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  • Published: 01 February 2017

Examining study habits in undergraduate STEM courses from a situative perspective

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

International Journal of STEM Education volume  4 , Article number:  1 ( 2017 ) Cite this article

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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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Hora, M.T., Oleson, A.K. Examining study habits in undergraduate STEM courses from a situative perspective. IJ STEM Ed 4 , 1 (2017). https://doi.org/10.1186/s40594-017-0055-6

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The relationship between students’ study habits, happiness and depression

Susan bahrami.

* Department of Educational Administration, Health Management & Economic Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Saeed Rajaeepour

** Assistant Professor, Department of Educational Administration, Faculty of Education and Psychology, The University of Isfahan, Isfahan, Iran.

Hasan Ashrafi Rizi

*** Assistant Professor, Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Monereh Zahmatkesh

**** Department of Medical Library and Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran.

Zahra Nematolahi

Background:.

One of the important requirements for cultural, social and even economic development is having a book-loving nation. In order to achieve this, there is a need for purposeful and continuous programming. The purpose of this research was to determine the relationship between students’ study habits, happiness and depression in Isfahan University of Medical Science.

This research was a kind of descriptive and correlation survey. Statistical population included all MSc and PhD students in the second semester of the Isfahan University of Medical Science (263 students). In this research, stratified and random sampling was used in which a sample of 100 students was selected. Data collection instruments were Beck Depression Inventory (BDI), Oxford Happiness Inventory and a researcher-made questionnaire to determine the amount of students’ study. Validity of this questionnaires was determined by structure and content related validity and its reliability was calculated by Cronbach's alpha coefficient for the first (r = 0.94), second (r = 0.91) and third (r = 0.85) questionnaire. Analysis of research findings was done through descriptive and inferential statistics.

Findings showed that 68.8 percent of students study less than 5 hours and only 2.5 percent of students study more than 10 hours. 65 percent of students had high amount of happiness and 35 percent had medium amount of happiness. In 60 percent of students there was no symptom of depression and 7.5 had depression symptoms. Also, there was no significant relationship between happiness and studying but there was a significant and negative relationship between studying and depression and happiness and depression.

CONCLUSIONS:

The amount of study and tendency for reading are among the most important indices of human growth in terms of potential abilities for achieving a perfect human life and to prevent one-dimensional thinking. Thus, finding ways to encourage students to study is considered essential to achieve a healthy and developed society.

Students in each society are human sources that make future of their country and existence of mental problems among students has many serious effects such as decreasing the academic achievement and increasing, drop out and inefficiency among students. Studying based on some specific criteria can lead to growth and development of personality dimensions in humans which in turn can cause mental health and treatment of many disorders including depression which is a very important health issue in the world. 1

Happiness is also a very important factor in human life. It is due to happiness that a person has always a good feeling about himself and others, rejects despair felling, accepts his weaknesses, never forgets learning, is always honest with himself and others, lives in the present time and is stable against problems. 2

Mental health of people in a society is an important and necessary factor for achieving a better life, social welfare and logical compatibility with life events. Therefore, some factors should be recognized in order to prevent occurrence of disorders. One unique property of humans is thinking ability which creates and accelerates human action. 3 Mental health is considered as one of the important factors for a healthy society. Having this divine gift (mental health) is very important for having a comfortable life and achieving great goals. Not having this divine grace (mental health) leads to different problems for people. 4

Jokar (2001) in an investigation of the lack of students’ motivation in secondary schools and students of public universities in Shiraz reported some preventive factors for studying such as high amount of homework at school and lack of proper facilities for studying. 5 Fazel and Shabani (2002) in an investigation of the amount of studying in 360 students of secondary school showed that the amount of studying in male students is 1 hour and 25 minutes and in female students is 2 hours and 32 minutes. 6

Jafari (2004) in an investigation of the amount of happiness and the elements affecting that among students of Isfahan University of Medical Sciences showed that 78 percent of students had high amount of happiness and the rest of students had medium amount of happiness. Cognitive and behavioral factors as well as self-disclosure were considered as the most influential factors in the clarification of students’ happiness. 7

Abedini et al. (2007) in investigation of the depression prevalence in 190 nursing and medical students of Hormozgan Medical University showed that depression prevalence in nursing students was 60 percent and in medical students was 49.5 percent. 8 Khosh Konesh et al. (2008) in investigation of happiness and mental health relationship in students found a significant and negative relationship between happiness feeling and mental problems, physical disorders, anxiety and depression of male and female students. 9

Dadkhah et al. (2006) in a research about investigation of mental health condition of students in Ardebil University of Medical Science showed that there was no significant difference between male and female students in terms of mental disorders. 10 Lightsey (1994) in a research about positive thinking effects on happiness and depression found that positive thoughts cause happiness but negative events and thoughts lead to depression. However, comparatively it can be said that the negative thoughts have less relationship with depression than positive thoughts and happiness. 11 Bitsika (2010) in investigation of 200 students of a private university in Austria showed that symptoms of fatigue, anxiety and insomnia were more prevalent among female students than in male students. 12

The present research attempted to determine study habits, happiness, and depression among Isfahan University of Medical Sciences students and also it aimed at determining the relationship between these three factors.

This research was a descriptive and correlation survey. Statistical population in this research included all MSc and PhD students in the second semester (2008 – 2009) of Isfahan University of Medical Sciences (263 students). A stratified and random sampling was used and a sample of 100 students was selected. Data collection instrument included 3 questionnaires as follows:

  • A researcher-made questionnaire including 17 items based on Likert five degree scale to determine the students’ study habits.
  • Beck Depression Inventory (BDI) which was published in 1987 13 : Respondents of this test should have reading ability at least in fifth or sixth grade of secondary schools in order to understand its questions They must answer each question based on a 4 grade scale of Likert from zero to three. Minimum grade in this test is zero and maximum grade is 63. By calculating the sum of student grades in each question, the student grade can be obtained directly. Grades can be interpreted as 0 - 13: none or the least amount of depression, 14- 19: mild depression, and 20 - 28: medium depression which is used for general level of depression. 14
  • Oxford Happiness Inventory (1987) which includes 29 multiple choice questions which are graded from zero to three, respectively. 15 Each examinee's grade stands between zeros to 87.

To determine the validity of the questionnaires, the content related validity was utilized and their validity was confirmed by a number of experienced instructors in Isfahan University of Medical Sciences, and some psychology experts of Isfahan University. Also, for estimating their reliability, Cronbach's alpha coefficient was used. In a pilot study, 30 questionnaires were distributed among students of the university and then, recollected and Cronbach's alpha coefficient calculated in the first (r = 0.94)), second (r = 0.91) and third (r = 0.85) ones. Analysis of research data was accomplished through descriptive statistics such as frequency percent and through inferential statistics such independent t test and one way variance analysis test (ANOVA).

Findings showed that 20 percent of respondents were male and 77.5 percent of respondents were female. Also, 8.8 percent of respondents were at PhD level and 81.3 percent were at MSc level. Furthermore, 61.3 percent of respondents were single and 35 percent were married.

Results of Table 1 showed that in 68.8 percent of students, the amount of studying was less than 5 hours and 2.5 percent of students studied between 10 – 15 hours and more than 15 hours. Also, 65 percent of students had high amount of happiness and 35 percent had medium happiness. In 60 percent of students, there was no symptom of depression and 7.5 had chronic depression symptoms.

Frequency and percentage of the examinee’s amount of study, happiness and depression of students

An external file that holds a picture, illustration, etc.
Object name is IJNMR-16-217-g001.jpg

Results of Table 2 showed that correlation coefficient between the grades of study and happiness in p ≤ 0.05 level was not significant. However correlation coefficient between grades of study and happiness in p ≤ 0.05 level was significant and negative. This means that the amount of depression was decreased by increasing the amount of study and on the other hand, the amount of depression decreases when happiness increases. Also, the comparison of the results with regard to the average grade of study, happiness and depression in terms of age and sex showed that the observed amount of t was not significant in p ≤ 0.05 level and it means that there was no significant difference between the amount of study, happiness and depression among students in terms of age and sex.

Correlation coefficient & the level of significant relationship between the amounts of study and students’ happiness and depression

An external file that holds a picture, illustration, etc.
Object name is IJNMR-16-217-g002.jpg

Amount of study and tendency for reading are among most important indices of human growth in terms of potential abilities for achieving the goal of a perfect human and eliminating one dimensional thinking. 16

Results showed that most students study less than 5 hours; high amount of homeworks, lack of motive for studying and expensive cost of books can be considered as factors of students’ lack of interest to study. This finding was in accordance with Joukar's research (2001) in Shiraz public schools and universities. 5

Findings showed that most students had high and medium amount of happiness and it can be justified in a way that most students were with high amount of motive and happiness. This finding in investigation of happiness amount and its effective factors was in accordance with Jaffari's (2004) research in students of Isfahan Medical Science University. 7

Also, findings showed no depression in most students and 7.5 percent of them had chronic depression. Also, to clarify these findings, it can be said that more than half of students had no depression and had a relative amount of mental health. This finding was in accordance with Abedini et al. studies (2007) but it was not in accordance with Hormozgan Medical students and nursing students and depression prevalence among them. 8

Also, the results showed that relation between the scores of study and happiness were not significant but the relation between study and depression was negative and significant. This means that their amount of depression decreased with increasing amount of studying and on the other hand, there was a significant and negative relation between happiness and depression. In other words, with increasing happiness in students, their amount of depression decreased. This finding was in accordance with the results of the research of Khosh Konesh et al. (2008) who investigated the relationship of happiness and mental health 9 and also, with Lightsey findings (1994) in a research titled “Positive thoughts in the happiness and depression of an individual”. 11

Also, results showed no significant difference between the amount of study, happiness and depression among students in terms of sex and age. This means that there was no difference in the amount of study, happiness and depression among male and female students in terms of age. This finding was not in accordance with findings of Khosh Konesh et al. findings (2008) and also Bitsika studies (2010) in Austria. 9 , 12 .

Conclusions

Considering the low amount of study in our country and according to the obtained results, it can be said that studying is influential in overcoming the individual's depression. Therefore, it has been suggested to perform some researches to find the fields of students’ interests for studying and comparison of different methods to encourage students to study so that they can enjoy a better mental health.

The authors declare no conflict of interest in this study.

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The effect of social media on the development of students’ affective variables.

\r\nMiao Chen,*

  • 1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China
  • 2 School of Marxism, Hohai University, Nanjing, Jiangsu, China
  • 3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd.

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

Publisher’s note

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Keywords : affective variables, education, emotions, social media, post-pandemic, emotional needs

Citation: Chen M and Xiao X (2022) The effect of social media on the development of students’ affective variables. Front. Psychol. 13:1010766. doi: 10.3389/fpsyg.2022.1010766

Received: 03 August 2022; Accepted: 25 August 2022; Published: 15 September 2022.

Reviewed by:

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

*Correspondence: Miao Chen, [email protected] ; Xin Xiao, [email protected]

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

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Americans’ social media use, youtube and facebook are by far the most used online platforms among u.s. adults; tiktok’s user base has grown since 2021.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A .

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

Social media platforms faced a range of controversies in recent years, including concerns over misinformation and data privacy . Even so, U.S. adults use a wide range of sites and apps, especially YouTube and Facebook. And TikTok – which some Congress members previously called to ban – saw growth in its user base.

These findings come from a Pew Research Center survey of 5,733 U.S. adults conducted May 19-Sept. 5, 2023.

Which social media sites do Americans use most?

A horizontal bar chart showing that most U.S. adults use YouTube and Facebook; about half use Instagram.

YouTube by and large is the most widely used online platform measured in our survey. Roughly eight-in-ten U.S. adults (83%) report ever using the video-based platform.

While a somewhat lower share reports using it, Facebook is also a dominant player in the online landscape. Most Americans (68%) report using the social media platform.

Additionally, roughly half of U.S. adults (47%) say they use Instagram .

The other sites and apps asked about are not as widely used , but a fair portion of Americans still use them:

  • 27% to 35% of U.S. adults use Pinterest, TikTok, LinkedIn, WhatsApp and Snapchat.
  • About one-in-five say they use Twitter (recently renamed “X”) and Reddit.  

This year is the first time we asked about BeReal, a photo-based platform launched in 2020. Just 3% of U.S. adults report using it.

Recent Center findings show that YouTube also dominates the social media landscape among U.S. teens .

TikTok sees growth since 2021

One platform – TikTok – stands out for growth of its user base. A third of U.S. adults (33%) say they use the video-based platform, up 12 percentage points from 2021 (21%).

A line chart showing that a third of U.S. adults say they use TikTok, up from 21% in 2021.

The other sites asked about had more modest or no growth over the past couple of years. For instance, while YouTube and Facebook dominate the social media landscape, the shares of adults who use these platforms has remained stable since 2021.

The Center has been tracking use of online platforms for many years. Recently, we shifted from gathering responses via telephone to the web and mail. Mode changes can affect study results in a number of ways, therefore we have to take a cautious approach when examining how things have – or have not – changed since our last study on these topics in 2021. For more details on this shift, please read our Q&A .

Stark age differences in who uses each app or site

Adults under 30 are far more likely than their older counterparts to use many of the online platforms. These findings are consistent with previous Center data .

A dot plot showing that the youngest U.S. adults are far more likely to use Instagram, Snapchat and TikTok; age differences are less pronounced for Facebook.

Age gaps are especially large for Instagram, Snapchat and TikTok – platforms that are used by majorities of adults under 30. For example:

  • 78% of 18- to 29-year-olds say they use Instagram, far higher than the share among those 65 and older (15%).
  • 65% of U.S. adults under 30 report using Snapchat, compared with just 4% of the oldest age cohort.
  • 62% of 18- to 29-year-olds say they use TikTok, much higher than the share among adults ages 65 years and older (10%).
  • Americans ages 30 to 49 and 50 to 64 fall somewhere in between for all three platforms.

YouTube and Facebook are the only two platforms that majorities of all age groups use. That said, there is still a large age gap between the youngest and oldest adults when it comes to use of YouTube. The age gap for Facebook, though, is much smaller.

Americans ages 30 to 49 stand out for using three of the platforms – LinkedIn, WhatsApp and Facebook – at higher rates. For instance, 40% of this age group uses LinkedIn, higher than the roughly three-in-ten among those ages 18 to 29 and 50 to 64. And just 12% of those 65 and older say the same. 

Overall, a large majority of the youngest adults use multiple sites and apps. About three-quarters of adults under 30 (74%) use at least five of the platforms asked about. This is far higher than the shares of those ages 30 to 49 (53%), 50 to 64 (30%), and ages 65 and older (8%) who say the same.  

Refer to our social media fact sheet for more detailed data by age for each site and app.

Other demographic differences in use of online platforms

A number of demographic differences emerge in who uses each platform. Some of these include the following:

  • Race and ethnicity: Roughly six-in-ten Hispanic (58%) and Asian (57%) adults report using Instagram, somewhat higher than the shares among Black (46%) and White (43%) adults. 1
  • Gender: Women are more likely than their male counterparts to say they use the platform.
  • Education: Those with some college education and those with a college degree report using it at somewhat higher rates than those who have a high school degree or less education.
  • Race and ethnicity: Hispanic adults are particularly likely to use TikTok, with 49% saying they use it, higher than Black adults (39%). Even smaller shares of Asian (29%) and White (28%) adults say the same.
  • Gender: Women use the platform at higher rates than men (40% vs. 25%).
  • Education: Americans with higher levels of formal education are especially likely to use LinkedIn. For instance, 53% of Americans with at least a bachelor’s degree report using the platform, far higher than among those who have some college education (28%) and those who have a high school degree or less education (10%). This is the largest educational difference measured across any of the platforms asked about.

Twitter (renamed “X”)

  • Household income: Adults with higher household incomes use Twitter at somewhat higher rates. For instance, 29% of U.S. adults who have an annual household income of at least $100,000 say they use the platform. This compares with one-in-five among those with annual household incomes of $70,000 to $99,999, and around one-in-five among those with annual incomes of less than $30,000 and those between $30,000 and $69,999.
  • Gender: Women are far more likely to use Pinterest than men (50% vs. 19%).
  • Race and ethnicity: 54% of Hispanic adults and 51% of Asian adults report using WhatsApp. This compares with 31% of Black adults and even smaller shares of those who are White (20%).

A heat map showing how use of online platforms – such as Facebook, Instagram or TikTok – differs among some U.S. demographic groups.

  • Estimates for Asian adults are representative of English speakers only. ↩

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Table of contents, q&a: how – and why – we’re changing the way we study tech adoption, americans’ use of mobile technology and home broadband, social media fact sheet, internet/broadband fact sheet, mobile fact sheet, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Is the University of Michigan Is Selling Student Data to Train AI?

The University of Michigan has allegedly sold 85 hours of audio recordings from various academic settings including lectures, interviews, office hours, study groups, and student presentations to third parties for the purposes of training artificial intelligence . The school has also allegedly sold a dataset of 829 academic papers from students to help fine tune large language models (LLMs) as well.

It is unclear whether those included in the data consented to having their audio and texts used in such a manner. However, a sample dataset downloaded by The Daily Beast included a recording of a lecture from 1999 making it highly unlikely that they knew their data would be used to train future generative AI models.

AI engineer Susan Zhang took to X to post a screenshot showing what looks to be an advertisement from Catalyst Research Alliance, a firm selling the UM data, that she recently received on LinkedIn. The sender wrote that they were “reaching out because, based on your profile, you may be working with” LLMs.

“I wanted to let you know that the University of Michigan is licensing academic speech data and student papers that could be very useful for training or tuning LLMs,” the user wrote.

“So I guess this is a thing now,” Zhang said. “Universities running ads to resell students data for training LLMs.”

so i guess this is a thing now universities running ads to resell students' data for training llms 💰💰💰 pic.twitter.com/8SR0gP6R10 — Susan Zhang (@suchenzang) February 15, 2024

In a statement to The Daily Beast, UM spokesperson Colleen Mastony said that the ad was "sent out by a new third party vendor that shared inaccurate information and has since been asked to halt their work."

"Student data was not and has never been for sale by the University of Michigan," Mastony said. She added that the papers and speech recordings were "voluntarily contributed by student volunteers" who participated in two research studies "under signed consent." One study occurred between 1997 and 2000, while the other occurred between 2006 and 2007.

However, the nature of UM's relationship with Catalyst Research Alliance as a "third party vendor" was still unclear. Whether or not the students knew that their data would later be sold to help train AI was also not clear. Catalyst Research Alliance did not respond when reached for comment.

According to the firm's website, the cost of licensing the datasets varies depending on whether or not customers want to purchase just the audio recordings or the papers as well. However, the price goes as high as $25,000 for both datasets.

“The University of Michigan has recorded 65 speech events from a wide range of academic settings, including lectures, discussion sections, interviews, office hours, study groups, seminars and student presentations,” Catalyst Research Alliance said on its website . “Speakers represent broad demographics, including male and female and native and non-native English speakers from a wide variety of academic disciplines.”

The sample dataset included an audio lecture titled “Graduate Cellular Biotechnology Lecture” dated Feb. 1, 1999. In it, the unidentified lecturer speaks for roughly an hour and a half. The dataset also included a .txt file of a paper titled “The Democratic Inadequacies of the European Union.”

Meet Laika, the Chatbot That Acts Like a Social Media Obsessed Teen

If true, the licensing deal is just another example of how personal data is being packaged and sold to help fuel emerging technologies such as generative AI. Even students whose work is completely unrelated to AI and LLMs can find their voice and writings being used in order to help train them.

“The whole thing feels deeply unethical,” Charles Logan, a learning sciences PhD candidate at Northwestern University, told The Daily Beast. Logan saw Zhang’s post on X and also commented on the situation, decrying it as the “logical progression of data capitalism.”

“When students are in a class or attending office hours there’s a trust implicit in that relationship,” Logan said. “They’re there to learn.”

He added that even if they are consenting to be a part of these datasets “there are still ways that they’re leaky.” “Private companies are monetizing student intellectual property and conversations that, if you’re in office hours or study groups, are deeply personal.”

That said, there is some room for doubt. Mastony said that the papers and recordings have "long been available for free to academics" and have "been used as a tool to improve writing and articulation in education. She added that "none of the papers or recordings included identifying information, such as names or other personal data."

“My first reaction is one of skepticism,” Vincent Conitzer, an AI ethics researcher at Carnegie Mellon University, told The Daily Beast. “Also, even taking this message mostly at face value, I suppose it may just all be based on recordings and papers that are anyway in the public domain.”

He added that “it seems odd to me to imagine the university at the highest levels standing behind something like what this message is suggesting.”

Read more at The Daily Beast.

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  2. (PDF) THE LEARNERS' STUDY HABITS AND ITS RELATION ON THEIR ACADEMIC

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  1. (PDF) THE LEARNERS' STUDY HABITS AND ITS RELATION ON ...

    Study habits are at the core of a learner's academic success. It is an action like reading, taking notes, conducting study groups that students perform frequently, and regularly accomplishing the ...

  2. Improving Students' Study Habits and Course Performance With a

    Many first-year college students are unprepared for the academic rigors of college, with as few as 27% of American high school students demonstrating proficiency in English, reading, mathematics, and science on the ACT college entrance exam ().College students may rely on study habits they have developed throughout their elementary and secondary education which served them sufficiently in the ...

  3. Study Habits and Procrastination: The Role of Academic Self-Efficacy

    The conceptual model, shown in. Figure 1. , assumes that the influence of Study Skill Habits on academic procrastination is mediated by Study Self-Efficacy. The SSH construct is specified as a formative latent construct, whereas SSE and procrastination are specified as reflective latent constructs.

  4. Relationship between study habits and academic achievement in students

    Introduction. Academic performance of students is one of the main indicators used to evaluate the quality of education in universities. 1, 2 Academic performance is a complex process that is influenced by several factors, such as study habits. 2 Study habit is different individual behavior in relation to studying 3 and is a combination of study method and skill. 4 In other words, study habits ...

  5. PDF Study of the relationship between study habits and academic ...

    Full Length Research Paper Study of the relationship between study habits and academic achievement of students: A case of Spicer Higher Secondary School, India Evans Atsiaya siahi1* and Julius K. Maiyo2 1University of Pune, India. 2Kibabii University College, India. Received 28 April, 2015, Accepted 10 August, 2015

  6. PDF Study Habits and Academic Performance among Students: A Systematic Review

    Good study habits are good asset to learners because study habits help students to attain mastery in areas of specialization and ensuing excellent performance, while the opposite becomes constraint to learning and achievement leading to failure". Palsane and Sharma (1989) has defined study habits in an operational manner as, " Study habits ...

  7. Study Habits and Academic Performance among Students: A Systematic

    Study habits are the well-planned intended methods of study, the chain of approaches in the process of memorising, systematizing, regulating, retaining novel facts and ideas related to the learning materials, which has gained the shape of consistent endeavours on the part of students, towards comprehending academic subjects and qualifying examinations. The constant practices a person utilizes ...

  8. PDF Influence of Study Habits on Academic Performance of International ...

    3.1.1 Study Habits Study habits are mainly external factors that facilitate the study process such as sound study routines that include how often a student engage in studying sessions, review the material, self-evaluate, rehears explaining the material, and studying in a conducive environment (Credé, 2008). 3.1.2 Study Skills

  9. Examining study habits in undergraduate STEM courses ...

    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 ...

  10. PDF Study habit and its impact on secondary school students ...

    Full Length Research Paper Study habit and its impact on secondary school students' academic performance in biology in the Federal Capital Territory, Abuja ... of study habits clearly puts students at a disadvantage, and is one of the main reasons students need remedial classes, fall behind in coursework and drop out of school. ...

  11. The impact of COVID-19 on student experiences and expectations

    Our findings on academic outcomes indicate that COVID-19 has led to a large number of students delaying graduation (13%), withdrawing from classes (11%), and intending to change majors (12%). Moreover, approximately 50% of our sample separately reported a decrease in study hours and in their academic performance.

  12. Student health behavior and academic performance

    This article reports on 28 health behaviors and habits and their impact on the academic performance of undergraduate students at a public university in the southern United States. The behaviors selected can be categorized in five categories: (1) sleeping habits, (2) working, (3) physical activity, (4) eating habits, and (5) alcohol, tobacco ...

  13. The Impact of Study Habits on The Academic Performance of Students

    The result revealed no significant relationship between study habits and academic performance. Also, the results showed that the study habits of the students are at a relatively average level. Additionally, enhancing students' study habits are relevant, especially in note-taking, reading ability, and health, thus improving their academic ...

  14. PDF Study Habits of Students: Keys to Good Academic Performance in Public

    Neal (2007) conclude that study habits are approaches applied to learning. Understandably, good study habits assist students to apply their thought processes to identify relevant bodies of knowledge and evaluation of same. Conversely, poor study habits impede students' capacity to engage in constructive intellectual exercise.

  15. Study Habits and the Perceived Factors that Distract the ...

    There are students who garnered exceptional ratings while some failed in their class performances. Students experience disturbances that are distractions in their studies. Utilizing the descriptive survey design, this study investigated the extent of respondents' study habits and the factors that distract their concentration.

  16. Study Habits and Attitudes of Freshmen Students: Implications for

    The substantive aim of this study was to investigate the favorable and unfavorable study habits and study attitudes of the freshmen students and determine its implications in formulating academic intervention programs. The Survey of Study Habits and Attitudes (SSHA) instrument developed by Brown and Holtzman (1969) was used to assess the study habits and attitudes of the students. Results show ...

  17. The relationship between students' study habits, happiness and

    Results of Table 1 showed that in 68.8 percent of students, the amount of studying was less than 5 hours and 2.5 percent of students studied between 10 - 15 hours and more than 15 hours. Also, 65 percent of students had high amount of happiness and 35 percent had medium happiness. In 60 percent of students, there was no symptom of depression ...

  18. (Pdf) Learning Styles, Study Habits, and Academic Performance of

    Related Papers. Journal of Education and Practice. The Effects of Remedial Mathematics in Learning College Algebra ... Table 2 presents the levels of study habits of the students in learning their lessons particularly in College Algebra Study periods 3 hours 2 hours 1 hour Mean Description High Average Low Hours spent in studying daily lessons ...

  19. PDF Analyzing the Effect of Learning Styles and Study Habits of ...

    purpose of this study was to explore relationship between students' learning styles the and study habits in accordance with their learning styles in an online distance learning environment. Sub-problems were related to tween Kolbthe connection be's learning styles, study habits of distance learners, and their learning performances. This would

  20. Frontiers

    Frontiers | Peer Reviewed Articles - Open Access Journals

  21. How Americans Use Social Media

    To better understand Americans' social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail ...

  22. Is the University of Michigan Is Selling Student Data to Train AI?

    The University of Michigan has allegedly sold 85 hours of audio recordings from various academic settings including lectures, interviews, office hours, study groups, and student presentations to third parties for the purposes of training artificial intelligence.The school has also allegedly sold a dataset of 829 academic papers from students to help fine tune large language models (LLMs) as well.

  23. Board Exams 2024: 5 Key Stress Factors Students Must Avoid

    The CBSE Board Exam 2024 commenced on February 15. While Class 12 students are familiar with the exam format, it's the first encounter with board exams for Class 10 students. However, regardless ...

  24. PDF Academic Achievements and Study Habits of College Students of District

    A habit is just a behavior that is repeated until it is automatic. A habit is something that is done on a scheduled, regular and planned basis that is not relegated to a second place or optional place in one`s life. It is simply done, no reservations, no excuses, no exceptions. Study habits are the ways that we study.

  25. KevinB88/DICE-SBU-Hacks-2024

    Facilitating a simple platform to auto-generate optimal study hours around a student's academic schedule. Supporting pupils of all ages, and encouraging healthier study-habits. - GitHub - KevinB88/DICE-SBU-Hacks-2024: Facilitating a simple platform to auto-generate optimal study hours around a student's academic schedule. Supporting pupils of all ages, and encouraging healthier study-habits.