence (Tinto 1993 ), higher education scholars have paid particularly close attention to the psychological factors that shape students’ experiences and ultimate success (or lack thereof). For instance, attributes associated with academic success such as involvement (Astin 1984 ) and engagement have been used to explain students’ relative success in their academic coursework (Carini et al. 2006 ). An underlying assumption in this literature is that students’ mental stances or psychological attributes play a major role in their academic outcomes and that higher education professionals should support them by facilitating higher degrees of involvement and engagement to increase their prospects for success.

Another line of inquiry has focused on subconscious psychological traits associated with student learning including cognitive styles and approaches to learning (see Coffield et al. 2004 for a review). For example, researchers have argued that people have stable cognitive styles or “typical or habitual mode(s) of problem solving, thinking, perceiving, and remembering” that shape how they think and learn (Riding and Cheema 1991 , p. 194). Another commonly used construct is that of approaches to studying, which refers to more elastic, changeable approaches and preferences that learners have for studying and learning (Entwistle and Tait 1990 ). Early work in this area argued for the existence of two distinct approaches to learning whose basic outlines persist to the present time: deep approaches to learning that involve searching for meaning and surface approaches that involve rote memorization (Marton and Säljö 1976 ; Biggs 1987 ). While such approaches to learning are theorized as being relatively stable within an individual, they can change over time with concerted effort. Furthermore, these psychological attributes should not be considered as operating independently from the context in which studying occurs (Ramsden 1979 ). In fact, early research in this area found that some students actively sought information in the environment (e.g., textbooks, lecture content) and then studied using what was called “cue-seeking” behavior, whereas others were more “cue-deaf” or worked to succeed without seeking hints about exams (Miller and Parlett 1974 ).

This focus on the origins of student’s motivation to initiate studying is similar to a long-standing line of inquiry that examines the degree to which learners are able and willing to assume control of their own learning process or what is known as self-regulated learning (Zimmerman and Schunk 2001 ). A self-regulated learner engages in a process of initiating the learning process on their own, setting goals, identifying appropriate strategies, and reflecting on his or her own task performance—all of which ultimately leads to a decision to enact changes in future behaviors or to maintain current practices (Cassidy 2011 ). Self-regulated learning is a particularly valuable idea in college student success, with empirical research in this area indicating that students who exhibit high degrees of self regulation have higher rates of academic achievement as measured by persistence and grades (Boekaerts and Corno 2005 ).

Research on study habits and skills

Another body of literature that examines studying focuses directly on the study habits and skills that students utilize during the act of studying itself. However, what at first glance may appear to be a straightforward, easily defined term is operationalized in a variety of ways in the literature. For instance, Robbins et al. ( 2004 , p. 276) define study skills as “activities necessary to organize and complete schoolwork tasks and to prepare for and take tests” and operationalize the construct using measures including time management, leadership skills, communication skills, and the un-defined category of “study skills and habits” (see also Credé and Kuncel 2008 ; Lotkowski et al. 2004 ). Other scholars have defined study habits in different ways, including the ability to concentrate, the scheduling of regular review sessions, and hours spend studying (Nonis and Hudson 2010 ). Conceptualizing study habits in terms of time spent studying is rather common, and a widely cited report by Babcock and Marks ( 2010 ) found that hours spent studying has declined from 24 h a week in 1961 to 14 h a week in 2003. In 2009, the picture was bleaker, with over half of freshmen who took the Your First College Year Survey and over half of seniors who took the College Senior Survey spending 10 h or less per week studying or doing homework (Ruiz et al. 2010 ; Franke et al. 2010 ).

While these studies capture important facets of studying and the role that they play in student achievement and persistence, the specific strategies and actions students actually engage in during their study sessions remain obscured. Providing more clarity on specific study habits, Karpicke et al. ( 2009 ) found that the preferred study strategy of 84% of the surveyed undergraduates was re-reading textbooks and lecture notes. Unfortunately, a study examining the utility of 10 learning techniques in the empirical literature found that habits such as these considered low utility in regard their impact on student learning, in contrast to high-utility techniques such as practice testing and distributed practice (i.e., taking tests over time), thus suggesting that many undergraduates utilize study habits that are ineffective (Dunlosky et al 2013 ).

Given the ubiquity of the Internet and digital media in many people’s lives, researchers are also investigating how these artifacts are being used as study aids. In one study exploring student utilization of digital and “traditional” resources, researchers found that 39 and 44% of students search Wikipedia and Google, respectively, if they need help with coursework, with only 36% seeking out a faculty member (Morgan et al. 2012 ). Similarly, a 2010 study of 36,950 undergraduates found that 33% used wikis, 24% used video-sharing websites, and 12% used blogging tools (Smith and Caruso 2010 ). Besides these more traditional digital media, including course websites hosted on institutional learning management systems, some argue that other tools that facilitate personalized learning (Dabbagh and Kitsantas 2012 ) and digitally mediated social learning via open Internet-based resources (Seely Brown and Adler 2008 ) are under-utilized in higher education. Researchers are also examining how digital media can inhibit studying, however, and Rosen et al. ( 2013 ) found middle-school, high-school, and undergraduate students were unable to remain on task for even 6 min before being tempted by Facebook or texting when studying at home.

However, the literature on study skills, strategies, and habits is limited by a tendency to reduce the complex and multi-faceted behaviors that comprise studying to metrics that cannot capture how and why students study (i.e., hours spent studying) or focus on strategies (e.g., re-reading) at the expense of other possible behaviors or choices students make. Perhaps the single largest limitation, however, is the lack of attention paid to the contexts within which students actually study. While some scholars have focused on the environmental contexts of studying (Kuo et al. 2004 ) and the interaction among study habits and social factors (Treisman 1992 ; Robbins et al 2004 ), few recent studies have attempted to describe studying behaviors as a multi-faceted process that includes not only study strategies but also how situations and resources are implicated in these practices.

This is important because educational practice, whether a group of undergraduates studying for a biology course or an administrator finalizing a budget, should not be thought of solely in terms of an individual making decisions in isolation, as the context of decision-making as well as tools and other artifacts utilized as part of the process is critically important. In studying the practices of principals in K-12 settings, for example, researchers have utilized theoretical frameworks from situated and distributed cognition which assert that the institutional context is not a mere backdrop for activity but is instead an integral feature of individual cognition and decision-making as well as task performance itself (Halverson 2003 ; Hora 2012 ; Spillane et al 2002 ). In this study, we draw upon these frameworks to conceptualize studying as the discrete behaviors of individuals (e.g., reviewing notes) as they unfold within specific contexts and that implicate particular artifacts and resources.

Why does the lack of descriptive research on student study habits that adopt a situative perspective matter? Because fine-grained descriptions of people’s behaviors in specific contexts and situations illuminates the specific steps people take when solving problems or performing tasks—information that can then be used by instructors and educational leaders to improve their practices and design more locally attuned interventions (Coburn and Turner 2012 ; Spillane et al. 2001 ). Educational researchers across the K-16 spectrum have argued that more practice-based research should be conducted on the various behaviors associated with teaching and learning, so that how and why educators and students make decisions in “the wild” of schools, colleges, and universities can illuminate barriers and supports to effective practice, rather than simply prescribing how people should think and act regardless of the situation (Bastedo 2012 ). With such a comprehensive and multi-faceted approach to describing studying, we set out to document the study habits of 61 undergraduates taking STEM courses as a corrective to the focus on both teaching and study strategies alone, in the hopes that such accounts could inform ways that educators can improve student learning and academic success.

Exploratory research is intended to examine poorly understood phenomena and generate new insights and hypotheses that can guide future research on the topic (Slavin 2002 ; Stebbins 2001 ). In this exploratory study, we examine the study habits of a group of STEM students, with a focus on describing the lived experiences and subjective interpretations of individuals and groups or what cultural anthropologists call an “emic” account of social life (Merriam 2014 ). The study took place at three large, public research universities in the USA and Canada that had similar undergraduate populations (approximately 25,000 students). These sites were selected due to the presence of instructional reform initiatives, which was a criterion for the larger study on STEM instructors’ data driven decision-making upon which this analysis is based. The disciplines included in this study are biology, geology, physics, and mechanical engineering based on the STEM-related focus of the larger study. For this study, a non-random purposive sampling procedure was used to identify faculty study participants. Faculty were included in the study population if they were listed as instructors in each institution’s course listings for the 2013 spring semester. We contacted 165 instructors via email requesting their participation in the study, and 59 participated (36% response rate). Thus, the faculty whose classes were recruited into the study were unique in that they were self-selected and taught undergraduate STEM courses at large research universities.

These instructors represented the initial pool of courses from which we selected student participants for the focus groups. We selected the focus group technique in order to collect a large amount of qualitative, in-depth data in a shorter amount of time than would be possible with individual interviews (Bernard 2011 ). Of the 59 faculty who participated in the larger study, we asked 30 instructors they would recruit students for participation in focus groups, of which 22 instructors agreed. The 30 courses (and instructors) selected for recruitment represented the largest courses across all four of the disciplines included in the study, which increased the prospects of recruiting sufficient numbers of students. Those instructors sent email requests to their classes, and students contacted the research team if they were interested in participation. There was a $20 incentive, and 61 students participated (see Table  1 ).

Data collection

A team of four researchers conducted the student focus group interviews using a semi-structured interview protocol, with each group led by one or two moderators depending upon scheduling constraints. The key question posed to participants in the focus groups was: “Please imagine for a moment how you typically study for this course —can you describe in as much detail as possible your study situation?” This question was followed by probes regarding the types of materials used for studying, whether participants studied alone or with others, and any additional details not yet described. While the open-ended nature of the questions resulted in detailed observations about study practices, it also led to idiosyncratic accounts that were not always comparable across individuals. We also did not provide a definition for the act of “studying” during the focus groups, which was based on our goal of capturing students’ own unique perceptions about what behaviors and situations constituted a study session. Each focus group included between two and six students and lasted approximately 45 min. These focus groups were audio recorded and transcribed.

Data analysis

Transcripts were entered into NVivo qualitative analysis software and then segmented into manageable units or discrete statements by participants that encapsulated a single thought or idea (Gee 1986 ). First, a code list was created to segment the data that aligned with the research questions guiding the analysis. We were interested in segments related to “study strategies” and “study situations,” and thus, any utterances pertaining to these two categories were sought out. Both analysts reviewed five transcripts with these two codes in mind and highlighted text fragments related to both codes and then met to ensure a common understanding of the relationship between the codes and the raw data. Upon ensuring that the codes were being applied similarly, the second author then segmented the remainder of the dataset. Second, we followed a structured approach to grounded theory that involved using a combination of a pre-existing “coding paradigm” and the inductive analysis of transcripts to develop a code list with which to analyze the entire dataset. The second author developed a preliminary code list using an inductive open-coding approach where terms or ideas mentioned by study participants themselves (e.g., re-reading textbooks) were used to create code names (Glaser and Strauss 1967 ) while the research questions and theoretical framework were also kept in mind (Strauss and Corbin 1990 ). After developing the initial code list, we met to discuss the codes and revised them while reviewing text fragments and discussing the applicability of codes to the data. During this process, we attempted to derive codes that maintained as much fidelity to participants’ own language and descriptions of study behaviors as possible.

The second author then developed the final code list using the constant comparative method, where each occurrence of a code was compared to each previous instance of that code in order to confirm or alter the code and/or its definition (Glaser and Strauss 1967 ), after which the final code list was applied to the entire dataset. At this point in the analytic process, qualitative researchers have the option of reporting recurrent themes with or without numeric counts of their prevalence. In this paper, where all study participants responded to questions in a similar fashion (e.g., specific study strategies), we elected to report the number of times a code was applied to the raw data in order to convey to readers the frequency with which a particular behavior or observation was identified in the data. In other cases where responses were more ambiguous and/or where different respondents interpreted questions differently, we report recurrent themes instead of numeric counts.

The data were also entered into a data matrix with subjects as rows and study cues, resources, and strategies as columns. These data were analyzed using exploratory data reduction methods (i.e., hierarchical cluster analysis and multi-dimensional scaling) to see if patterns across the data could be discerned. Clear patterns were not discernable, so these data were then organized to report the frequency with which particular strategies were used according to different groups of students (e.g., discipline, social situation). The results reported in this paper depict the percentage of students within each group reporting each strategy, with results weighted according to the size of each respective group.

Next, we analyzed two students and one entire focus group who provided particularly rich details about their study habits in order to depict how studying unfolds in real-world settings at the individual level. These subjects were selected because of the level of detail they provided when self-reporting their conceptions of what studying means, the contexts in which their studying occurred, and their actual study behaviors. These case studies also highlight the situated nature of studying in the influence of peers, curricular artifacts, and other features of the environment on their study habits. Finally, we examined the resulting themes to explore any patterns in the data and identified a new way of thinking about studying that is reported in this paper.

Limitations to this study include the self-selected nature of the sample, both of the participating instructors and students, that limits generalizability of the findings to broader populations of undergraduates. Such limitations to generalizability are an inherent part of research using small, non-randomly selected samples, but their strength is in illuminating behaviors at a fine-grained level. While future research involving larger samples will be necessary to assess how widespread the behaviors reported in this paper truly are, the data do raise questions and considerations about studying that can be applied to different institutions. Another limitation is that the focus group method may introduce an element of self-censoring and social desirability bias by participants due to the public nature of the setting, which can result in incomplete or inaccurate answers to the facilitator’s questions. Finally, because participants discussed their studying with varying degrees of specificity it was difficult at times to ascertain whether similar behaviors were being reported. The limitations associated with social desirability and the veracity of students’ accounts could not be overcome with the current study, though future work should consider incorporating an observational component to corroborate self-reported behaviors with actual practice.

Before reporting data addressing the research questions guiding the study, we first discuss how respondents had differing notions of what activities constituted “studying.” For some, it meant any exposure to course material such as attending a class, whereas for others, studying implied completing assigned tasks. In yet other cases studying referred to activities that were not assigned and took place outside of class. As one student said, “I see studying more as something that I do separate from any assigned material.” In addition to these task-oriented conceptions, some reported “folk” theories of the learning or ideas about phenomena that are not necessarily grounded in evidence. For example, one student stated, “Studying to me means stressing out your brain so that it realizes that the information is significant.”

Thus, for the students in this study, “studying” was not easily distilled into a set of discrete strategies such as re-reading the textbook or hours spent engaged in discrete strategies. Further, as we discuss below, students’ views of studying also implicate a variety of strategies, social and physical settings, and resources as being involved in the studying process (Greeno 1998 ; Halverson 2003 ; Robbins et al 2004 ). Future research should delve more deeply into what students consider to be studying in terms of its physical, artifactual, and temporal boundaries. To maintain a consistent definition for this analysis, however, we defined studying as any interaction with course material outside of the classroom.

Cues to initiate studying and timing of study strategies

Prior to engaging in particular study activities, students frequently discussed why they started studying, which centered on the core idea of “cues” that trigger study behaviors. These cues were either provided by the instructor or were internally generated. While students were not explicitly asked about what cued their study sessions, descriptions of the study processes for many students provided information for this analysis. Another important aspect of these preliminary stages of studying is when students choose to study—either throughout the semester, several days before an assessment, or the day before a test or exam (i.e., cramming).

Instructor-generated cues

Throughout a given semester, 40 students reported that instructors often provided cues regarding when and what they should study. The most important cue for students tended to be the announcement of an upcoming assessment, thus initiating the process of studying. For some, an impending assessment was the only reason for studying. Similarly, instructors’ discussions about assessments (e.g., topics that would be covered) served as a primary rationale for some students to attend class. One participant said, “I go to class to (hear) the professor say this week on the exam you will see this subject or that subject.” Consequently, for some students, the classroom becomes a venue in which cues pertaining to assessments are sought and then applied to their studying.

Self-generated cues

Fewer (four) participants also discussed another cue for studying, that of recognizing that they were not sufficiently prepared or familiar with the course material. One participant explained that he studied after realizing that he did not understand a concept, which then set in motion a series of study behaviors that lasted until he felt conversant with the material. He said, “…and then I realize, ‘Oh man, I don’t understand pulleys so well,’ so last week I studied pulleys until I understood them.” Others reported a strong desire to learn certain skills and material so that they could reach their career goals.

Next, we discuss findings regarding when students reported engaging in study activities. For 11 respondents studying took place several days before an exam or test, while 14 reported waiting until the last day or even night before, popularly known as “cramming.” While the literature indicates that cramming is an ineffective way to study (e.g., Kornell 2009 ), and some students recognize its limitations (e.g., one student reported that after cramming “[the information] is not still in my brain”), this mode of preparation remains a common method. Finally, 15 respondents discussed studying throughout the term. In some cases, this practice was instigated by course-specific factors such as an instructor’s use of weekly quizzes, whereas in others, the student established a regular schedule of studying on their own.

Marshaling resources for studying

After discussing cues and timing for studying, the respondents then discussed collecting and utilizing a variety of resources with which to study. In describing students’ use of resources, we included references to commonly used tools such as course websites and textbooks as well as human resources that learners draw upon when studying. This represents a broader view of resources within organizations than is commonly used but captures knowledge and capabilities of instructors and staff within an educational organization (Gamoran et al. 2003 ). Understanding the resources used during studying is important because digital, print, and human resources and tools are used to enhance or even shape the studying act itself.

The resources discussed by the respondents included digital tools and media, print resources, and human resources, and those most commonly reported are depicted in Table  2 .

Digital resources

While the most commonly reported digital tool included laptops or desktop computers, we focus here instead on the applications used by students on these now ubiquitous resources for college students. The most widely reported resource was the course website (27 students), which operated on various learning management system (LMS) platforms. These websites were developed by instructors who posted a variety of learning resources including videotaped lectures, readings, practice exams, and course syllabi. One student described her professor’s course website as such, “So basically like any way you learn you can find it on [course website name] through all her resources and find a good way to study for you.”

The next most widely used digital resource included websites for seeking out new information including Google (24) and Wikipedia (13). These websites helped students expand upon lecture notes or clarify concepts or steps in solving problems. For example, one participant noted that in lecture, he listened for key words that could be included on exams and then looked them up online, because “With the Internet and Wikipedia you just need to know a few keywords and you can learn about anything.” Other resources included Facebook (9) which was used as an organizing tool and Youtube (5) for informational purposes. These results support prior research that found college students regularly utilize these online resources, even more so than their own instructors (e.g., Morgan et al. 2012 ).

However, the evidence suggests that technology also acts as a disruptive force in some students’ study habits. Nineteen respondents reported that some digital resources, usually cell phones and Facebook, regularly disrupted their studying yet they had no strategy for managing these distractions. One student noted, “[When studying] I look up sports stuff, any excuse not to be studying….at a computer I can just click on whatever I want.” To mitigate the potential distractions of the Internet or a buzzing phone, 21 students reported having developed strategies for managing distractions, often by deliberately removing them from their study “space.” The optimal studying situation for one student was in an isolated cubicle in the library basement with no cell phone reception, and he would turn off his laptop’s wireless Internet signal. In another case, a student went to her parent’s house on the weekends for a self-imposed “no devices zone” where her phone was confiscated so she could concentrate. Thus, digital resources can both enhance and detract from an individual’s studying, and students have varying degrees of success when it comes to managing the detrimental aspect of digital devices and media.

Print-based resources

Another type of resource that respondents regularly used was print-based resources such as textbooks (34) and lecture notes (33). Lecture notes took many forms including notes taken by student in class as well as notes and/or PowerPoint slides provided by the instructor, both of which were reported as important resources for studying. Another less utilized print resource discussed by five respondents was cue cards, which were mostly used to memorize key facts and formulas.

Human resources

The last type of resource reported by students pertained to the knowledge and content-expertise of people within their courses and/or departments. These included instructors (8) as well as teaching assistants (8) and tutors (4). In some cases, the participants reported approaching instructors or teaching assistants outside of class to obtain assistance with homework, upcoming or previous exams, and challenging concepts or problems. For students who were especially struggling with the course, tutors provided expertise and one-on-one instruction that these students viewed as an especially important form of academic support.

Setting and strategies

Next, we report data that speak to the studying process itself, particularly with whom students study and the specific strategies they employ.

The social setting in which studying occurs

When describing their actual study sessions, the respondents noted whether or not they studied alone or with others. For 39 respondents, studying was often a solitary affair. Some students noted that studying alone was an explicit strategy to reduce distracting conversations with others, while others stated that it was simply a habit. In contrast, 35 students described studying in groups. In these cases, the respondents stated that group-based studying was useful because peers could provide new insights or solutions. However, because 24 students reported studying both alone and in groups, depending on the proximity to an exam or the nature of the assignment, it is clear that for some students in the sample, the social setting in which studying occurred was rather flexible and not a fixed criterion or preference.

Employing specific study strategies

The studying process next involves the selection of specific strategies or techniques. While the participants often described these strategies using imprecise or idiosyncratic terminology such that it was often not possible to align them with those discussed in the literature (e.g., Dunlosky et al. 2013 ), it was possible to identify several core strategies utilized by this group of undergraduate students. In this section, we elaborate on the most commonly referenced strategies (see Table  3 ).

Thirty-eight participants re-read or reviewed course material or notes taken in class. This strategy was discussed as both a general practice that took place throughout the term as well as an initial step in preparing for exams. For example, one participant said that he re-read all of his lecture notes before working with old test materials “to try to understand what the professor had said fully” before attempting to take practice tests.

Given the broad conception of studying used in this analysis (i.e., any interaction students have with course material outside of class), we include the strategy of “doing homework” which 25 participants reported. As one participant put it: “My method of studying is pretty much to do any homework or review questions…” Homework also provided a litmus test of understanding—one participant explained how he learned a lot in class, but it really became clear when he answered the homework questions correctly.

While students reported reviewing lecture notes from class, this particular strategy involved 22 students creating their own artifacts such as cue cards, consolidating notes from different sources (some instructor-provided, others self-procured) into one set of notes, and so on. For example, one participant explained, “I write myself notes and everything is in my notes, including the textbook material and the prof’s slides and what the prof said or the stuff I found in Wikipedia or everything.” Others created study aids (e.g., games or cheat sheets) that were used throughout the term for study sessions.

Twenty participants reported reading the textbook in some capacity, either in full or in part, either assigned or unassigned, or they consulted the book when confronted with unfamiliar material. Often, the participants did not specify if they were re-reading, reading it for the first time, or if they were skimming. Importantly, the depth with which students read textbooks appeared to vary based on their intentions. In one case, a student explained, “Sometimes I just go through the chapter we’re going to go through in class and I just read all the captions for the images (to prepare for the lecture) so I know what we’re going to talk about and then afterwards I’ll read through the chapter.” In most cases, however, students spoke more ambiguously about reading.

Nineteen participants reported working with test materials provided by the instructor or students who had previously taken the course. One participant reported her routine as taking practice exams in a simulated test-taking environment, followed by an item-by-item analysis of her performance. Another talked about reviewing tests from previous years and randomly selecting problems to complete for practice. In both cases, the materials provided the students with an opportunity to monitor their level of understanding (or lack thereof) while also becoming attuned to the test-maker’s approach.

Working on problems was a strategy reported by 17 participants. Although ambiguous, the specific nature of the term “problems” likely refers to mathematical or computational problems given that many of these participants were enrolled in science or engineering courses. As one participant said, “I just find every single practice problem that I can get my hands on and do it.”

Eleven participants reported working on a variety of questions while studying. In one class, students worked on study questions or short essay prompts that review that day’s lecture. Further, instead of relying on practice exams, one student in that class reported, “I’ve found the best way to do well on the test is not to do all of her practice exams, but do (the) study questions.” Others reported working on end-of-chapter questions and completing discussion questions as an effective study strategy.

Taking quizzes related to course material outside of class was another method of studying reported by 11 participants. Sometimes the instructor provided the quiz to test comprehension after a reading assignment. One student who takes bi-weekly extra-credit quizzes provided by her instructor said, “I take them pretty seriously, I’ll prep a little bit before them even though they’re only five questions and if I get something wrong I’ll read (about it).”

Other factors influencing the study process

In addition to specific cues, resources, and study strategies, respondents also discussed various situations or factors that influenced their study behaviors.

Role of instructor in providing resources for studying

Student’s use of resources during their studying depends, in part, on the instructor and his/her provision of particular resources within the course. For example, some instructors provided their students with a variety of modalities and tools for learning (e.g., podcasts, supplementary readings, online lecture notes) that other students might not have had access to in other courses or with other instructors. These can be offered as in-class resources, or more commonly, embedded within the course’s website or LMS. Students can then select from the resources made available by their instructors, as well as resources that they find on their own, to construct their own unique study situation.

Course characteristics and discipline

The participants described how disciplinary content and course structure also influenced the strategies and resources they used. Some students perceived that different disciplines required different approaches to studying. One participant said, “You can’t study math how you would study biology, right?” The student followed up this observation by describing how studying for a math course entailed doing numerous problem sets, while a biology course required extensive reading, memorization, and understanding laboratory assignments. Other course characteristics that influenced teaching were the assessments and teaching methods used in the course. For instance, one student explained how her approach to preparing for multiple-choice exams emphasized a surface knowledge of selected topics: “Instead of looking at a topic and being able to discuss it for paragraphs at a time in like an essay format, I’ll try to memorize details that I feel are important.” Another respondent student noted that his studying “tends to match the style of the class” so that in a class taught with PowerPoint slides, his studying entails “a lot of time looking at slides,” whereas a more interactive class involves focusing on concepts and hands-on activities. This student’s approach to studying suggests that an instructor’s teaching style may have consequences for student learning not only through in-class comprehension of material but also by sending messages to students regarding the best way to study.

Personal situations and dispositions

The participants also alluded to personal factors that influenced their studying such as the lack of time due to heavy course loads and/or work schedules, family situations, and health-related issues. Additionally, the participants brought to a course pre-existing dispositions and experiences that influenced their approach to studying. One of these pertains to historic study habits from high school, where some students attempted to alter their “old” study behaviors to fit with the “new” expectations and demands of the university, while others simply continued using what had worked for them previously. Finally, student’s personal reasons for taking a course (e.g., to satisfy degree requirement, curiosity) also shaped how participants approached their studying.

Patterns in cues, resources, and strategies

Next, we sought to explore whether or not patterns in the data existed in regard to how cues, resources, and strategies were inter-related or not. Preliminary analyses using exploratory data techniques did not reveal discernable patterns, and no clear links were evident across the three primary components of studying identified in the data (i.e., cues, resource use, strategies). Instead, we chose to examine patterns in the use of study strategies considered effective in the literature (e.g., Dunlosky et al. 2013 ) according to two aspects of study behaviors (i.e., study timing, social setting) and two variables related to subject characteristics (i.e., course level and discipline). All analyses include weighted averages.

First, when looking at when studying occurs according to three groups of students (i.e., less than 1 week prior, cramming, throughout term), some data points stand out (see Fig.  1 ). Crammers review notes more often than others (94%), while those studying less than 1 week prior to exams use more textbooks (73%), study questions (55%), and video (36%) than other groups and those studying throughout the term or semester use problem sets (53%) more often than others. These data indicate that some variation in study strategies is evident depending on when students choose to study.

Selected study strategies by timing of study practices and social setting

Second, when organizing the data according to two groups (i.e., studying alone or studying in groups) differences in study strategies are also evident (see Fig.  2 ). Note that some students reporting doing both, hence, the large numbers in both groups that do not sum to 60 (42 and 36, respectively). Students studying alone tend to review notes (64%) and textbooks (52%) and also do practice tests (24%) and quizzes (19%) more than those studying in groups. In contrast, those studying in groups create study artifacts (47%), do problems (31%) and questions (31%), and use online materials such as video (14%) and the Mastering Physics/Anatomy videos (19%).

Third, when the data are organized according to the discipline of the course students were enrolled in at the time of data collection, additional points of variation are evident (see Fig.  3 ). Again, students may or may not be majors in these fields but discussed their study habits in relation to these disciplines. Students taking biology courses (26) report reviewing notes (69%) and textbooks (46%), doing practice tests (31%) and questions (35%), and reviewing videos (27%) more than students taking courses in other fields. Students in physics courses (11) reported creating artifacts (64%), doing problem sets (82%), and mastering resources (45%) more than others. Mechanical engineers and geology students did not report any study strategies more than other groups.

Selected study strategies by discipline and course level

Finally, the data indicate that study habits vary by course level, with students in upper division courses (18) reporting using certain study strategies more than those in lower division courses (43), including practice tests (33%), questions (39%), video (28%), and mastering physics or anatomy resources (33%). The students in lower division courses reported reviewing notes (58%), creating artifacts (37%), reviewing textbooks (51%), and doing problems (42%) and quizzes (16%) more than the students in upper division courses (see Fig.  4 ).

Case examples

Finally, to illustrate how each of these sets of findings is evident in students’ own real-world experiences, we present three in-depth analyses of students’ actual studying practices. The first two cases are those of individual students—Larry and Brianna—whose study behaviors reflect different sequences of decisions that link particular cues, resource use, and strategies. The final case is that of a group of five students in a single course (i.e., upper division anatomy and physiology). These cases illustrate how studying is a complex, idiosyncratic practice, while also being shaped by the social, institutional, and technological milieu in which students operate.

Larry. When we spoke with Larry he was studying for an upper level biology course that was required for his major. He first talked about his personal view of what studying means, stating that:

Studying to me means stressing out your brain so that it realizes that the information is significant. Basically, your brain can be lazy when it doesn’t think that something’s important and stressing it is what makes it retain information. So studying for exams is a lot about stressing your brain out.

This theory of how the brain and learning work thus set the stage for Larry’s subsequent study habits, which was a process that began in the classroom. There, he “frantically” wrote on the instructor’s PowerPoint slides that he printed off before class, labeling images, drawing arrows, and identifying mechanisms for cell signaling that were being discussed in class. Larry said that he did not necessarily understand the concepts at the time but took the notes down to reference later. In fact, it is not until he finds the time to sit in the library and read the relevant sections of the textbook that “it all comes together and finally makes sense.” While reading, he writes down key terms and their definitions in a notebook. For Larry, the library represented an important resource in his education because he does not own the book because he cannot afford it. Thus, he spent a lot of time in the library reading one of two copies on reserve. He also attended the optional recitation section for the course where he was able to speak with the instructor one-on-one and earn extra credit.

While he tried to study throughout the term, with his demanding course load and work schedule, he often only had time to study 3 or 4 days prior to an exam. Describing his study habits as “messy” and comprised of “lots of big stages,” Larry first gathered his notes from classroom sessions and his review of the textbook and then made flashcards for key concepts from the course. He also completed the end-of-chapter quizzes in the text and reviewed (and retakes) any old quizzes or exams from the course. The day before the exam, he tells himself “Wow Larry, you really have to get to it now,” and he sequestered himself at the library to review his notes and difficult concepts in the textbook and to re-watch videotaped lectures from the course website. All along, he deliberately studied alone because he had to maximize the limited time available for studying, such that he “cannot afford to sit around and have people talk about other stuff.” After several hours in the library reviewing these materials, Larry generally felt ready for the exam. Altogether, in Larry’s case, studying is an act that is instigated by instructor’s cues (i.e., upcoming exam), informed by a folk theory of the mind, involves a variety of curricular resources, and is strongly influenced by his personal situation.

Brianna. Next, we consider the case of Brianna who was enrolled in a lower division physics course when we met with her group. Her general approach to studying was to hope that the instructor was direct about expectations and guidelines because then, “the ball is in (her) court and (she) either learns the material or not.” Thus, Brianna was relatively self-motivated but relied on instructors to provide cues regarding when and how hard to study. This motivation is also sparked by her aspiration to attend medical school, which requires doing well on the Medical College Admission Test (MCAT). Brianna observed that “even if I get an A on a test but have no idea what is going on, it wouldn’t set me up to be in a good place for studying the MCAT in the future.”

In addition, she relied heavily on what she called her own preferred “learning style,” which centered on reading and re-reading text, whether it be the textbook or notes taken in class. Given her reliance on text and notes, Brianna observed that, “I pretty much show up to lecture just to write down what he’s saying.” The notes she took in class then became an important artifact for later studying, as she used them to create flashcards from her notes (and the textbook) and an outline for the course that is added to throughout the term. During her actual study sessions, Brianna either studied alone, reviewing her notes, scanning various digital resources, and doing practice problems, or with a group of friends in the library where she typically had on her headphones while surrounded by classmates who intermittently helped one another out on difficult problems.

Brianna’s use of digital technology is notable because she described online videos, the course website, the Internet, and social media as the “majority” of the resources she used to study. For example, she consulted free online tutoring videos (especially videos featuring one tutor in particular at the University of California at Berkeley), which helped her fill out her notes and summaries from the class and textbook. Overall she described the Internet as a “great resource” for finding course materials (e.g., slides, notes, exams, and videos) from other instructors teaching the same course at other colleges or universities. The questions these other instructors ask their students provides insights into what Brianna perceives her professor may ask, so she values their outsider’s perspective. Further, when doing her homework problems, she looked up the solutions online, even when she was confident about her answer. She does this to ensure that she is “approaching [the problem] in the right way” or to see if there are alternative methods to solving the problem. This, in turn, gives her a more “holistic grasp of the question.” Finally, in this and other courses, social media sites such as Facebook provided a place where she and her friends posted questions and shared approaches to different problems. Notably, most of the digital resources Brianna utilized were not part of the official course materials organized by her instructor and posted on the course website.

Thus, for Brianna studying involves a process of re-reading course materials and tools such as cue cards and digital media, largely in response to instructor’s cues about upcoming exams or homework. Driven by the desire to attend medical school introduced an element of motivation that made her take studying rather seriously.

Dr. Wells’ course. The final case is that of five female students taking an upper division anatomy and physiology course with Dr. Wells. In this course, which had an enrollment of 525 students across three sections, Dr. Wells had provided a rich array of learning resources on the course website that included weekly postings of videotaped lectures and PowerPoint slides, weekly practice questions, old exams, and links to other online resources. The students in the focus group noted that Dr. Wells did not simply post these resources and let students figure out how to utilize them but instead discussed in class how to use each tool and study with them. As Angelica noted, “She just does a really great job of giving us a lot of different ways to study.” Jacquie concurred, saying that while an online course she was taking was similarly well-resourced, “Dr. Wells provides more alternative methods to study which is what makes her course stand out.” Ultimately, in providing such a variety of resources for studying, Dr. Wells had crafted a learning experience that stood out for these students. For Bailey, who had little experience with the material, this was particularly important because “it’s really hard to stick your fingers in and get going,” and if you only have a textbook to work with, the entry points to the material are limited, often inaccessible and not particularly engaging.

In many ways, Dr. Wells was running a partially flipped classroom, in that students watched videotaped lectures online and came to a class that was highly interactive and engaging. During the class, Dr. Wells was constantly in motion, using her iPad to project slides on the screen while also writing and drawing using a stylus pen. Many questions were asked of students, including peer-based activities and small group discussions. Linda noted that Dr. Wells also emphasized important ideas across various formats such as clicker questions, practice tests, and study questions, such that “the repetition is awesome…even if you’re tired or distracted, eventually you’ll still get it.”

In terms of how these students actually studied in the course, weekly study questions (i.e., short essay questions that recap entire lectures) provided by Dr. Wells played an important role. For Jacquie, who said that she essentially crams before the exam—saying “well yeah, that’s why we study, for the exams”—her lecture notes, study questions, the mastering anatomy online resources, and old test materials were all utilized during study sessions. After discussing the course with her friends, however, she concluded that the best way to succeed in the course was “not to do all of her practice exams but to do those freaking (weekly) study questions.” Angelica said that “I noticed when I don’t do the study questions I don’t do very well.” This approach is similar to the technique of distributed practice, or regularly spaced testing of material over time, which is one of the high-impact study strategies identified by Dunlosky and colleagues ( 2013 ). Robin also spent 3–4 h after each class doing the study questions, along with a variety of other tools including the mastering anatomy activities, notes, lecture videotapes, and old test materials. In fact, while the group varied on the timing of their studying, all were cued by the instructor, used a variety of digital and print resources and between four and six study strategies. This reliance on multiple resources and study strategies is unsurprising given how Dr. Wells structured her course and guided her students in regard to studying. Within this learning environment, students then developed their own approach to studying but in ways strongly shaped by the resources and strategies Dr. Wells had embedded in the course structure.

The field of higher education in general and STEM education in particular continues to grapple with how to best facilitate learning, persistence, and retention throughout students’ postsecondary careers. Does the answer lie in changing teacher behaviors alone, such as the adoption of active learning techniques, structural responses such as reducing student debt and dealing with the rising price of college, or is success also dependent upon student attributes such as engagement and motivation? What these questions reveal is that students’ experiences in college are shaped by a variety of influences and that the intersection among policy, economics, organizations, and instruction provides a more accurate frame for thinking about student success than a search for a single “magic bullet” solution.

The same idea applies to thinking about the role that effective study strategies play in student learning. While the use of high-impact practices such as distributed practice is certainly a key ingredient in leading to student learning (Dunlosky et al. 2013 ), it is important to recognize that students’ adoption of these practices requires several antecedent conditions to be in place before this can happen. These include knowledge of these methods, time to study, access to the resources required to study in this manner, and so on. Similarly, studying is not simply about using strategies such as re-reading text or doing practice problems but is a process that involves cues about when to study, the timing of their actual study sessions, which resources to utilize, where to study, and which strategies to employ. How these stages unfold in practice are also shaped by a variety of factors such as a students’ personal life, the course material, and how instructors structure courses and make learning resources available. This is not to diminish the importance of high-impact study strategies but instead to point out that there are many steps taken by students to get to the point where they can sit down and utilize them with some regularity.

In the remainder of this paper, we discuss how this exploratory study contributes to the literature on college student study habits, particularly through the articulation of a multi-dimensional conception of studying that can provide instructors and administrators with a more nuanced account of how students engage in studying. In combination with the data reported in this paper as well as developments in educational technology and research, such an account also highlights the importance of instructional design that facilitates students’ use of high-impact strategies, diversified learning tools, and self-regulatory capabilities.

A new approach for thinking about undergraduates’ study behaviors

The results reported in this paper confirm and extend prior research on college student study habits. The data reinforce prior research that some of the most dominant study strategies utilized by students include reviewing notes and re-reading textbooks (Karpicke et al. 2009 ), utilize a variety of digital resources (Smith and Caruso 2010 ), and also rely on instructors to provide cues to begin studying (Miller and Parlett 1974 ). While the study described here is limited by a small sample size and lack of data on the impact of various study habits on learning outcomes, it was designed to shed light on fine-grained behaviors among a small group of students in order to advance our understanding of decision-making and action in specific social, organizational, technological contexts. In doing so, we extend the prior literature by offering an integrative multi-stage approach for thinking about study behaviors.

When interpreting the results from this exploratory study, we observed that students discussed their studying in terms of stages that began with cues to study and ended with their use of specific strategies. Along the way, they made decisions about who to study with and which resources to use, an account consistent with a situative theory of cognition, which posits that mental activity and social action is situated within specific socio-cultural and organizational contexts while also being distributed among mind, tool, and activity (Greeno 1998 ; Spillane et al. 2001 ). In other words, studying is not solely a matter of a “mind” sequestered with a book and highlighter pen, or a behavior that could be distilled into hours spent studying or the prevalence of a particular strategy, but instead involves people interacting with one another and various tools in specific situations (Seely Brown and Adler 2008 ). This is not to diminish the value of experimental work that does hone in on specific aspects of studying such as how students self-pace their study or time spent on specific tasks (Bjork, Dunlosky and Kornell 2013 ) but instead is an argument that a broader perspective of the act of studying itself is also useful.

Consequently, based on the data presented in this paper, we suggest that a new way of thinking about studying is warranted that includes the following components: (1) recognizing the situation and detecting cues to initiate studying, (2) marshaling resources and managing distractions (or not), (3) selecting a time and social setting to study, and then selecting specific strategies, and (4) engaging in a period of self-reflection. We illustrate this approach using the three cases reported earlier in the paper (see Fig.  1 ).

Some caveats are necessary when interpreting this figure. First, while the stages of cue detection and timing, resource use, and strategies are based on data from this paper, the self-regulative period is not. Instead, it is included as a post-assessment phase of reflection and commitment that the literature indicates is an important aspect of learning (Zimmerman and Schunk 2001 ). Second, we do not claim that all 61 participants in the study progressed through each of these steps, but instead that this conception of studying captures the broad range of behaviors and experiences students reported engaging in during a recent study session. As a result, we are not suggesting that this account of studying is generalizable to all students but is a heuristic device for thinking about studying in a more multi-dimensional manner than is common at the present time.

Thus, we argue that conceptions of the act of "studying" extend beyond a focus on discrete, decontextualized factors such as hours spent studying or the use of specific strategies (e.g., re-reading text). In making this argument, we highlight the importance of ecological validity when thinking about study habits in general and interpreting laboratory-based research in particular. In other words, understanding how findings from the literature about "effective" study habits may vary according to disciplinary, social, institutional, or personal situations will be important for future work in the area. We also suggest that a more multi-dimensional conception of studying can also be a useful interpretive framework for educators, instructional designers, and administrators to begin thinking more broadly and strategically about how their courses are designed (or not) to foster effective study habits. By recognizing that studying involves multiple states, resources, strategies and actors, it becomes necessary to move beyond simply providing “how-to” guides for studying or recommendations for students to use high-impact practices to instead think about the role that cue-seeking, resource acquisition, and distraction management play in shaping students’ study habits. With a more situative view of studying in mind, it is possible to consider how the course as a whole creates an environment that prompts particular study behaviors, such as Dr. Wells’ provision of various learning tools via her LMS that prompted students to study with them. Thinking of studying in these terms, in the remainder of this paper we highlight ways that educators can facilitate or support effective studying and learning practices: fostering self-regulated learning and using principles from instructional design to encourage high-impact studying.

Fostering self-regulated learners

One of the most pressing issues facing educators is the fact that many students continue to utilize ineffective study practices, such as re-reading textbooks or cramming the night before an exam. Informing some low-impact practices are “faulty mental models” (p. 417) about how memory and learning work, such as the view that information can be recalled and played back like a recording (Bjork, Dunlosky and Kornell 2013 ). Instead, the retrieval process involves reconstructing knowledge from various stored memories, is heavily dependent on specific cues, and that upon cueing information in memory becomes reinforced. Essentially, students need to understand that in order to create a library of information in their minds that is easily accessed and retained over the long term requires a “meaningful encoding of that information” which involves integrating information into a network of connected ideas and then regularly practicing retrieval of that information (Dunlosky et al. 2013 ).

Besides becoming more sophisticated learners and theorists about how the mind works, it is clear that students can also benefit from more guidance about how to more effectively study and learn. Educational psychologists argue that becoming a more adept learner is not simply about amassing tips and strategies about how to study but is based on becoming what is known as a self-regulated learner, which is the “self-directive process by which learners transform their mental abilities into academic skills” (Zimmerman 2002 , p. 65). Self-regulation is not just a quality or personal aptitude, however, but is best thought of as a sequence of states that include forethought (i.e., plans for studying), performance, and self-reflection. Motivation to initiate studying on one’s own is important, but perhaps more critical is the self-monitoring of performance, especially the ability to scrutinize and interpret failure and make corrections (Boekaerts and Corno 2005 ). Furthermore, while considerable barriers exist for students to develop self-regulative habits, such as a belief that intelligence is “fixed” and not malleable (Yeager and Dweck 2012 ) and assumptions that learning should be simple and unproblematic (Bjork, Dunlosky and Kornell, 2013 ), helping students to develop this aptitude is critical because it is a core aspect of success not only in school but also in life and the workplace (Pellegrino and Hilton 2012 ).

So how, if at all, can STEM educators embed self-regulatory skills into their courses? Setting aside for the moment the extent to which self-control, goal setting, and responsibility should be learned in the home, grade school, or various other cultural fields during childhood, the fact remains that it is possible to teach some aspect of self-regulatory competencies in the college classroom (Nilson 2013 ). One strategy is to create a classroom environment with high expectations and a low- to zero-tolerance policy for irresponsible behavior or late assignments, thereby encouraging if not forcing students to set goals for themselves and achieve them. Another strategy is the widely used instructional wrapper, which refers to prompts for students to reflect on their performance before and after an assignment or activity, which trains students to regularly reflect on their study habits and approach to learning (Lovett 2013 ). Other ideas include modeling learning strategies such as self-monitoring and summarizing in front of students in what is known as a “cognitive apprenticeship” (Palincsar and Brown 1984 ), using small group work tasks designed to spark self-regulation (Fitch et al. 2012 ) and assigning open-ended tasks and assessments requiring students to choose strategies and take control of their learning (Boekaerts and Corno 2005 ).

We conclude this discussion about self-regulation with a note about digital media. While online resources and digital devices can play an important and productive role in facilitating student learning (Dabbagh and Kitsantas 2012 ; Smith and Caruso 2010 ), it is evident from the data that they can detract from focused study. Thus, another aspect about self-regulation is the willingness and ability to remove digital distractions when they are not serving a productive purpose, such as the students in this study who deliberately went to libraries without wireless Internet or parents’ homes where devices were confiscated. As will be discussed in the next section, technology can and should be part of educators’ instructional toolkit, but students would be well served by adopting more self-regulated stances when it comes to the presence of technology in their study sessions.

Encouraging personalized learning and high-impact studying through course structure

Next, we turn to issue of how instructors, through the deliberate design of their courses, can facilitate effective study habits. Here, we focus on two aspects of effective studying: the use of multiple representations and modalities and the use of high-impact strategies. In both cases, we can look to the example of Dr. Wells’ course where she embedded within the structure of the course itself opportunities for students to draw upon various learning tools while also imposing a high-impact study habit (i.e., distributed practice) via weekly practice questions.

First, providing students with a variety of learning resources and tools offers them a variety of entry points with which to explore the material. The rationale for doing so is not to support students’ distinct learning styles, an idea that is popular but unsupported by the empirical evidence (Pashler et al. 2008 ), but instead is based on the fact that learners that engage with varied representations of an idea or concept demonstrate improved learning outcomes (Pellegrino and Hilton 2012 ). In addition, the provision of various learning tools is useful because today’s learners are broadly proficient in developing personalized learning pathways, whether for academic or personal purposes, using online resources and social media (Dabbagh and Kitsantas 2012 ). In doing so, many create social learning environments, or what some call “participatory cultures,” where people develop online learning communities where they collectively create, share, and learn from each other (Jenkins et al. 2006 ). Essentially, the idea is to make available a repertoire of learning tools and media for students that they can then select from to approach the material from multiple perspective and according to their own unique way of engaging with different learning modalities.

The second approach for facilitating effective study habits pertains to the structure of a course, from the timing and nature of assessments to the types of learning activities students are required to do. Again, consider the example of Dr. Wells, who embedded within her course an assessment strategy that forced her students to engage in the high-impact study habit of distributed practice. Through weekly practice questions, students were required to regularly take mini-exams on different topics (Dunlosky et al. 2013 ). Spacing out study sessions on distinct topics enhances learning through the spacing effect and also by introducing comparisons or “interference” across topics, which results in higher-order representations or complex mental models that not only are repositories of information but also facilitate transfer and retention (Bjork, Dunlosky, and Kornell 2013 ).

Similarly, the role of course structure in facilitating student learning has been well documented in STEM education, where pre-class reading quizzes and weekly practice exams have been linked to improved student learning (Freeman et al. 2011 ), and even in reducing the achievement gap between white and under-represented minority students, because highly structured courses with regular practice may introduce study and learning skills to students with little experience from high school (Haak et al. 2011 ). The takeaway here is that as educators, we can design our courses and teach our classes with explicit attention towards creating (and mandating) situations for students to engage in certain study habits.

The attention currently being placed on STEM instructors and their pedagogical acumen as key facilitators of student learning is well-placed, but the relationship between teaching and learning is anything but direct, linear, and unproblematic. What students decide to do in terms of when and how to study act as critical intermediaries between what instructors do in the classroom and students’ ultimate performance in college. As Entwistle and Tait suggested over 25 years ago, ( 1990 , p. 170), students’ behaviors and strategies “affects learning probably as much as, if not more than, the classroom skills of the lecturer.” On this point, there are both promising and troubling signs. While students are increasingly utilizing varied resources and media in a deliberate and creative manner to advance their studies, some study methods that are demonstrably ineffective continue to be widely used. The exploratory study reported in this paper offers a new, multi-dimensional way to think about studying that suggests future research directions exploring undergraduate study habits including similar descriptive research with larger samples and additional disciplines, experimental research focusing on specific strategies under different conditions (e.g., resource use, cues for studying), and examining the relationship between course structure and studying.

Ultimately, students must take responsibility for their learning and strategize ways to create situations—whether in a quiet library basement or a group study session online—where they can effectively study and learn. However, postsecondary educators must also be cognizant of the pressures facing today’s college students and the fact that many have not been taught how to engage in high-impact study habits but instead rely on re-reading highlighted text. One of the guiding principles for instructional design should be the idea that it is no longer tenable to assume that students have been taught how to effectively study and learn prior to their matriculation into a college or university. While students bring a wealth of new learning habits and technological acumen to the twenty-first century classroom—whether online or face-to-face—they still need guidance in how to study. This conclusion, however, should not lead to complaints about unprepared students or a failed K-12 sector but instead needs to spark postsecondary educators to carefully design of rich and engaging learning environments that sparks self-regulatory habits of mind and encourages high-impact studying, so that students are well positioned to succeed.

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Acknowledgements

The authors would also like to thank Jana Bouwma-Gearhart and Jennifer Collins for their involvement in this study and collecting data reported in this paper.

This research was supported by a grant from the National Science Foundation (DUE#1224624) for the Tracking the Processes of Data Driven Decision-Making Study ( http://tpdm.wceruw.org ).

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AO participated in the design of the study and data collection, led the data analysis, and collaborated with MH to draft the manuscript. MH conceived of the study, led the design, participated in data collection and analysis, and finalized the manuscript. Both authors read and approved the final manuscript.

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Places with more college graduates tend to foster better lifestyle habits overall, research finds

by Christy DeSmith, Harvard Gazette

Having more education has long been linked to better individual health. But those benefits are also contagious, say the co-authors of a new working paper .

"It's not just that the individuals who have more years of education are in better health," said David M. Cutler, Otto Eckstein Professor of Applied Economics. "It's that even people with fewer years of education—for example, people with just a high school degree—are in better health when they live around people who have more years of education."

The paper examines why cities with more college graduates see lower mortality rates for residents overall. It's not due to spatial sorting, or the practice of relocating to live amidst those with similar habits. Nor did the researchers find a particularly strong correlation with factors like clean air, low crime, and high-quality health care infrastructure. Instead, most of the explanation involves rates of smoking, physical activity, and obesity.

The pattern has everything to do with a community's common culture, said co-author Edward L. Glaeser, the Fred and Eleanor Glimp Professor of Economics and chair of the Department of Economics. "Smoking, for example, is a social activity," he said. "Fundamentally, being around other smokers is fine if you're smoking, but it's usually pretty unpleasant if you're not smoking."

Glaeser, an urban economist and author of "Triumph of the City" (2011), has spent decades studying how varying education levels play out across U.S. society. One well-established finding concerns economic resilience . "If you ask yourself, which American cities managed to turn themselves around after the very difficult period of the 1970s and 1980s? Educated places like Seattle or Boston did. Less-educated places did not," Glaeser said.

For his part, Cutler, a health economist , spent the last few decades parsing the strong link between education and individual health outcomes. All the while he kept collaborating with Glaeser to explore obesity , smoking , and other health-related behaviors at the community level. The economists revisited these issues in the 2021 book "Survival of the City: The Future of Urban Life in an Age of Isolation."

Also collaborating on the new paper were Jacob H. Bor, an associate professor of global health at Boston University, and Ljubica Ristovska, a postdoctoral fellow at Yale. Together, the researchers rejected the spatial sorting explanation with the help of data from the University of Michigan's Health and Retirement Study .

Similar analysis was done using data from the National Longitudinal Surveys of young women and men. Results showed that unhealthy people of all ages relocate more frequently than healthy ones. But both groups settle in areas with roughly equal levels of human capital (defined here as a population's years of education).

The team analyzed a variety of information sources—from county-level homicide statistics to regional estimates of air quality and a federal measure of hospital quality —to see whether mortality differentials are due to area amenities. "We estimate that at most 17% percent of the human capital externality on health is due to these external factors, driven largely by greater use of preventative care," the co-authors wrote.

Instead, the majority of the correlation between human capital and area health—at least 60 percent—is explained by differences in health-related behaviors, the researchers found. Combining data from both the U.S. Census Bureau and Centers for Disease Control and Prevention revealed that every 10% increase in an area's share of college graduates was associated with an annual 7% decrease in all-cause mortality.

With additional data from the CDC's Behavioral Risk Factor Surveillance System and the Census Bureau's Current Population Survey (CPS), the researchers were able to probe connections between human capital and various health-related behaviors. Every 10% increase in an area's college graduates was associated with a 13% decrease in smoking, a 7% decrease in having no physical activity, and a 12% decrease in the probability of being very obese.

"It really opens up all these questions of how people form their beliefs," Cutler said.

The paper went deepest on smoking, given the wealth of historical numbers on cigarette initiation, cessation, and beliefs. CPS data showed that in cities where people have more years of education—New York City, Boston, or Seattle, for example—people are more likely to think that smoking is bad for you.

Residents of these cities are also likelier to support smoking regulations. For every 10% increase in bachelor's degrees, the probability of working at a place with a complete smoking ban increases by 2 percentage points.

Cutler and Glaeser were especially fascinated to find a growing connection over time between human capital and area health, especially between the years 1990 and 2010. As the correlation between individual education and behavior increased, they explained, the relationship between a community's education levels and its mortality rates slowly followed suit.

"Just look at people who were 70 in 2000," said Glaeser, who has observed a similar dynamic over the same period between human capital and earnings . "These people were 30 in 1960. A lot of people were smoking in 1960, and there wasn't nearly as strong of an education gradient as we saw 30 years later."

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Eight Recommendations to Promote Effective Study Habits for Biology Students Enrolled in Online Courses

Sharday n. ewell.

a Department of Biological Sciences, Auburn University, Auburn, Alabama, USA

Sehoya Cotner

b Department of Biological Sciences/bioCEED Centre for Excellence in Biology Education, University of Bergen, Bergen, Norway

c Department of Biology Teaching and Learning, University of Minnesota, Minneapolis, Minnesota, USA

Abby Grace Drake

d Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA

Sheritta Fagbodun

e Department of Biology, Tuskegee University, Tuskegee, Alabama, USA

Angela Google

f Department of Biology, University of South Alabama, Mobile, Alabama, USA

Lecia Robinson

Paula soneral.

g Department of Biological Sciences, Bethel University, St. Paul, Minnesota, USA

Cissy J. Ballen

To achieve meaningful learning experiences in online classrooms, students must become self-regulated learners through the development of effective study habits. Currently, there is no set of recommendations to promote study habits in online biology learning environments. To fill gaps in our understanding, a working group associated with a research coordination network (Equity and Diversity in Undergraduate STEM, EDU-STEM) convened virtually in June 2021. We identify student barriers to self-regulated learning in online environments and present eight practical recommendations to help biology educators and biology education researchers apply and advance evidence-based study habits in online courses. As higher education institutions continue to offer online learning opportunities, we hope this essay equips instructors with the knowledge and tools to promote student success in online biology coursework.

PERSPECTIVE

The global spread of COVID-19 forced universities to rapidly shift from face-to-face instruction to virtual learning. During this time, students completed their coursework online, and had limited access to their peers, instructors, and other learning supports ( 1 ). Students expressed stress related to learning in this environment, citing challenges with completing coursework as well as a negative perception of online learning ( 1 ). While some students thrived with the increased level of autonomy offered by online learning, others viewed it negatively and struggled with time management, motivation, and focus ( 1 , 2 ). These challenges indicate that students need more guidance and support to achieve meaningful learning experiences in online classrooms. Consistent with this, a survey distributed to faculty in 672 U.S. institutions at the start of the pandemic indicated that the rapid shift to online learning left faculty, many of whom who had no prior experience with teaching online, with limited information on how to best support their students ( 3 ). Specifically, faculty desired more information for students on how to succeed in online learning environments ( 3 ).

Prior to the pandemic, students drove increasing enrollments in online courses across the United States due to their accessibility, flexibility, and convenience ( 4 ). However, students frequently emphasize their preference for face-to-face courses over online courses due to belief that face-to-face courses provide explicit instruction necessary to be academically successful, especially in classes that students perceive as difficult ( 5 ). According to Jaggars (2014), students perceived learning less in online classes and resorted to reading from the textbook to obtain information ( 5 ). Thus, to best support students who enroll in online courses, faculty must understand the perceptions, strategies, and barriers students encounter in these environments.

One of the largest barriers to student success in online courses is self-regulated learning (SRL). SRL refers to how students proactively transform their mental abilities to academic skills through self-generated thoughts, feelings, and behaviors ( 6 ). Students who lack the SRL necessary for online courses experience negative academic outcomes ( 7 , – 10 ). According to Zimmerman’s framework of SRL, there are three critical components by which self-regulated learners engage in their own learning: metacognition, motivation, and behavior ( 6 , 11 ). For example, self-regulated learners may have the ability to monitor their own learning (metacognition), have intrinsic interest in their studies (motivation), and employ effective study habits (behavior) ( 5 , 6 , 11 ). In this essay, we focus on the application of appropriate study habits because students report significant struggle in this area, particularly in online environments that demand high-SRL skills ( 12 , 13 ). Study habits encompass a variety of behaviors that include what strategies students use to learn, understand, and retain course content, how much time is spent studying, and how students distribute their study time over the course of a semester ( 12 , 14 ). As the transition to emergency remote learning demonstrated, these skills are underdeveloped in students, particularly those enrolled in large, introductory classes, and many students developed a negative attitude toward online learning as a result ( 1 , 2 ). How can faculty equip their students with the cognitive tools necessary to study effectively and succeed in the online environment?

To address this question, faculty must first understand why students relied on ineffective study habits during online learning. In this paper, we present a framework for challenges and solutions related to the promotion of study habits in the online environment. We explored these ideas during a breakout discussion of participants from a Research Coordination Network, Equity and Diversity in Undergraduate STEM (EDU-STEM) in June of 2021. During this breakout session, a group of faculty who represent a variety of institutional types, including community colleges, private undergraduate colleges, research-intensive institutions, and minority serving institutions gathered to discuss experiences with emergency remote teaching and identify best practices for supporting students in the online learning environment. The goal of this session was to come to a consensus regarding student challenges and best practices to promote successful study habits in online instruction. During the pandemic, we taught large enrollment introductory biology classes through asynchronous or synchronous Zoom sessions and used a variety of learning management systems (LMS) (e.g., Canvas, Moodle, Blackboard) to distribute course-related information to students.

Here, we identify challenges for student development of effective study habits in online courses and offer eight recommendations for instructors to help students overcome these challenges and develop the tools necessary to be successful in biology.

CHALLENGES FOR STUDENT DEVELOPMENT OF EFFECTIVE STUDY HABITS IN ONLINE COURSES

A goal of college courses is to teach students how to be self-regulated learners, or students who actively participate in their own learning by adjusting their efforts, approaches, and behaviors to achieve their learning goals ( 6 , 11 , 12 ). Students often enter college with underdeveloped self-regulatory skills ( 11 , 12 , 15 ). Specifically, students lack the ability to effectively assess their learning and often feel that they learn more from cognitively superficial study habits such as re-reading the textbook or their lecture notes ( 11 ). This outcome is particularly common among students enrolled in online coursework. Due to limited instructor–student interaction, online courses require students to assume greater responsibility for their learning, to actively monitor their performance, and to apply appropriate study strategies to be academically successful ( 9 , 16 ). However, despite the importance of this skill, students enrolled in online classes may be unaware of how to effectively implement SRL or face serious challenges out of their control that result in poor performance outcomes in online courses ( 4 , 17 ).

Below we summarize discussions among the contributing authors, who are biology instructors from a Research Coordination Network (Equity and Diversity in Undergraduate STEM, or EDU-STEM) about central challenges students face in their efforts to study and prepare for biology courses. These include: (i) low motivation and low self-efficacy; (ii) depression and anxiety; (iii) Zoom fatigue; (iv) inability to access network of peers; and (v) work/life balance ( Table 1 ).

TABLE 1

Challenges students experience when attempting to prepare and study for online biology classes

Low motivation and low self-efficacy

Students reported that the abrupt shift from face-to-face learning to online learning environments resulted in difficulties with SRL ( 1 ). Self-efficacy (student perception that they are capable of learning) and motivation (intrinsic motivation to learn) have been identified as factors that contribute to SRL development in online environments ( 1 , 16 , 17 ). Students who have high self-efficacy and are highly motivated tend to appropriately control their learning process and utilize appropriate study strategies in order to achieve their learning goals. However, students who have low self-efficacy or lack motivation fail to use these strategies and experience negative academic outcomes as a result (e.g.,- spending more time on assignments, failing to submit assignments on time, and submitting poor quality work) ( 1 , 18 ). Research into online learning environments corroborates previous research; specifically, Landrum (2020) found student confidence in their ability and motivation to learn in an asynchronous, online environment in the absence of peers and instructors to be a predictor of student performance and persistence ( 19 ).

Anxiety and depression

Prior to the COVID-19 pandemic, institutions across the United States were reckoning with a growing mental health crisis, with an increasing number of students seeking mental health treatment for anxiety and depression ( 20 , 21 ). Anxiety is a common mood disorder that is characterized by excessive worrying and hypervigilance. It is often associated with depression, which is characterized by persistent feelings of sadness and hopelessness ( 21 , – 23 ). These feelings can be accompanied by loss of energy, difficulty concentrating, and difficulty sleeping ( 21 ). Given that mental health can affect student motivation and concentration, it has been identified as a leading barrier to academic success ( 21 , 24 ). During COVID-19 online learning, the pandemic lockdown exacerbated mental health issues such as depression in undergraduate students due, in part, to concerns about their own well-being, as well as the health of their friends and family, less interaction with others, and less community support ( 24 , – 29 ). Women, students of color, LGBTQ+ students, and students from lower-than-average socio-economic backgrounds were disproportionately affected by lockdown and reported higher levels of anxiety and depression ( 20 , 25 , 30 , 31 ). Many students reported that the transition to online classes and difficulty with online learning were major stressors that resulted in higher rates of anxiety and depression ( 1 , 20 , 24 ). Consequently, students reported increased workloads, more difficulty in completing academic tasks due to physical isolation from instructors or teaching assistants, and struggling to concentrate ( 1 , 24 ). Consistent with these findings, undergraduate and postgraduate students reported that mental health difficulties negatively affected their ability to study ( 32 ). Despite the urgent need for increased mental health support, previous research showed students perceived barriers to seeking help from professionals or peers due to fear of judgment ( 21 , 24 ). Interestingly, despite the difficulties reported with online coursework, some students reported the use of effective study habits (e.g., setting a study schedule) as a coping mechanism for their anxiety ( 24 ).

Zoom fatigue

The COVID-19 pandemic caused a rapid rise in the use of video conferencing systems, such as Zoom, Google Meet, Blackboard Ultra, and Microsoft Teams. For example, Zoom video conferencing increased from approximately 10 million daily Zoom meeting participants in December 2019 to 200 million in March 2020 and 300 million in April 2020 ( 33 ). The increase in screen and sitting time caused many users to experience fatigue or burnout. A new phrase, Zoom fatigue, or Zoom burnout, paralleled the growth in Zoom usage peaking in popularity in late April 2020 and July 2021 respectively (data source: Google Trends [ https://www.google.com/trends ]).

Zoom fatigue refers to the mental and physical exhaustion people feel after participating in prolonged periods of virtual meetings. It is caused by excessive amounts of closeup eye gaze, cognitive load, increased self-evaluation from staring at video of oneself, and constraints on physical mobility and communication ( 34 ). Users reported increases in physical, behavioral, and psycho-emotional problems ( 35 , 36 ), including difficulty concentrating, physical exhaustion, anxiety, irritability, headache, eye strain, and increased pessimism. Furthermore, Zoom fatigue has been attributed to the fatigue associated with concerns regarding self-presentation (i.e., being watched on camera enhances need to manage impression and turns focus inward) ( 37 ). Ultimately, this limits participant engagement with the virtual meeting ( 37 ).

Unsurprisingly, Zoom fatigue has resulted in students reporting moderate to considerable difficulty with online learning. Learning, even in online environments, requires active engagement, and students have not received training on how to actively engage in asynchronous or synchronous Zoom sessions ( 38 ). As a result, students reported increased distractions and struggled with remembering the materials presented during asynchronous or synchronous sessions due to the passive intake of information and lack of engagement in the content that would promote deeper learning ( 38 ). As a result, students may rely heavily on passive strategies (e.g., re-reading the textbook, re-watching lecture videos) during their study periods to fill the gaps in their knowledge.

Isolation from peers/ability to access network of peers

Past research highlights how undergraduate students rely on their peers to teach and learn disciplinary content related to coursework ( 39 , 40 ) and employ effective study habits ( 41 , 42 ). Research on information seeking behavior , or the way people search for and utilize information ( 43 ), has demonstrated that students and the public actually prefer to obtain information from human sources ( 44 ). For example, students’ information seeking behavior may involve actively finding relevant information about the syllabus from a peer to prepare for an exam.

During face-to-face instruction, students are immersed in classrooms with other students and plenty of opportunities exist to interact with peers before, during, and after class in such a way that supports collaborative learning. In online learning environments, however, instructors or course developers must implement virtual structures that support collaborative learning with peers. The lack of default opportunities to engage in discussions with peers represents a potential obstacle for students, who will not reap the benefits of these interactions in their learning. These obstacles may be particularly high for first-year students, transfer students, or those who are unfamiliar with others who are in the class. Previous research documented how students’ studying for exams in large active learning organismal biology classes differed 1 week and 1 month after a university's decision to transition to emergency remote instruction due to COVID-19 ( 45 ). They found that at both time points, one of the largest concerns reported by students related to a lack of access to their in-class assigned groups. One student acknowledged, “I was unable to study with friends and had no way of knowing if my knowledge had holes in it.” This reflection speaks to the importance of student access to their network of peers in preparing for in-class assessments.

Work-life balance

Students in online learning environments may experience challenges in managing the expectations of work, school, and home ( 46 ). In traditional face-to-face learning environments, there are firm barriers between learning time, family time, work time, and leisure time that allow students to schedule and complete activities associated with each task (e.g., setting up dedicated study time) ( 46 ). However, as online learning became increasingly prevalent during the COVID-19 pandemic, these boundaries between school, family, work, and leisure time began to blur and students struggled with adjusting their behaviors to balance each of these domains ( 1 , 46 ). The pandemic highlighted the sizeable population of students for whom college was not their only focus. Such students may hold one or two jobs to cover tuition costs and other living expenses. Others may have family responsibilities such as caring for a younger sibling, elderly parent(s), or caring for their own family and children.

EIGHT RECOMMENDATIONS TO DEVELOP STUDENT STUDY HABITS IN ONLINE BIOLOGY COURSES

After discussing challenges for students and why some might rely on ineffective study habits during online learning, we explored recommendations for instructors during a breakout discussion of participants from Research Coordination Network EDU-STEM. While each recommendation will profit from more research in the context of undergraduate biology, they represent the opinions of experienced faculty who teach biology from a range of institutions and serve as a starting point into empirical work on these topics. The eight recommendations aimed at promoting effective study habits include (i) establish content (and digital) learning objectives; (ii) align assessments and assignments with learning objectives; (iii) high quality feedback; (iv) increase scaffolding; (v) incorporate multiple due dates; (vi) incorporate online formative assessments; (vii) provide resources outside of textbook and recorded lectures; and (viii) facilitate student-content engagement with interactive instructional materials ( Table 2 ).

TABLE 2

Recommendations for developing effective study habits

Recommendation 1: establishing content (and digital) learning objectives

We recommend that online instructors select and articulate not only biology content and science process learning objectives, but also digital learning objectives that develop the skills and confidence of students in the effective use of the digital technology that supports their learning. In the beginning weeks of a course, instructors can easily create digital objectives for navigating the LMS (e.g., “students will be able to access course announcements”). However, students need continuous practice using these online tools and it is important for instructors to incorporate their content objectives with digital objectives. For example, instructors who wish for their students to gain core knowledge may articulate low-level Bloom's objectives and select a quizzing strategy through adaptive learning programs that allow students to practice factual knowledge. In this instance, it is equally important for instructors to name the learning objective for the digital output (e.g., “students will demonstrate proficiency in timed online multiple-choice quizzing”) and provide adequate training for how to use this platform of practice effectively (e.g., “treat the answer choices as multiple true-false statements for extra practice”). Similarly, instructors may wish for their students to apply recently learned content to a real-world scenario and may require students to complete the assignment in online collaborative groups. Thus, for this assignment the digital objective would be “students will demonstrate proficiency in accessing student groups on the LMS and contacting groupmates.” In doing so, student content learning and digital skills are made as explicit as the modality in which they will demonstrate those outcomes. Class time may be used to model effective online learning study skills calibrated to the learning objectives and digital mode, and students may be assigned and encouraged to use and transfer these skills independently as they grow in their metacognitive awareness and self-regulation.

Although low-level Bloom’s objectives are commonly utilized in online spaces ( 47 ), higher order skills may be assessed using alternative assessment styles in the digital space. For example, conceptual models may be assessed in a drawing app (Google JamBoard) or problem-solving, and argumentation skills may be assessed using video apps. Regardless of the digital output, the core tenet of explicitly stating the digital learning objectives and outputs along with the desired content and scientific skill objectives is key.

Recommendation 2: aligning assessments and assignments with learning objectives

Alignment of course activities and assessment methods with learning objectives is critical for effective course design across in-person and online learning environments ( 48 , – 53 ). Through this alignment, instructors can clearly communicate to students what is expected of them, and work shows that students find properly aligned objectives helpful in highlighting what they are expected to know ( 54 ). Additionally, undergraduate science students believe that learning objectives are a helpful tool to narrow down and organize their studying while preparing for exams ( 54 ). But how do students use learning objectives to study? In a study conducted by Osueke et al. (2018), students indicated that they use learning objectives for self-assessment (e.g., answering the learning objectives as questions or self-testing using the learning objectives) and as a resource for studying (e.g., using the learning objectives as a study guide) ( 54 ).

Given that online students may not know how to use learning objectives as a resource for their learning, we recommend that instructors not only clearly define and explicitly communicate learning objectives to students, but also provide explicit instruction on how to use learning objectives for self- assessment.

Recommendation 3: high quality feedback

In large foundational face-to-face STEM courses, students commonly receive formal instructor feedback only a few times throughout the semester, generally after large consequential exams that account for a significant part of the students’ grades. However, it’s easier for instructors to engage in informal dialogue and gauge student understanding in face-to-face settings, and students may benefit from unstructured or unplanned feedback on assignments, assessments, or other important information. In some online learning environments, such as those where the instructor is teaching an online class of black screens, it is more difficult to ‘read the room’ and know when students need clarification on a topic. In these online contexts, providing high quality feedback becomes a crucial element of the learning process ( 55 ). Student reports also show instructor feedback is used as a method for improving learning or study strategies ( 56 ). Students struggle with constructing meaning from online content and identifying concepts to study, underlining the need for instructors to provide high quality feedback on student assignments and assessments. This fosters student motivation and can provide information to help shape learning ( 55 ).

Recommendation 4: scaffolding

The incorporation of specific design elements, such as active learning strategies, student engagement, and assessment strategies, guide students in how to manage their time, self-assess their understanding of material, and promote satisfaction in learning. These design elements, also known as scaffolding, refer to the teacher-generated support a learner is given to accomplish a specific task ( 57 ). For example, an instructor can teach students how to engage with recorded lectures by providing guided note taking sheets ( 58 ). Additionally, instructors can encourage students to self-assess by providing frequent short practice tests and quizzes ( 58 ). As a learner achieves mastery in a specific task, the teacher may remove the support and pass the responsibility of learning on to the learner ( 57 ). Interestingly, scaffolds also allow students to think deeply about the content and are particularly effective in helping students develop study strategies and time management skills ( 17 ).

Prior to and during the pandemic, students commonly reported that online learning was subpar compared to face-to-face learning and that they simply did not learn as much in their online classes ( 1 , 58 ). Students also cited poor time management, inability to assess learning, and lack of access to supporting resources as barriers that contributed to their negative experience and poor academic outcomes ( 1 , 45 ). However, by including scaffolds in their learning management systems, instructors can elicit appropriate study habits from students.

Recommendation 5: multiple due dates

Students prefer clear and consistent due dates for assignments in online courses, and previous work shows this relates to their self-perceived learning and learning satisfaction ( 58 ). However, during the COVID-19 pandemic, students frequently expressed issues with procrastination and turning work in on time despite having a clear due date ( 1 ). This, along with a perceived increase in workload and inability to construct meaning from recorded lectures, can result in students using ineffective study habits (e.g., “cramming”) during their study time. To limit these issues, we recommend that instructors move due dates to multiple times during the week instead of requiring students to submit all of their assignments on one day ( 17 , 59 ). Setting multiple due dates allows students to work in smaller, manageable time increments. Additionally, this easy change models the highly effective study strategy of spacing (i.e., studying across multiple sessions instead of cramming) for students. Specifically, setting multiple due dates encourages students to work on assignments at multiple points during the week and ultimately promotes greater long-term learning ( 12 , 17 ).

Recommendation 6: incorporating online formative assessments

Due to Zoom fatigue and other factors affecting focus, students have struggled with constructing meaning from the lectures posted by instructors. Interestingly, prior studies have demonstrated that online formative assessments are essential for gaining, refocusing, and extending student attention following STEM lectures ( 58 ). Therefore, to assist students in understanding the content presented, it is recommended that instructors incorporate frequent, low-stakes online formative assessments (i.e., activities that are a small portion of students’ grades and are intended to generate feedback on learning progress) ( 60 ). Formative assessments encompass a variety of activities such as weekly quizzes, homework assignments, group discussions, and in-class polling. While frequent, low-stakes formative assessments are important for students in all classroom contexts, they are especially important for online learners who may struggle with self-regulated learning because they provide students with immediate feedback and explanations that students can use to modify their learning ( 58 ). Thus, instructors should incorporate online formative assessments into their LMS. These assessments can be created through various learning management systems such as Canvas, Blackboard, or Moodle. Alternatively, instructors can use tools such as EdPuzzle ( www.edpuzzle.com ), Quizizz ( www.quizizz.com ), Kahoot! ( https://kahoot.com/ ), or Quizlet ( www.quizlet.com ) to create formative assessments for their students.

Recommendation 7: providing resources outside of textbook and recorded lectures

In an institutional survey given to students enrolled in online STEM courses, students indicated that they enjoyed course-related videos that allowed for greater understanding of course content ( 58 ). While some students are considered to be digital natives, or those who were exposed to computers and digital technology from an early age, many students struggle with using digital tools, such as navigating learning management systems, for academic purposes. This means that the tools provided to students intended to enhance their learning may inadvertently serve as a barrier to learning. We suggest that instructors provide links to other resources (e.g.,- YouTube videos, instructor generated guided notes, simulations) that provide students with different ways to engage with and conceptualize the content ( 17 ).

Recommendation 8: facilitate student-content engagement with interactive instructional materials

Learner interaction with content has previously been identified as a key factor that supports learning in online courses ( 61 ). This interaction can take a number of forms (e.g., watching instructional videos, interaction with multimedia, and searching for information) and requires instructors to take an active role in facilitating sustained engagement with the course material ( 61 , 62 ). Thus, online instructors are encouraged to invest time in searching for interactive instructional materials ( 62 ). However, during the COVID-19 pandemic, instructors were forced to rapidly shift to online teaching, and many had to do so without previous online teaching experience. As a result, like many newcomers to online teaching, instructors simply replicated their traditional classroom model to an online platform without alteration to account for the new instructional context ( 63 ). This left some students struggling to effectively engage with and study the content, which may contribute to the perception that all online learning was less interactive ( 64 ) and less motivating ( 65 ) than face-to-face instruction. Instructors can address some of these concerns by creating interactive, online content compatible with asynchronous or synchronous environments. Interactivity can enhance student motivation by increasing autonomy, such that the user must make choices and direct the pace of their own learning, and a sense of competence, such that lessons can be scaffolded so that novel concepts are not introduced until initial concepts are mastered ( 66 , – 68 ).

Several tools exist for creating interactive content—tools that go beyond what is native to an existing LMS. Tools that are either free or relatively low-cost include Edpuzzle ( https://edpuzzle.com/ ), Quizizz ( www.quizizz.com ), Kahoot! ( https://kahoot.com/ ), Quizlet ( www.quizlet.com ), Nearpod ( www.nearpod.com ), Wordwall ( https://wordwall.net/ ), and H5P ( https://h5p.org/ ). These tools allow the instructor to create activities (e.g., multiple choice quizzing, interactive video presentations, simulations) that are scaffolded, tailored to course content, and aligned with course outcomes ( Fig. 1 ). Further, these tools can be embedded into several existing LMS platforms such Canvas, Moodle, and Blackboard, and shared easily with colleagues.

Examples of interactive activities on H5P from Cotner and Wassenberg ( 72 ).

The goal of this essay was to identify challenges students face studying for online biology courses and to provide recommendations for instructors to foster the development of effective study strategies. Our recommendations were informed by our experiences with teaching online biology courses and by the current state of knowledge regarding best practices for online teaching. As instructors communicate expectations to students through pedagogical choices (e.g., use of instructional time, assignments, course structure, scaffolding, and organization), students will adjust to these situational and environmental cues with explicit training and modeling for how to study and master the material in the online learning space. We hope that iterative, high-structure, and developmental approaches in the online learning space will result in similar positive impacts as previously reported in physical classrooms ( 69 , – 71 ).

ACKNOWLEDGMENTS

We thank the following members of the Ballen lab for valuable feedback on the manuscript and early discussions on this topic: Abby Beatty, Ariel Steele, Chloe Josefson, Emily Driessen, Todd Lamb, Peyton Brewer, William Grogan, Quinn Johnston, and Rachel Youngblood. Additionally, we thank Jordan Harshman, Ngawang Gonsar, and Marcos E. Garcia-Ojeda for their contributions to early discussions on this topic. This work was supported by a research coordination network grant NSF DBI-1919462. Any opinions, findings, conclusions, and recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

The authors certify that they have no affiliations or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent licensing arrangements) or nonfinancial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.

We declare no conflicts of interest.

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• Half of Latinas Say Hispanic Women’s Situation Has Improved in the Past Decade and Expect More Gains

Government data shows gains in education, employment and earnings for Hispanic women, but gaps with other groups remain

Table of contents.

• Assessing the progress of Hispanic women in the last 10 years
• Views of Hispanic women’s situation in the next 10 years
• Views on the gender pay gap
• Latinas’ educational attainment
• Latinas’ labor force participation
• Latinas’ earnings
• Latinas as breadwinners in their relationships
• Bachelor’s degrees among Latinas
• Labor force participation rates among Latinas
• Occupations among working Latinas
• Earnings among Latinas
• Latinas as breadwinners in 2022
• Appendix: Supplemental charts and tables
• Acknowledgments
• The American Trends Panel survey methodology
• Methodology for the analysis of the Current Population Survey

This report explores Latinas’ economic and demographic progress in the last two decades – and their perceptions of that progress – using several data sources.

The first is a Pew Research Center survey of 5,078 Hispanic adults, including 2,600 Hispanic women. Respondents were asked whether U.S. Latinas saw progress in their situation in the last decade, whether they expected any in the future decade, and how big a problem the U.S. gender pay gap is. The survey was conducted from Nov. 6 to 19, 2023, and includes 1,524 respondents from the American Trends Panel (ATP) and an additional 3,554 from Ipsos’ KnowledgePanel .

Respondents on both panels are recruited through national, random sampling of residential addresses. Recruiting panelists by mail ensures that nearly all U.S. adults have a chance of selection. This gives us confidence that any sample can represent the whole population, or in this case the whole U.S. Hispanic population. (For more information, watch our Methods 101 explainer on random sampling.) For more information on this survey, refer to the American Trends Panel survey methodology and the topline questionnaire .

The second data source is the U.S. Census Bureau’s and Bureau of Labor Statistics’ 2003, 2008, 2013, 2018 and 2023 Current Population Survey (CPS) Monthly and Annual Social and Economic Supplement (ASEC) data series, provided through the Integrated Public Use Microdata Series (IPUMS) from the University of Minnesota.

The CPS Monthly microdata series was used only to calculate median hourly earnings for those ages 25 to 64 years old and who were not self-employed. Medians were calculated for the whole year by considering all wages reported in that year, regardless of month. Median wages were then adjusted to June 2023 dollars using the Chained Consumer Price Index for All Urban Consumers for June of each year. For more information on the demographic analysis, refer to the methodology for the analysis of the Current Population Survey .

The terms  Hispanic  and  Latino  are used interchangeably in this report.

The terms Latinas and Hispanic women are used interchangeably throughout this report to refer to U.S. adult women who self-identify as Hispanic or Latino, regardless of their racial identity.

Foreign born  refers to persons born outside of the 50 U.S. states or the District of Columbia. For the purposes of this report, foreign born also refers to those born in Puerto Rico. Although individuals born in Puerto Rico are U.S. citizens by birth, they are grouped with the foreign born because they are born into a Spanish-dominant culture and because on many points their attitudes, views and beliefs are much closer to those of Hispanics born outside the U.S. than to Hispanics born in the 50 U.S. states or D.C., even those who identify themselves as being of Puerto Rican origin.

The terms  foreign born  and  immigrant  are used interchangeably in this report. Immigrants are also considered first-generation Americans.

U.S. born  refers to persons born in the 50 U.S. states or D.C.

Second generation  refers to people born in the 50 U.S. states or D.C. with at least one immigrant parent.

Third or higher generation  refers to people born in the 50 U.S. states or D.C., with both parents born in the 50 U.S. states or D.C.

Throughout this report, Democrats are respondents who identify politically with the Democratic Party or those who are independent or identify with some other party but lean toward the Democratic Party. Similarly, Republicans are those who identify politically with the Republican Party and those who are independent or identify with some other party but lean toward the Republican Party.

White, Black  and  Asian each include those who report being only one race and are not Hispanic.

Civilians are those who were not in the armed forces at the time of completing the Current Population Survey.

Those participating in the labor force either were at work; held a job but were temporarily absent from work due to factors like vacation or illness; were seeking work; or were temporarily laid off from a job in the week before taking the Current Population Survey. In this report, the labor force participation rate is shown only for civilians ages 25 to 64.

The phrases living with children or living with their own child describe individuals living with at least one of their own stepchildren, adopted children or biological children, regardless of the children’s ages. The phrases not living with children or not living with their own child describe individuals who have no children or whose children do not live with them.

Occupation and occupational groups describe the occupational category of someone’s current job, or – if unemployed – most recent job. In this report we measure occupation among civilians participating in the labor force. Occupational groups are adapted from the U.S. Census Bureau’s occupation classification list from 2018 onward .

Hourly earnings , hourly wages and hourly pay all refer to the amount an employee reported making per hour at the time of taking the Current Population Survey where they were employed by someone else. Median hourly wages were calculated only for those ages 25 to 64 who were not self-employed. Calculated median hourly wages shared in this report are adjusted for inflation to 2023. (A median means that half of a given population – for example, Hispanic women – earned more than the stated wage, and half earned less.)

Breadwinners refer to those living with a spouse or partner, both ages 25 to 64, who make over 60% of their and their partner’s combined, positive income from all sources. Those in egalitarian relationships make 40% to 60% of the combined income. For those who make less than 40% of the combined income, their spouse or partner is the breadwinner . This analysis was conducted among both opposite-sex and same-sex couples.

Half of Latinas say the situation of Hispanic women in the United States is better now than it was 10 years ago, and a similar share say the situation will improve in the next 10 years.

Still, 39% of Latinas say that the situation has stayed the same, and 34% say it will not change in the next 10 years. Two-thirds (66%) say the gender pay gap – the fact that women earn less money, on average, than men – is a big problem for Hispanic women today, according to new analysis of Pew Research Center’s National Survey of Latinos.

At 22.2 million, Latinas account for 17% of all adult women in the U.S. today. Their population grew by 5.6 million from 2010 to 2022, the largest numeric increase of any major female racial or ethnic group. 1

Latinas’ mixed assessments reflect their group’s gains in education and at work over the last two decades, but also stalled progress in closing wage gaps with other groups.

• Hispanic women are more likely to have a bachelor’s degree today (23% in 2023) than they were in 2013 (16%). More Hispanic women than ever are also completing graduate degrees .
• Hispanic women have increased their labor force participation rate by 4 percentage points, from 65% in 2013 to 69% in 2023.
• The median hourly wage of Hispanic women has increased by 17% in the last decade. In 2023, their median hourly wage was $19.23, up from $16.47 in 2013 (in 2023 dollars).

Despite this progress, Hispanic women’s pay gaps with their peers haven’t significantly improved in recent years:

• The gender pay gap among Hispanics persists with no significant change. In 2023, Hispanic women earned 85 cents (at the median) for every dollar earned by Hispanic men, compared with 89 cents per dollar in 2013 (and 87 cents per dollar in 2003).
• Hispanic women continue to lag non-Hispanic women in earnings , with no significant improvement in the past decade. In 2023, the median Hispanic woman made 77 cents for each dollar earned by the median non-Hispanic woman, compared with 75 cents per dollar in 2013.
• The pay gap between Hispanic women and White men has changed only slightly . In 2023, Hispanic women earned 62 cents of every dollar earned by non-Hispanic White men, up from 59 cents per dollar in 2013.

In addition, Hispanic women lag Hispanic men and non-Hispanic women in labor force participation, and they lag non-Hispanic women in educational attainment. Read more in Chapter 2 .

Among Latinas who are employed, about half (49%) say their current job is best described as “just a job to get them by.” Fewer see their job as a career (30%) or a steppingstone to a career (14%).

Pew Research Center’s bilingual 2023 National Survey of Latinos – conducted Nov. 6-19, 2023, among 5,078 Hispanic adults, including 2,600 Hispanic women – explores what it’s like to be a Latina in the U.S. today. This report uses findings from our 2023 survey as well as demographic and economic data from the Current Population Survey.

The following chapters take a closer look at:

• How Latinas view the progress and future situation of Hispanic women in the U.S.
• What government data tells us about Latinas’ progress in the labor market, earnings and educational attainment
• How Latinas’ educational and economic outcomes vary

For additional survey findings on what it means to be a Latina in the U.S. today and the daily pressures they face, read our report “A Majority of Latinas Feel Pressure To Support Their Families or To Succeed at Work.”

• Latinas’ population size and growth rate from 2010 to 2022 were calculated using the 2010 and 2022 American Community Surveys, accessed through IPUMS. The rest of the demographic analysis in this post uses data from the Current Population Survey. ↩

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Key facts about U.S. Latinos with graduate degrees

Hispanic enrollment reaches new high at four-year colleges in the u.s., but affordability remains an obstacle, u.s. public school students often go to schools where at least half of their peers are the same race or ethnicity, what’s behind the growing gap between men and women in college completion, for u.s. latinos, covid-19 has taken a personal and financial toll, most popular, report materials.

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In food, collagen is naturally found only in animal flesh like meat and fish that contain connective tissue. However, a variety of both animal and plant foods contain materials for collagen production in our own bodies.

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Collagen Supplementation

Despite its abundance in our bodies, collagen has become a top-selling supplement purported to improve hair, skin, and nails—key components of the fountain of youth. The idea of popping a pill that doesn’t have side effects and may reverse the signs of aging is attractive to many. According to Google Trends, online searches for collagen have steadily increased since 2014.

Collagen first appeared as an ingredient in skin creams and serums. However, its effectiveness as a topical application was doubted even by dermatologists, as collagen is not naturally found on the skin’s surface but in the deeper layers. Collagen fibers are too large to permeate the skin’s outer layers, and research has not supported that shorter chains of collagen, called peptides, are more successful at this feat.

Oral collagen supplements in the form of pills, powders, and certain foods are believed to be more effectively absorbed by the body and have skyrocketed in popularity among consumers. They may be sold as collagen peptides or hydrolyzed collagen, which are broken down forms of collagen that are more easily absorbed. Collagen supplements contain amino acids, the building blocks of protein , and some may also contain additional nutrients related to healthy skin and hair like vitamin C , biotin , or zinc .

What does the research say on collagen supplements?

Most research on collagen supplements is related to joint and skin health. Human studies are lacking but some randomized controlled trials have found that collagen supplements improve skin elasticity. [3,4] Other trials have found that the supplements can improve joint mobility and decrease joint pain such as with osteoarthritis or in athletes. [5] Collagen comprises about 60% of cartilage, a very firm tissue that surrounds bones and cushions them from the shock of high-impact movements; so a breakdown in collagen could lead to a loss of cartilage and joint problems.

However, potential conflicts of interest exist in this area because most if not all of the research on collagen supplements are funded or partially funded by related industries that could benefit from a positive study result, or one or more of the study authors have ties to those industries. This makes it difficult to determine how effective collagen supplements truly are and if they are worth their often hefty price.

A downside of collagen supplements is the unknown of what exactly it contains or if the supplement will do what the label promotes. There are also concerns of collagen supplements containing heavy metals. In the U.S., the Food and Drug Administration does not review supplements for safety or effectiveness before they are sold to consumers.

Another potential downside is that taking a collagen supplement can become an excuse to not practice healthy behaviors that can protect against collagen decline, such as getting enough sleep and stopping smoking.

That said, the available research has not shown negative side effects in people given collagen supplements. [3,4]

Can You Eat Collagen?

Food containing collagen

• There are foods rich in collagen, specifically tough cuts of meat full of connective tissue like pot roast, brisket, and chuck steak. However, a high intake of red meat is not recommended as part of a long-term healthy and environmentally sustainable diet . Collagen is also found in the bones and skin of fresh and saltwater fish. [2]
• Bone broth, a trending food featured prominently in soup aisles, is promoted as a health food rich in collagen. The process involves simmering animal bones in water and a small amount of vinegar (to help dissolve the bone and release collagen and minerals) anywhere from 4 to 24 hours. However, the amount of amino acids will vary among batches depending on the types of bones used, how long they are cooked, and the amount of processing (e.g., if it is a packaged/canned version).
• Gelatin is a form of collagen made by boiling animal bones, cartilage, and skin for several hours and then allowing the liquid to cool and set. The breakdown of these connective tissues produces gelatin. Collagen and its derivative, gelatin, are promoted on certain eating plans such as the paleo diet .

Foods to boost collagen production

• Several high-protein foods are believed to nurture collagen production because they contain the amino acids that make collagen—glycine, proline, and hydroxyproline. [6] These include fish, poultry, meat, eggs , dairy , legumes , and soy .
• Collagen production also requires nutrients like zinc that is found in shellfish, legumes, meats, nuts , seeds, and whole grains ; and vitamin C from citrus fruits, berries, leafy greens, bell peppers, and tomatoes.

Is bone broth healthy?

In reality, bone broth contains only small amounts of minerals naturally found in bone including calcium , magnesium , potassium , iron , phosphorus , sodium , and copper. The amount of protein , obtained from the gelatin, varies from 5-10 grams per cup.

There is some concern that bone broth contains toxic metals like lead. One small study found that bone broth made from chicken bones contained three times the lead as chicken broth made with the meat only. [7] However the amount of lead in the bone broth per serving was still less than half the amount permitted by the Environmental Protection Agency in drinking water. A different study found that bone broth, both homemade and commercially produced, contained low levels (<5% RDA) of calcium and magnesium as well as heavy metals like lead and cadmium. [9] The study noted that various factors can affect the amount of protein and minerals extracted in bone broth: the amount of acidity, cooking time, cooking temperature, and type of animal bone used. Therefore it is likely that the nutritional value of bone broths will vary widely.

Healthy Lifestyle Habits That May Help  

Along with a healthy and balanced diet , here are some habits that may help protect your body’s natural collagen:

• Wear sunscreen or limit the amount of time spent in direct sunlight (10-20 minutes in direct midday sunlight 3-4 times a week provides adequate vitamin D for most people).
• Get adequate sleep . For the average person, this means 7-9 hours a night.
• Avoid smoking or secondhand smoke.
• Control stress . Chronically high cortisol levels can decrease collagen production.
• Although the exact connection between exercise and skin quality is unclear, some studies have found that exercise slows down cell activity involved with aging skin. [10]  

Bottom Line

At this time, non-industry funded research on collagen supplements is lacking. Natural collagen production is supported through a healthy and balanced diet by eating enough protein foods , whole grains , fruits, and vegetables and reducing lifestyle risk factors.

• Rinnerhaler M, Bischof J, Streubel MK, Trost A, Richter K. Oxidative Stress in Aging Human Skin. Biomolecules . 2015 Apr 21;5(2):545-89.
• Avila Rodríguez MI, Rodriguez Barroso LG, Sánchez ML. Collagen: A review on its sources and potential cosmetic applications. Journal of Cosmetic Dermatology . 2018 Feb;17(1):20-6.
• Proksch E, Segger D, Degwert J, Schunck M, Zague V, Oesser S. Oral supplementation of specific collagen peptides has beneficial effects on human skin physiology: a double-blind, placebo-controlled study. Skin pharmacology and physiology . 2014;27(1):47-55.
• Kim DU, Chung HC, Choi J, Sakai Y, Lee BY. Oral intake of low-molecular-weight collagen peptide improves hydration, elasticity, and wrinkling in human skin: a randomized, double-blind, placebo-controlled study. Nutrients . 2018 Jul;10(7):826.
• Bello AE, Oesser S. Collagen hydrolysate for the treatment of osteoarthritis and other joint disorders: a review of the literature. Current medical research and opinion . 2006 Nov 1;22(11):2221-32.
• Lodish H, Berk A, Zipursky SL, et al. Molecular Cell Biology . New York: W. H. Freeman; 2000.
• Monro JA, Leon R, Puri BK. The risk of lead contamination in bone broth diets. Medical hypotheses . 2013 Apr 1;80(4):389-90.
• Global Market Insights. Worldwide Broth Market . Feb 26, 2018.
• Hsu DJ, Lee CW, Tsai WC, Chien YC. Essential and toxic metals in animal bone broths. Food & nutrition research . 2017 Jan 1;61(1):1347478.
• Crane JD, MacNeil LG, Lally JS, Ford RJ, Bujak AL, Brar IK, Kemp BE, Raha S, Steinberg GR, Tarnopolsky MA. Exercise‐stimulated interleukin‐15 is controlled by AMPK and regulates skin metabolism and aging. Aging cell . 2015 Aug;14(4):625-34.

Last reviewed May 2021

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E-Cigarette Use Among Youth

What to know.

E-cigarettes are the most commonly used tobacco product among U.S. youth. No tobacco products, including e-cigarettes, are safe, especially for children, teens, and young adults. Learn more about e-cigarette use among youth.

• In the United States, youth use e-cigarettes, or vapes, more than any other tobacco product. 1
• No tobacco products, including e-cigarettes, are safe, especially for children, teens, and young adults. 2
• Most e-cigarettes contain nicotine, which is highly addictive. Nicotine can harm the parts of an adolescent's brain that control attention, learning, mood, and impulse control. 2
• E-cigarette marketing, the availability of flavored products, social influences, and the effects of nicotine can influence youth to start or continue vaping. 3 4
• Most middle and high school students who vape want to quit. 5
• Many people have an important role in protecting youth from vaping including parents and caregivers, educators and school administrators, health care providers, and community partners.
• States and local communities can implement evidence-based policies, programs, and services to reduce youth vaping.

E-cigarette use among U.S. youth

In 2023, e-cigarettes were the most commonly used tobacco product among middle and high school students in the United States. In 2023: 6

• 550,000 (4.6%) middle school students.
• 1.56 million (10.0%) high school students.
• Among students who had ever used e-cigarettes, 46.7% reported current e-cigarette use.
• 1 in 4 (25.2%) used an e-cigarette every day.
• 1 in 3 (34.7%) used an e-cigarette on at least 20 of the last 30 days.
• 9 in 10 (89.4%) used flavored e-cigarettes.
• Most often used disposable e-cigarettes (60.7%) followed by e-cigarettes with prefilled or refillable pods or cartridges (16.1%).
• Most commonly reported using the following brands: Elf Bar, Esco Bars, Vuse, JUUL, and Mr. Fog.

Most middle and high school students who vape want to quit and have tried to quit. 5 In 2020:

• 63.9% of students who currently used e-cigarettes reported wanting to quit.
• 67.4% of students who currently used e-cigarettes reported trying to quit in the last year.

Most tobacco use, including vaping, starts and is established during adolescence. There are many factors associated with youth tobacco product use . These include:

• Tobacco advertising that targets youth.
• Product accessibility.
• Availability of flavored products.
• Social influences.
• Adolescent brain sensitivity to nicotine.

Some groups of middle and high school students use e-cigarettes at a higher percentage than others. For example, in 2023: 6

• More females than males reported current e-cigarette use.
• Non-Hispanic multiracial students: 20.8%.
• Non-Hispanic White students: 18.4%.
• Hispanic or Latino students: 18.2%.
• Non-Hispanic American Indian and Alaska Native students: 15.4%.
• Non-Hispanic Black or African American students: 12.9%.

Many young people who vape also use other tobacco products, including cigarettes and cigars. 7 This is called dual use. In 2020: 8

• About one in three high school students (36.8%) who vaped also used other tobacco products.
• One in two middle school students (49.0%) who vaped also used other tobacco products.

E-cigarettes can also be used to deliver other substances, including cannabis. In 2016, nearly one in three (30.6%) of U.S. middle and high school students who had ever used an e-cigarette reported using marijuana in the device. 9

• Park-Lee E, Ren C, Cooper M, Cornelius M, Jamal A, Cullen KA. Tobacco product use among middle and high school students—United States, 2022 . MMWR Morb Mortal Wkly Rep. 2022;71:1429–1435.
• U.S. Department of Health and Human Services. E-cigarette Use Among Youth and Young Adults: A Report of the Surgeon General . Centers for Disease Control and Prevention; 2016. Accessed Feb 14, 2024.
• Apelberg BJ, Corey CG, Hoffman AC, et al. Symptoms of tobacco dependence among middle and high school tobacco users: results from the 2012 National Youth Tobacco Survey . Am J Prev Med. 2014;47(Suppl 1):S4–14.
• Gentzke AS, Wang TW, Cornelius M, et al. Tobacco product use and associated factors among middle and high school students—National Youth Tobacco Survey, United States, 2021 . MMWR Surveill Summ. 2022;71(No. SS-5):1–29.
• Zhang L, Gentzke A, Trivers KF, VanFrank B. Tobacco cessation behaviors among U.S. middle and high school students, 2020 . J Adolesc Health. 2022;70(1):147–154.
• Birdsey J, Cornelius M, Jamal A, et al. Tobacco product use among U.S. middle and high school students—National Youth Tobacco Survey, 2023 . MMWR Morb Mortal Wkly Rep. 2023;72:1173–1182.
• Wang TW, Gentzke AS, Creamer MR, et al. Tobacco product use and associated factors among middle and high school students—United States, 2019 . MMWR Surveill Summ. 2019;68(No. SS-12):1–22.
• Wang TW, Gentzke AS, Neff LJ, et al. Characteristics of e-cigarette use behaviors among US youth, 2020 . JAMA Netw Open. 2021;4(6):e2111336.
• Trivers KF, Phillips E, Gentzke AS, Tynan MA, Neff LJ. Prevalence of cannabis use in electronic cigarettes among U.S. youth . JAMA Pediatr. 2018;172(11):1097–1099.

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Foothill center offers vital grounds for cattle studies

These cattle at the University of California Sierra Foothill Research Extension Center in Browns Valley are part of a study on administering an ionophore feed additive, monensin, to promote weight gain. The Yuba County property facilitates cattle ranching research, hosting livestock and researchers for as many as 20 study projects at any given time during the year.

Photo/John Watson

By John Watson 

During the summers of 2021 and 2022, as cattle roamed pastures and shaded woods of the University of California Sierra Foothill Research and Extension Center in Browns Valley, a UC Davis research team closely monitored potential ties between each animal’s personality and its grazing habits.

The project, resulting in a peer-reviewed paper published this past February, was just one of hundreds of studies conducted at the center in Yuba County during the past six decades.

Stretching across more than 5,700 acres of river, grassland, oak woodland and riparian habitat, the center supports research on beef cattle production, nutrition and health, rangeland water quality management, oak woodland restoration, native plant conservation and invasive plant management, among other related topics.

A hefty percentage of the studies the center supports aims to make cattle ranching more productive and profitable.

The cattle personality study, for instance, looked at benefits of matching herds to landscape. It analyzed whether selecting or subdividing herds by personality type could serve as an alternative to placing expensive incentives in undergrazed—and often higher-elevation—parts of the pasture, trying to lure herds to those spots.

The study hypothesis: There might be little need for water, mineral supplements, fencing or other incentive methods in undergrazed pastureland if the herd comprises very active, hill-climbing animals to begin with. In other words, could consideration of personalities optimize cattle distribution on rangeland?

The study, a part of UC Davis graduate Maggie Creamer’s doctoral dissertation, investigated the consistency of grazing patterns across two summers among 50 2- to 8-year-old pregnant Angus and Hereford beef cows, all fitted with GPS collars. Over two summers, they roamed 625 acres of grasslands and treed areas, elevation of which ranged from 600 to 2,020 feet.

The results show animals that were generally calmer—for example, when being handled or going through a chute—tended to graze more widely than animals that were skittish and nervous. Those grazing behaviors remained largely unchanged when a water site incentive was introduced at higher elevation in year two.

“Before opting for expensive interventions to get cattle to graze a different way, ranchers might want to explore this topic,” said Creamer, who now works as a postdoctoral scholar in North Carolina.

Kristina Horback, an associate professor at the UC Davis Department of Animal Science, joined Creamer in presenting their findings in a study published in February in the journal Applied Animal Behaviour Science. She explained that fertile areas for further study include the role that genetics and breeds might play in determining personality traits, along with investigations involving different landscapes and analyses of individual behavior when the animal is handled.

Formed in the 1960s as one of nine such units administered by UC Agriculture and Natural Resources, the Sierra research center provides a setting that, at any given time, supports some 20 research projects.

Current studies include an investigation into the most effective ways to administer daily doses of the ionophore monensin, with the goal of increased cattle weight gain. Monensin encourages bacteria shift of rumen microbes that increase the production of propionic acid, which in turn enhances weight gain through more efficient conversion of feed to volatile fatty acids. A dose of 50 milligrams a day has proven to improve weight gain, and some studies have shown more gains with rates up to 200 milligrams a day.

The problem is underconsumption: Because monensin isn’t very palatable, it’s difficult to entice cattle to consume the higher doses. The study used 140 cattle to analyze multiple consumption rates of two alternative dosage methods: free-choice loose mineral and salt blocks. Results will be tabulated this summer.

“We have to bring the cattle in every 45 days and change pastures seasonally, which is more than we could ask of local cooperators,” said co-principal investigator Josh Davy, in a nod to the center’s ability to navigate constraints faced by commercial growers or landowners.

“The center’s staff members always create a positive working environment for collaboration and good solutions,” he added.

Another current project deals with a type of seaweed, Asparagopsis taxiformis, known to reduce methane production in cattle by up to 90%. With some similarities to the monensin project, the primary objective of this study is to validate a practical method through which a seaweed supplement can be fed to range livestock.

Ongoing studies also include a project that explores augmenting cattle immune systems to combat pinkeye, the most common eye disease of cattle in the United States.

For the project, cattle consume iodine in the form of ethylenediamine dihydroiodide, or EDDI, which supplements a naturally occurring immune mechanism, resulting in a potent antibacterial and antiviral compound. The goal is to determine whether feeding iodine via a mineral mix using EDDI can achieve iodine target concentrations in tears to inactivate the bacterium that causes pinkeye.

The Sierra Foothill Research and Extension Center includes 160 acres of irrigated pasture, 270 acres of long-term ungrazed oak woodland and 200 acres of control-burned rangeland annually. Its rolling to steeper-sloping terrain ranges from 220 to 2,020 feet above sea level, with access to the Yuba River and six small permanent streams. Research requests for land, labor and facilities are screened by a research advisory committee.

“SFREC is one of the jewels of the UC system,” said Anthony O’Geen, a UC Cooperative Extension soil resource specialist. “It’s a beautiful place with managed and pristine ecosystems, resourceful staff to support research and outreach and wonderful soils. It’s the perfect natural laboratory.”

(John Watson is a reporter in Nevada County. He may be contacted at [email protected].)

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