research application study habits and memory part 1 checkpoint

Princeton Correspondents on Undergraduate Research

Studying Studying: Memory Tips from Psychology

research application study habits and memory part 1 checkpoint

With finals season approaching, a sense of dread sets in every time I take notes in lecture—how am I supposed to remember all of this information at once? Often the prospect of beginning the study process is so overwhelming that organizing all the information seems almost impossible.

As I mentioned in one of my previous posts , I’ve realized that a lot of my psychology research and coursework have provided many useful tips to make studying as effective and efficient as possible. I have compiled a few here to help you get started and hopefully feel more comfortable diving into studying when reading period comes around.

1. Know where to devote your time – As my final research project for my cognitive psychology class revealed, memory of complex stimuli—things that contain many small details or are particularly abstract—is best following a few long exposures, while memory of simple stimuli—those that contain small amounts of information and are fairly concrete—is best following a lot of short exposures. So, what does this mean for studying? For concepts that you feel like you know well, it would be most beneficial to study those concepts multiple times for short periods of time. But, for concepts that are more complex and challenging for you, you should try to study for longer, but fewer periods of time.

2. Break up information into manageable sections – This is a strategy known as “chunking” in psychology. Research has shown that the human brain can hold just about 7 items in short-term memory at a time. But before you freak out—this does not mean that you are doomed and will only be able to remember 7 important dates for your history final. This number 7 is not as limiting as it may seem. Instead, imagine 7 slots available for you to store information, but each of these slots doesn’t have to be filled with only one, small fact. It all depends on how you group information; for example, you can fill these 7 slots with 7 large concepts that include a lot more detailed information within them. Try to find a way to break up all the information in a class into no more than 7 meaningful sections and use those sections to ground your studying.

3. Be deliberate about where you study – When you remember something, you are retrieving it from your memory. This retrieval can be aided by certain cues that are associated with the initial formation or encoding of the memory. One of these cues is context, i.e. where you first encoded the memory. So, if I am taking my psychology exam and I can’t remember the answer to a question, if I can remember where I studied, thinking about that place may help me remember the memory I am looking for. I use this strategy especially when I am studying for two classes that are somewhat similar—for example two psychology classes. Sometimes I will get confused about what material goes with each class, but if I separate my studying for each class by location, those different locations help me differentiate the material itself and remember it better.

I am lucky enough to be studying a discipline that embeds these enlightening nuggets of information into the coursework. I hope that these tips are helpful to you and encourage you to be mindful of what and how you are learning throughout the semester in order to develop the most effective strategies for each of your classes.

— Ellie Breitfeld, Natural Sciences Correspondent

The Innovative Instructor

Pedagogy – best practices – technology.

An Evidence-based Approach to Effective Studying

Dr. Culhane is Professor and Chair of the Department of Pharmaceutical Sciences at Notre Dame of Maryland University School of Pharmacy.

If you are like me, much of your time is spent ensuring that the classroom learning experience you provide for your students is stimulating, interactive and impactful. But how invested are we in ensuring that what students do outside of class is productive? Based on my anecdotal experience and several studies 1,2,3 looking at study strategies employed by students, the answer to this question is not nearly enough! Much like professional athletes or musicians, our students are asked to perform at a high level, mastering advanced, information dense subjects; yet unlike these specialists who have spent years honing the skills of their craft, very few students have had any formal training in the basic skills necessary to learn successfully. It should be no surprise to us that when left to their own devices, our students tend to mismanage their time, fall victim to distractions and gravitate towards low impact or inefficient learning strategies. Even if students are familiar with high impact strategies and how to use them, it is easy for them to default back to bad habits, especially when they are overloaded with work and pressed for time.

Several years ago, I began to seriously think about and research this issue in hopes of developing an evidence-based process that would be easy for students to learn and implement. Out of this work I developed a strategy focused on the development of metacognition – thinking about how one learns. I based it on extensively studied, high impact learning techniques to include: distributed learning, self-testing, interleaving and application practice. 4 I call this strategy the S.A.L.A.M.I. method. This method is named after a metaphor used by one of my graduate school professors. He argued that learning is like eating a salami. If you eat the salami one slice at a time, rather than trying to eat the whole salami in one setting, the salami is more likely to stay with you. Many readers will see that this analogy represents the effectiveness of distributed learning over the “binge and purge” method which many of our students gravitate towards.

S.A.L.A.M.I. is a “backronym” for S ystematic A pproach to L earning A nd M etacognitive I mprovement. The method is structured around typical, daily learning experiences that I refer to as the five S.A.L.A.M.I. steps:

Pre-class preparation
In-class engagement
Post-class review
Pre-exam preparation
Post-assessment review

When teaching the S.A.L.A.M.I. method, I explain how each of the five steps correspond to different “stages” or components of learning (see figure 1). Through mastery of skills associated with each of the five S.A.L.A.M.I. steps, students can more efficiently and effectively master a subject area.

Despite its simplicity, this model provides a starting point to help students understand that learning is a process that takes time, requires the use of different learning strategies and can benefit from the development of metacognitive awareness. Specific techniques designed to enhance metacognition and learning are employed during each of the five steps, helping students use their time effectively, maximize learning and achieve subject mastery. Describing all the tools and techniques recommended for each of the five steps would be beyond the scope of this post, but I would like to share two that I have found useful for students to evaluate the effectiveness of their learning and make data driven changes to their study strategies.

Let us return to our example of professional athletes and musicians: these individuals maintain high levels of performance by consistently monitoring and evaluating the efficacy of their practice as well as reviewing their performance after games or concerts. If we translate this example to an academic environment, the practice or rehearsal becomes student learning (in and out of class) and the game or concert acts as the assessment. We often evaluate students’ formative or summative “performances” with grades, written or verbal feedback. But what type of feedback do we give them to help improve the efficacy of their preparation for those “performances?” If we do give them feedback about how to improve their learning process, is it evidenced-based and directed at improving metacognition, or do we simply tell them they need to study harder or join a study group in order to improve their learning? I would contend that we could do more to help students evaluate their approach to learning outside of class and examination performance. This is where a pre-exam checklist and exam wrapper can be helpful.

The inspiration for the pre-exam checklist came from the pre-flight checklist a pilot friend of mine uses to ensure that he and his private aircraft are ready for flight. I decided to develop a similar tool for my students that would allow them to monitor and evaluate the effectiveness of their preparation for upcoming assessments. The form is based on a series of reflective questions that help students think about the effectiveness of their daily study habits. If used consistently over time and evaluated by a knowledgeable faculty or learning specialist, this tool can help students be more successful in making sustainable, data driven changes in their approach to learning.

Another tool that I use is called an exam wrapper. There are many examples of exam wrappers online, however, I developed my own wrapper based on the different stages or components of learning shown in figure 1. The S.A.L.A.M.I. wrapper is divided into five different sections. Three of the five sections focus on the following stages or components of learning: understanding and building context, consolidation, and application. The remaining two sections focus on exam skills and environmental factors that may impact performance. Under each of the five sections is a series of statements that describe possible reasons for missing an exam question. The student analyzes each missed question and matches one or more of the statements on the wrapper to each one. Based on the results of the analysis, the student can identify the component of learning, exam skill or environmental factors that they are struggling with and begin to take corrective action. Both the pre-exam checklist and exam wrapper can be used to help “diagnose” the learning issue that academically struggling students may be experiencing.

Two of the most common issues that I diagnose involve illusions of learning 5 . Students who suffer from the ‘illusion of knowledge’ often mistake their understanding of a topic for mastery. These students anticipate getting a high grade on an assessment but end up frustrated and confused when receiving a much lower grade than expected. Information from the S.A.L.A.M.I. wrapper can help them realize that although they may have understood the concept being taught, they could not effectively recall important facts and apply them. Students who suffer from the ‘illusion of productivity’ often spend extensive time preparing for an exam, however, the techniques they use are extremely passive. Commonly used passive study strategies include: highlighting, recopying and re-reading notes, or listening to audio/video recordings of lectures in their entirety. The pre-exam checklist can help students identify the learning strategies they are using and reflect on their effectiveness. When I encounter students favoring the use of passive learning strategies I use the analogy of trying to dig a six-foot deep hole with a spoon: “You will certainly work hard for hours moving dirt with a spoon, but you would be a lot more productive if you learned how to use a shovel.” The shovel in this case represents adopting strategies such as distributed practice, self-testing, interleaving and application practice.

Rather than relying on anecdotal advice from classmates or old habits that are no longer working, students should seek help early, consistently practice effective and efficient study strategies, and remember that digesting information (e.g. a S.A.L.A.M.I.) in small doses is always more effective at ‘keeping the information down’ so it may be applied and utilized successfully later.

Kornell, N., Bjork, R. The promise and perils of self-regulated study. Psychon Bull Rev. 2007;14 (2): 219-224.
Karpicke, J. D., Butler, A. C., & Roediger, H. L. Metacognitive strategies in student learning: Do students practice retrieval when they study on their own? Memory . 2009; 17: 471– 479.
Persky, A.M., Hudson, S. L. A snapshot of student study strategies across a professional pharmacy curriculum: Are students using evidence-based practice? Curr Pharm Teach Learn. 2016; 8: 141-147.
Dunlosky , J., Rawson , K.A., Marsh , E.J., Nathan , M.J., Willingham , D.T. Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology. Psychol Sci Publ Int. 2013; 14 (1): 4-58.
Koriat, A., & Bjork, R. A. Illusions of competence during study can be remedied by manipulations that enhance learners’ sensitivity to retrieval conditions at test. Memory & Cognition . 2006; 34 : 959-972.

James M. Culhane, Ph.D. Chair and Professor, School of Pharmacy, Notre Dame of Maryland University

Article contents

Habit formation and behavior change.

Benjamin Gardner Benjamin Gardner Department of Psychology, King's College London
and Amanda L. Rebar Amanda L. Rebar Department of Human, Health, and Social Sciences, Central Queensland University
https://doi.org/10.1093/acrefore/9780190236557.013.129
Published online: 26 April 2019

Within psychology, the term habit refers to a process whereby contexts prompt action automatically, through activation of mental context–action associations learned through prior performances. Habitual behavior is regulated by an impulsive process, and so can be elicited with minimal cognitive effort, awareness, control, or intention. When an initially goal-directed behavior becomes habitual, action initiation transfers from conscious motivational processes to context-cued impulse-driven mechanisms. Regulation of action becomes detached from motivational or volitional control. Upon encountering the associated context, the urge to enact the habitual behavior is spontaneously triggered and alternative behavioral responses become less cognitively accessible.

By virtue of its cue-dependent automatic nature, theory proposes that habit strength will predict the likelihood of enactment of habitual behavior, and that strong habitual tendencies will tend to dominate over motivational tendencies. Support for these effects has been found for many health-related behaviors, such as healthy eating, physical activity, and medication adherence. This has stimulated interest in habit formation as a behavior change mechanism: It has been argued that adding habit formation components into behavior change interventions should shield new behaviors against motivational lapses, making them more sustainable in the long-term. Interventions based on the habit-formation model differ from non-habit-based interventions in that they include elements that promote reliable context-dependent repetition of the target behavior, with the aim of establishing learned context–action associations that manifest in automatically cued behavioral responses. Interventions may also seek to harness these processes to displace an existing “bad” habit with a “good” habit.

Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. Behavior change techniques that may facilitate movement through discrete phases in the habit formation trajectory are highlighted, and techniques that have been used in previous interventions are explored based on a habit formation framework. Although these interventions have mostly shown promising effects on behavior, the unique impact on behavior of habit-focused components and the longevity of such effects are not yet known. As an intervention strategy, habit formation has been shown to be acceptable to intervention recipients, who report that through repetition, behaviors gradually become routinized. Whether habit formation interventions truly offer a route to long-lasting behavior change, however, remains unclear.

automaticity
behavior change
dual process

What Are Habits and Habitual Behaviors ?

Everyday behaviors shape human health. Many of the dominant causes of death, including heart disease, diabetes, cancer, chronic lower respiratory diseases, and stroke, are preventable (World Health Organization, 2017 ). Adopting health-promoting behaviors such as eating more healthily or increasing physical activity may improve quality of life, physical and mental health, and extend lives (Aune et al., 2017 ; Centers for Disease Control and Prevention, 2014 ; Rebar et al., 2015 ; World Health Organization, 2015 ). For some behaviors, one performance is sufficient to attain desired health outcomes; a single vaccination, for example, can yield immunity to disease (e.g., Harper et al., 2004 ). For many behaviors, however, achieving meaningful health outcomes depends on repeated performance: Going for a run once, for example, will not achieve the same health benefits as regular activity over a prolonged period (Erikssen et al., 1998 ). In such instances, behavior change must be viewed as a long-term process, which can be conceptually separated into stages of initiation and maintenance (Prochaska & DiClemente, 1986 ; Rothman, 2000 ). This distinction is important from a practical perspective because while people may possess the capability, opportunity, and motivation to initiate behavior change (Michie, van Stralen, & West, 2011 ), they often fail to maintain it over time, lapsing back into old patterns of behavior (Dombrowski, Knittle, Avenell, Araujo-Soares, & Sniehotta, 2014 ). Some have attributed this to changes in motivation after initial experiences of action (Armitage, 2005 ; Rothman, 2000 ). People may overestimate the likelihood of positive outcomes or the valence of such outcomes, or they may fail to anticipate negative outcomes (Rothman, 2000 ). Alternatively, a newly adopted behavior may lose value and so become deprioritized over time. Motivation losses threaten to derail initially successful behavior change attempts.

Habit formation has attracted special attention as a potential mechanism for behavior change maintenance (Rothman, Sheeran, & Wood, 2009 ; Verplanken & Wood, 2006 ) because habitual behaviors are thought to be protected against any dips in conscious motivation. Viewing habit as a means to maintenance may seem truistic; in everyday discourse, a habit is an action done repetitively and frequently, and so making action habitual will necessarily entail maintenance. Within psychology, however, the term habit denotes a process whereby exposure to a cue automatically triggers a non-conscious impulse to act due to the activation of a learned association between the cue and the action (Gardner, 2015 ). Habit is learned through “context-dependent repetition” (Lally, van Jaarsveld, Potts, & Wardle, 2010 ): Repeated performance following exposure to a reliably co-occurring cue reinforces mental cue-action associations. As these associations develop, the habitual response gradually becomes the default, with alternative actions becoming less cognitively accessible (Danner, Aarts, & de Vries, 2008 ). Habit is formed when exposure to the cue is sufficient to arouse the impulse to enact the associated behavior without conscious oversight (Gardner, 2015 ; Neal, Wood, Labrecque, & Lally, 2012 ; Wood, Labrecque, Lin, & Rünger, 2014 ). In the absence of stronger influences favoring alternative actions, the habit impulse will translate smoothly and non-consciously into action, and the actor will experience behavior as directly cued by the context (Wood & Neal, 2007 ).

Defining habit as a process that generates behavior breaks with earlier definitions, which depicted habit as a form of behavior (see Gardner, 2015 ). This definition of habit as a process resolves a logical inconsistency that arises from portraying habit as a determinant of behavior (e.g., Hall & Fong, 2007 ; Triandis, 1980 ); as Maddux ( 1997 , pp. 335–336) noted, “a habit cannot be both the behavior and the cause of the behavior.” It also allows for the habit process to manifest in multiple ways for any behavior. A distinction has been drawn between habitually instigated and habitually executed behavior (Gardner, Phillips, & Judah, 2016 ; Phillips & Gardner, 2016 ). Habitual instigation refers to habitual triggering of the selection of an action and a non-conscious commitment to performing it upon encountering a cue that has consistently been paired with the action in the past. Habitual execution refers to habit facilitating completion of the sub-actions that comprise any given action such that the cessation of one action in a sequence automatically triggers the next. Take, for example, “eating a bag of chips.” While people typically mentally represent this activity as a single unit of action (Wegner, Connally, Shearer, & Vallacher, 1983 , cited in Vallacher & Wegner, 1987 ), it can be deconstructed into a series of discrete sub-actions (e.g., “opening bag,” “putting hand in bag,” “putting food in mouth,” “chewing,” “swallowing”; Cooper & Shallice, 2000 ). “Eating a bag of chips” is habitually instigated to the extent that the actor is automatically cued to select “eating chips” from available behavioral options. This may also activate the first sub-action in the sequence (“opening bag”). “Eating a bag of chips” is habitually executed to the extent that the cessation of, for example, “putting my hand in the bag” habitually cues “putting food in mouth,” the cessation of which habitually cues “chewing,” and so on, until the perceptually unitary action (“eating a bag of chips”) is complete. 1 The term habitual behavior describes any action that is either instigated or executed habitually. This includes actions that are habitually instigated but non-habitually executed (e.g., habitually triggered to begin eating a bag of chips, but deliberates about how many chips to put in mouth), non-habitually instigated but habitually executed (e.g., consciously decides to eat a bag of chips, but habitually puts the chips in mouth, chews, and swallows), or both habitually instigated and habitually executed (e.g., habitually starts eating chips, and habitually puts them in mouth, chews, and swallows; Gardner, 2015 ). This description allows for a behavior to be habitual, yet not fully automated (see Aarts, Paulussen, & Schaalma, 1997 ; Marien, Custers, & Aarts, 2019 ) and better resonates with everyday experiences of complex health behaviors such as physical activity, which may be partly habit-driven, yet also require conscious oversight to be successfully completed (Rhodes & Rebar, 2019 ).

Habit has been implicated in behaviors across a range of domains, including media consumption (LaRose, 2010 ), purchasing patterns (Ji & Wood, 2007 ), environmentally relevant actions (Kurz, Gardner, Verplanken, & Abraham, 2014 ), and health behaviors. Studies have pointed to a multitude of health-related actions that may potentially be performed habitually, including dietary consumption (Adriaanse, Kroese, Gillebaart, & De Ridder, 2014 ), physical activity (Rebar, Elavsky, Maher, Doerksen, & Conroy, 2014 ), medication adherence (Hoo, Boote, Wildman, Campbell, & Gardner, 2017 ), handwashing (Aunger et al., 2010 ), and dental hygiene (Wind, Kremers, Thijs, & Brug, 2005 ). Habit strength is consistently found to correlate positively with behavioral frequency (Gardner, de Bruijn, & Lally, 2011 ; Rebar et al., 2016 ) and may bridge the “gap” between intention and behavior, though there are varying accounts regarding interplay between habits and intentions in regulating behavior. Some have argued that people are more likely to act on intentions when they have habits for doing so (Rhodes & de Bruijn, 2013 ). When motivation is momentarily low upon encountering associated contexts, habit may translate into performance despite motivational lapses. In this way, habit has been proposed to represent a form of self-control, protecting regularly performed behaviors that are desired in the longer-term against shorter-term motivation losses (Galla & Duckworth, 2015 ). Other studies have suggested that habit can direct action despite intentions not to act (Neal, Wood, Wu, & Kurlander, 2011 ; Orbell & Verplanken, 2010 ; but see Rebar et al., 2014 ). For example, one study showed that United Kingdom smokers with habits for smoking while drinking alcohol reported “action slips” after the introduction of a smoking ban in public houses; despite intending to adhere to the ban, several reporting “finding themselves” beginning to light up cigarettes while consuming alcohol (Orbell & Verplanken, 2010 ). These two perspectives concur in highlighting the potential for habit to override conscious motivational tendencies. Such effects may be attributable to habitual instigation rather than execution (Gardner et al., 2016 ); someone who is habitually prompted to act is more likely to frequently perform those actions and to do so without relying on intention.

The effects of habit—or more specifically, instigation habit (Gardner et al., 2016 )—have important implications for behavior maintenance. By virtue of their cue-dependent, automatic nature (Orbell & Verplanken, 2010 ), habitually instigated behaviors should, in theory, persist even when they no longer serve the goal that initially motivated performance, or where motivation has eroded (Wood & Neal, 2007 ). For example, a person starting a new job out of town may consistently decide to commute by bicycle, which will likely create a habit for bicycle commuting whereby the workday morning context automatically prompts bicycle use without any deliberation over available alternatives (Verplanken, Aarts, Knippenberg, & Moonen, 1998 ). This may, however, lead to instances whereby the commuter “accidentally” uses the bicycle out of habit, despite, for example, knowing of road closures that will slow the journey and which would render alternative transport modes preferable (see Verplanken, Aarts, & Van Knippenberg, 1997 ). This example demonstrates several key features of habitual responses: learning via consistent pairing of cues (e.g., 8 a.m. on a workday) and action (selecting the bicycle); cue-dependent automaticity (using the bicycle at 8 a.m. on a workday without deliberation); and goal-independence, persisting even where an actor no longer has the motivation to act or is motivated to act in another way (e.g., when roads are closed). It also demonstrates how habit formation can maintain behavior by “locking in” new behaviors, protecting them against losses in conscious motivation. Habit development may also play a useful role in cessation of unwanted behaviors. Many ingrained behaviors—for example, eating high-calorie snacks—persist because they have become habitual and so are difficult to change. The lack of reliance on conscious intentions that is characteristic of habitual behavior, and which is thought to protect new behaviors against motivation losses, makes it difficult to break unwanted habits despite strong intentions to do so (Webb & Sheeran, 2006 ). While habit formation per se is not a sufficient strategy for “giving up” an unwanted behavior, behavior change can be made easier by seeking to form a new (“good”) habit in place of the old (“bad”) habit, rather than attempting only to inhibit the unwanted action (Adriaanse, van Oosten, de Ridder, de Wit, & Evers, 2011 ). Indeed, in the real world, habit development often involves displacing existing actions with more desirable alternatives such as eating healthy snacks in place of higher-calorie foods (Lally, Wardle, & Gardner, 2011 ; McGowan et al., 2013 ). Such “habit substitution” can take one of two basic forms, involving either avoidance of cues to the unwanted action or the development of new responses that compete with the unwanted habitual response. The “habit discontinuity hypothesis” speaks to the former of these, arguing that naturally occurring disruption of contexts—such as a residential relocation, for example—discontinues exposure to old habit cues (Walker, Thomas, & Verplanken, 2015 ). This represents an opportunity for people to act on their conscious motivation in response to newly encountered cues, and so to develop new, potentially more desirable habitual responses such as using active travel modes in place of more sedentary travel options like driving (Verplanken & Roy, 2016 ). Bad habits offer established cue-response structures that can hasten learning of new, good habits. Thus, where discontinued cue exposure is not feasible, people may seek to develop new cue-behavior associations to compete with and ultimately override old associations (Bouton, 2000 ; Walker et al., 2015 ). For example, people wishing to reduce habitual unhealthy snacking may form plans that dictate that when they are watching television and wish to snack (cue), they will eat fruit (new, desired behavior) instead of high-calorie foods (undesired, habitual behavior; e.g., Adriaanse, Gollwitzer, De Ridder, De Wit, & Kroese, 2011 ). In both instances of discontinued cue exposure and the adoption of competing responses to existing cues, the development of new habit associations and the decaying (or deprioritizing) of old habit associations are thought to occur concurrently (Adriaanse et al., 2011 ; Walker et al., 2015 ; Wood & Neal, 2007 ).

How Does Habit Form?

There have been calls for habit formation, whether focused solely on establishing new actions or displacing unwanted actions, to be adopted as an explicit goal for behavior change interventions (Rothman et al., 2009 ; Verplanken & Wood, 2006 ). Developing effective habit formation interventions requires an understanding of how habit forms.

The concept of behavior as an automatic response to covarying contextual cues, directed by learned cue-action associations, is rooted in behaviorist principles and studies of animal learning (e.g., Hull, 1943 ; Skinner, 1938 ; Thorndike, 1911 ). For example, in his maze-learning studies, Tolman ( 1932 ) noted that his rats, having repeatedly run down the route at the end of which was a food reward, continued to pursue that route even when the reward was removed. Adams ( 1982 ) trained rats to press a lever in a cage so as to receive intermittently delivered sucrose pellets. After receiving a lithium chloride injection that caused ingestion of the sucrose to induce nausea, those rats that were more highly trained (i.e., had pressed and received the sucrose reward a greater number of times in the training phase) were likely to persist longer in pressing the lever. Of course, unlike rats, humans possess the cognitive capacity to anticipate and reflect on their actions, and health-related behaviors among humans are inherently more complex than selecting maze routes or pressing levers. Yet, homologous neural processes are implicated in the acquisition and practice of habitual responses in rats and humans (Balleine & O’Doherty, 2010 ), and, like rats, people can acquire habitual behavioral responses despite a lack of insight into those behaviors or the associations that govern their performance (Bayley, Frascino, & Squire, 2005 ).

The route to human habit formation is conceptually simple: A behavior must be repeatedly performed in the presence of a cue or set of cues (i.e., context) so that cue-behavior associations may develop. For behaviors that are initially purposeful and goal-directed, the habit-formation process represents a period of transition whereby behavioral regulation transfers from a reflective and deliberative processing system to an impulsive system, which generates action rapidly and automatically based solely on activation of associative stores of knowledge (Strack & Deutsch, 2004 ). While there has been much lab-based research into the learning of relatively simple habitual responses in humans (e.g., button pressing; Webb, Sheeran, & Luszczynska, 2009 ), only relatively recently have studies focused on formation of real-world health-related habits (Fournier et al., 2017 ; Judah, Gardner, & Aunger, 2013 ; Lally et al., 2010 ). This work has largely been facilitated by the development of the Self-Report Habit Index (SRHI; Verplanken & Orbell, 2003 ), which affords reflections on the “symptoms” of habit, such as repetitive performance, mental efficiency, and lack of awareness.

Lally et al.’s ( 2010 ) seminal habit formation study used an SRHI sub-scale to assess the trajectory of the relationship between repetition and habit development among 96 participants for a 12-week period. They were instructed to perform a self-chosen physical activity or diet-related behavior (e.g., “going for a walk”) in response to a naturally occurring once-daily cue (e.g., “after breakfast”). Each day, they reported whether they had performed the action on the previous day, and if so, rated the experienced automaticity of its performance. Habit development within individuals was found to be most accurately depicted by an asymptotic curve, with early repetitions achieving sharpest habit gains, which later slowed to a plateau. The level at which habit peaked differed across participants, with some reportedly attaining scores at the high end of the automaticity index and others peaking below the scale mean. This plateau was reached at a median of 66 days post-baseline, though there was considerable between-person variation in the time taken to reach the plateau (18–254 days, the latter a statistical forecast assuming continued performance beyond the study period). These findings were echoed in a study of adoption of a novel stretching behavior (Fournier et al., 2017 ). Once-daily performance was found to yield asymptotic increases in self-reported habit strength. Habit plateaued at a median of 106 days for a group that performed the stretch every morning upon waking, and 154 days for those who stretched in the evening before bed, which the authors interpreted as evidence of the role of cortisol (which naturally peaks in the morning) in habit learning.

These studies reveal that habit development is not linear; if this were so, the fourth repetition of a behavior would have the same reinforcing impact on habit as would, say, the 444th. Rather, the asymptotic growth curve demonstrates that initial repetitions have the greatest impact on habit development. This in turn demands that the habit formation process be broken down into discrete phases and that the early phase, characterized by the sharpest gains in automaticity, may be a critical period during which people require most support to sustain motivation before the action becomes automatic (Gardner, Lally, & Wardle, 2012 ). Lally and Gardner ( 2013 ) have proposed a framework that organizes habit formation (and substitution) into four interlinked phases (see also Gardner & Lally, 2019 ). It argues that, for new behaviors initially driven by conscious motivation, habit forms when a person (1) makes a decision to act and (2) acts on his or her decision (3) repeatedly, (4) in a manner conducive to the development of cue-behavior associations. Phases 1 and 2 may be taken together to represent pre-initiation, occurring before the first enactment of the new behavior, whereas phases 3 and 4 are post-initiation phases, addressing the motivational and volitional elements needed to sustain behavior after initial performance (phase 3) and the effect of repetition on habit associations (phase 4) (see also Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ; Rothman, 2000 ). Phase 3 captures the critical period after initiation but before habit strength has peaked (Fournier et al., 2017 ; Lally et al., 2010 ).

The framework is not intended as a theory or model of the habit formation process, but rather as a means to conceptually organize the processes and mechanisms that underpin habit development. According to the framework, any variable can promote habit formation in one or more of four ways: It may enhance motivation (phase 1) or action control (i.e., the enactment of intentions into behavior; Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ) (phase 2) so as to initiate the behavior; it may modify motivation and other action control processes to continue to perform the behavior (phase 3); or it may strengthen cue-behavior associations (phase 4). One variable may operate through multiple processes: For example, anticipating pleasure from action can motivate people to perform it for the first time (phase 1) and to continue to perform it (phase 3) (Radel, Pelletier, Pjevac, & Cheval, 2017 ; Rothman et al., 2009 ). The experience of pleasure can also quicken learning of cue-behavior associations (phase 4) (de Wit & Dickinson, 2009 ). By extension, Lally and Gardner’s ( 2013 ) framework categorizes techniques that promote habit formation according to their likely mechanism (or mechanisms) of action; techniques may enhance motivation (phase 1) or action control (phase 2) to initiate change, sustain motivation and action control over time (phase 3), or reinforce cue-behavior associations (phase 4).

Which Behavior Change Techniques Should Be Used to Form Habit?

The most comprehensive taxonomy of behavior change techniques currently available defines habit formation as a discrete technique, which it defines as any effort to “prompt rehearsal and repetition of the behavior in the same context repeatedly so that the context elicits the behaviour” (Michie et al., 2013 , Suppl. Table 3 , p. 10). Yet, this definition incorporates only context-dependent repetition and not any other technique that may promote habit by increasing the likelihood of context-dependent repetition (i.e., promoting motivation or action control; phases 1–3 of Lally and Gardner’s framework) or enhancing the contribution of each repetition to the learning of habit associations (phase 4). Although context-dependent repetition is necessary for habit to form, it realistically requires supplementation with techniques targeting pre- and post-initiation phases en route to habit formation (Gardner Lally, & Wardle, 2012 ). While Michie et al. ( 2013 ) treat habit formation as a unitary technique, habit formation may perhaps be more realistically seen as an intervention approach that comprises a broader suite of techniques, which marry context-dependent repetition with strategies that: reinforce motivation; boost action control capacity, opportunity, or skills; facilitate post-initiation repetition; or quicken the learning of associations arising from repetition.

Theory points to techniques that may facilitate progression through these phases. Intention formation (phase 1 of Lally & Gardner’s [ 2013 ] framework) is likely when people anticipate that the action or its likely consequences will be positive and believe that they have a realistic opportunity and capability to perform the behavior (Ajzen, 1991 ; Bandura, 2001 ; Michie et al., 2011 ; Rogers, 1983 ; Schwarzer, Lippke, & Luszczynska, 2011 ). Providing information on the likely positive consequences of action, or choosing to pursue actions that are already most highly valued, may therefore aid habit development by enhancing motivation. Action control skills are required to initiate intention enactment (phase 2) and to maintain the behavior by consistently prioritizing the intention over competing alternatives (phase 3). This will likely be facilitated by self-regulatory techniques such as planning, setting reminders, self-monitoring, and reviewing goals to ensure they remain realistic and attractive, and receiving (intrinsic) rewards contingent on successful performance (Gardner et al., 2012 ; Lally & Gardner, 2013 ). People are most likely to engage in context-dependent repetition in response to highly salient cues (e.g., event- rather than time-based cues, which likely require conscious monitoring; McDaniel & Einstein, 1993 ). Pairing the action with more frequently and consistently encountered cues may quicken habit learning at phase 4 (Gardner & Lally, 2019 ). Highly specific action plans detailing exactly what will be done and in exactly which situation (i.e., implementation intentions; Gollwitzer, 1999 ) should therefore be conducive to the acquisition of associations (but see Webb et al., 2009 ). Implementation intentions can also facilitate habit substitution: By consistently enacting new, pre-specified cue responses that directly compete with existing habitual responses, such as feeding children water instead of sugary drinks (McGowan et al., 2013 ), new responses may acquire the potential to override and erode old habitual responses (Adriaanse et al., 2011 ). The reinforcing value of repetition may also be strengthened where intrinsic reward is delivered or attention is drawn to an undervalued intrinsic reward arising from action (Radel et al., 2017 ).

Which Behavior Change Techniques Have Been Used to Form Habit, and with What Effect?

While theory can recommend techniques that should be used to promote habit formation, evaluations of habit-based interventions are needed to show which techniques have been used, and with what effect, in real-world behavior change contexts. To this end, a systematic literature search was run to identify habit-based health-promotion interventions and to document the behavior change methods used.

Four psychology and health databases (Embase, Medline, PsycInfo, Web of Science) were searched in March 2018 to identify sources that had cited one of nine key papers about habit and health. These sources were selected to capture topics of habit measurement (Gardner, Abraham, Lally, & de Bruijn, 2012 ; Ouellette & Wood, 1998 ; Verplanken & Orbell, 2003 ), principles and processes of habit formation (Gardner, Lally, & Wardle, 2012 ; Lally & Gardner, 2013 ; Lally et al., 2010 ; Lally et al., 2011 ), and conceptual commentaries (Gardner, 2015 ; Wood & Rünger, 2016 ). Papers were eligible for review if they (a) were published in English, (b) were peer-reviewed, (c) reported primary quantitative or qualitative data, (d) had tested efficacy or effectiveness for changing behavior or habit, (e) used interventions designed to promote habit formation for health behaviors, (f) targeted context-dependent repetition, and (g) were informed by theory or evidence around habit, operationalized as a learned automatic response to contextual cues or a process that generates such responses. Interventions adopted primarily to elucidate the habit formation process (rather than to develop or assess intervention effectiveness; e.g., Judah et al., 2013 ; Lally et al., 2010 ) and any that focused exclusively on breaking existing habits (e.g., Armitage, 2016 ) were excluded. For each eligible intervention, all available material was coded, including linked publications (e.g., protocols), to identify component techniques using the Behavior Change Technique Taxonomy v1 (Michie et al, 2013 ).

Twenty papers, reporting evaluations of 19 interventions, were identified. Four of the 19 interventions represented variants of interventions used elsewhere in the 20 papers. For example, one trial evaluated the same habit-based intervention component in two conditions, which varied only in the frequency of supplementary motivational interviews and booster phone calls (Simpson et al., 2015 ). Thus, the 19 could be reduced to 15 unique habit-based interventions, of which four focused on both dietary and physical activity habits, six on physical activity (or sedentary behavior) only, two on dietary consumption only, two on dental hygiene, and one on food safety. In all of the studies, habit measures were self-reported.

Diet and Physical Activity Interventions

One randomized controlled trial (RCT) compared, in overweight and obese adults, an intervention that included advice on forming and substituting healthy for unhealthy habits, with a non-habit-based intervention that emphasized relationships with food, body image, and weight biases (Carels et al., 2014 ; see also Carels et al., 2011 ). Those in the habit-based intervention received training on changing old routines and developing new ones, including advice on using cues and forming implementation intentions. Both intervention groups received weekly weight assessments and monitored their physical activity, calorie intake, and output. At a 6-month follow-up, both the habit-based ( n = 30) and non-habit intervention groups ( n = 29) were eating a healthier diet, exercising more regularly, and had lost weight. Physical activity habit strengthened and sitting habit weakened in both groups, though no between-group differences were found in weight loss or habit strength.

Lally et al.’s ( 2008 ) “Ten Top Tips” weight loss intervention centered on a leaflet outlining recommendations for forming healthy eating and physical activity habits, as supplemented by a daily adherence monitoring diary. The leaflet included advice on routinization, identifying effective cues, and habit substitution. A small non-randomized trial compared the intervention, augmented with monthly ( n = 35) or weekly weighing ( n = 34), against a no-treatment control. The intervention group lost more weight than the control group at 8 weeks and maintained weight loss at 32 weeks. Scores at 32 weeks suggested the tips had become habitual, and habit change correlated positively with weight loss (Lally et al., 2008 ; see also Lally et al., 2011 ). In a subsequent RCT (Beeken et al., 2012 , 2017 ), intervention recipients ( n = 267) lost more weight at 3 months than did a usual-care group ( n = 270). At 24 months, the intervention group had maintained weight loss, though the usual care group had lost a similar amount of weight. Habit strength, measured only at baseline and 3 months, increased more in the intervention than in the control group (Beeken et al., 2017 ). Weight loss at 3 months was attributable to gains in both habit and self-regulatory skill (Kliemann et al., 2017 ).

Simpson et al.’s ( 2015 ) weight-loss intervention provided participants with motivational advice designed to prompt intention formation, with information about how to form dietary and activity habits, and social support. Two intervention variants, differing according to the frequency of sessions, were evaluated against a minimal-treatment control, which did not feature habit-based advice, in a feasibility RCT among obese patients. Recipients of the more intensive intervention variant ( n = 55) showed greater BMI reduction at a 12-month follow-up than did the less intensive intervention ( n = 55) or control groups ( n = 60). There were no between-group differences at 12 months in physical activity or overall healthy eating, nor were there differences in activity or diet habit scores.

One RCT compared an 8-week computer-tailored intervention designed to reduce cardiovascular risk against a no-treatment control among cardiac and diabetes rehabilitation patients who already intended to increase their activity and fruit and vegetable consumption (Storm et al., 2016 ). The intervention provided information about health risks of inactivity and unhealthy diet and enhancing self-regulatory skills. Immediately following intervention cessation, fruit and vegetable consumption and physical activity habit and behavior scores were greater among the intervention ( n = 403) than control group ( n = 387), but no differences were observed 3 months post-baseline.

Physical Activity and Sedentary Behavior Interventions

An intervention for new gym members promoted habits for both physical activity and preparatory actions for gym attendance (e.g., packing a gym bag; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). Members received advice on how to form habits, including selecting time cues, setting action plans, and using accessories to increase enjoyment and so support cue-consistent performance and foster intrinsic motivation, which theory suggests can strengthen the impact of repetition on habit development (Lally & Gardner, 2013 ). Moderate-to-vigorous physical activity gains, objectively observed at an 8-week follow-up, were greater among intervention recipients ( n = 47) than the no-treatment control group ( n = 47). Habit strength was not assessed.

All 49 participants in Fournier et al.’s ( 2017 ) RCT were given access to twice-weekly, 1-hour tailored physical activity sessions for 28 weeks, with one group ( n = 23) also sent SMS reminders targeting intrinsic motivation and consistent performance to the intervention group to foster habitual attendance. Although physical activity habit strength (assessed using a subscale of the SRHI) increased for both groups immediately post-intervention, the SMS group experienced quicker habit gains. Marginally greater activity was observed in the SMS group at 12 months.

One 4-month intervention for middle- to older-aged adults comprised seven 2-hour group sessions and sought to create new balance and strength exercise habits by recommending small modifications to everyday routines (e.g., placing frequently used items on high shelves to promote stretching to reach them) (Fleig et al., 2016 ; see also Clemson et al., 2012 ). An uncontrolled trial among 13 participants showed that, while there were no apparent changes in objectively measured physical performance, there were considerable habit strength gains for the recommended actions over 6 months. Notably, participants reported in interviews that the exercises had become automatically triggered, yet they performed them consciously, suggesting that the intervention promoted habitual instigation rather than execution.

Another intervention promoting small activity changes in older adulthood was evaluated in two papers (Matei et al., 2015 ; White et al., 2017 ). Drawing on Lally et al.’s ( 2008 ) “Ten Top Tips,” it comprised a leaflet offering recommendations for integrating and substituting light-intensity physical activities into everyday routines, with supplementary self-monitoring record sheets (Gardner, Thune-Boyle, et al., 2014 ). An 8-week uncontrolled trial was undertaken among two discrete samples (Matei et al., 2015 ). No changes were found in sitting time, physical activity, or sitting or physical activity habit among one sample ( n = 16), but a second sample ( n = 27) reported decreased sitting time and increased walking. Qualitative data suggested both groups experienced automaticity gains and some health benefits. A subsequent pilot RCT showed that intervention recipients ( n = 45) experienced no greater change than did a control group ( n = 46) who received a pre-existing fact sheet promoting activity and reducing sitting, but with no habit-based advice (White et al., 2017 ). Both groups reduced sitting time and sitting habit and increased activity and activity habit.

Using an experience sampling design, Luo et al. ( 2018 ) tracked change in standing or moving breaks from sedentary behavior in office workers given 3 weeks of access to automated computer-based reminders to break up sitting, timed to occur based on daily self-selected work and break durations. Although sitting behavior was not monitored, habit strength and self-regulation for taking “moving breaks” during work hours both increased significantly across the study.

Similarly, Pedersen et al. ( 2014 ) evaluated a software package that automatically deactivated desk-based employees’ computer screens every 45 minutes to substitute new physical activity habits for existing prolonged sitting habits. Although all participants received information on the detrimental health impact of sitting and benefits of activity, self-report activity data suggested that those who used the software for 13 weeks ( n = 17) expended greater energy per day than did those not given the software ( n = 17).

Dietary Interventions

One intervention promoted habitual healthy child-feeding practices among parents of children aged 2–6 years (McGowan et al., 2013 ). On each of four occasions over 8 weeks, parents chose to pursue one of four families of habit formation targets (increased feeding of fruit, vegetables, water, and healthy snacks). They received advice on the importance of child dietary consumption and on self-regulatory strategies, including action planning, goal setting, and context-dependent repetition. An RCT showed that intervention parents ( n = 58) reported greater child intake of vegetables, water, and healthy snacks but a waiting-list control group ( n = 68) did not. Habit strength increased for all three behaviors, and a habit score averaged across behaviors correlated with behavior change (McGowan et al., 2013 ; see also Gardner, Sheals, Wardle, & McGowan, 2014 ).

In one RCT, fruit and vegetable consumption changes were compared between participants who received habit-based messages, and those receiving general, non-habit-based tips for increasing consumption or messages about healthy eating more broadly (Rompotis et al., 2014 ). Notably, habit-based messages focused on anticipating stimulus control and environmental modification and on eating the same fruits and vegetables at the same time each day, so targeting both habitual instigation and execution (see Phillips & Gardner, 2016 ). The intervention was delivered via SMS in one set of conditions and email in the other. At 8-weeks post-intervention, both intervention groups (SMS n = 26, email n = 30) had increased fruit consumption and fruit habit strength, but those in all other conditions had not (SMS fruit and vegetable tips, n = 24, SMS healthy eating tips, n = 23; email fruit and vegetable tips, n = 29, email healthy eating n = 29). No effects were found on vegetable consumption or habit.

Oral Hygiene

Two school-based interventions aimed to increase tooth brushing in primary school children. One involved weekly dental hygiene lessons and daily tooth brushing practice time (Gaeta, Cavazos, Cabrera, & Rosário, 2018 ). School visits were also made by health promoters, and a seminar was held for teachers. One control group ( n = 52) received the visits and seminar only, and a second control group ( n = 52) received the seminar only. A quasi-experiment showed that children in the habit-based intervention ( n = 106) and visits-and-seminar control group had less dental plaque, and a stronger tooth brushing habit at 12-week follow-up than did the seminar-only control group. The habit-based intervention group had the lowest plaque.

Wind et al.’s ( 2005 ) intervention also involved allocation of a designated tooth brushing time during the school day and encouragement from teachers. Tooth brushing rates increased in the intervention group ( n = 141) during treatment but not in the control group (the nature of which could not be identified from the published report; n = 155). There were no differences in behavior at 12-months post-intervention nor in habit at any follow-up.

Food Safety

An intervention promoted the microwaving of dishcloths or sponges, for hygiene reasons (Mullan, Allom, Fayn, & Johnston, 2014 ). Recipients received emails and a poster providing instructions on how and why to microwave the dishcloths and sponges, designed to be placed in kitchens to act as a cue to the action. In an RCT, one intervention group was instructed to self-monitor their action, for intervention purposes, every 3 days ( n = 15) and another every 5 days ( n = 17). Relative to those who received an unrelated control treatment ( n = 13), frequency and habit strength increased in the two intervention groups at 3 weeks and was sustained to the final 6-week follow-up.

Behavior Change Techniques Used in Previous Interventions

A total of 32 discrete behavior change techniques were each identified in at least one of the 15 interventions (see Table 1 and Table 2 ). Aside from context-dependent repetition itself—which, as an inclusion criterion, was necessarily present in all interventions—the most commonly used were “use prompts and cues” (present in 11 interventions; 73%), “action planning” (8 interventions; 53%), “provide instruction on how to perform the behavior” (8 interventions; 53%), “set behavioral goals” (8 interventions; 53%), and “self-monitor behavior” (7 interventions; 47%). Also common were “behavioral practice or rehearsal” (6 interventions; 40%), “provide information on health consequences” (6 interventions; 40%), and “problem solving” (5 interventions; 33%). “Behavioral substitution” and habit substitution (labeled “habit reversal” in the taxonomy) were each used in 4 interventions (27%).

Table 1. Behavior Change Techniques Identified in 15 Habit Formation Interventions

Note . With the exception of “context-dependent repetition,” all technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

* This technique is labeled “habit formation” in the BCT Taxonomy v1 (Michie et al., 2013 ). Rephrasing this as “context-dependent repetition” more clearly delineates the underlying technique (i.e., to consistently repeat behavior in an unvarying context) from the outcome that it is designed to serve (i.e., to form habit). It also better acknowledges the possibility that such repetition may not lead to the formation of habit. For example, Lally et al. ( 2010 ) observed some participants who failed to attain peak habit strength in an 84-day study period, and some who experienced gains that peaked at low levels, suggesting that while repetition had rendered the behavior more habitual, the action remained predominantly regulated by conscious motivation rather than habit.

Table 2. Behavior Change Techniques Documented in 15 Habit Formation Interventions

Note . All technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

While all 15 interventions were based on the principle of habit formation, none used context-dependent repetition as a standalone technique. 2 The use of techniques additional to repetition echoes the view that in the real world, habit is best promoted by embedding context-dependent repetition into a broader package of techniques that also target motivation and action control, which are prerequisites for repetition (Lally & Gardner, 2013 ). Techniques most commonly adopted in past interventions have focused predominantly on action control (e.g., planning, goal-setting, identifying cues, rehearsing action, problem solving). The relative paucity of techniques targeting motivation may reflect an assumption that, for most of the behaviors targeted, intervention recipients generally recognize the value of behavior change, but lack the volitional skills, opportunities, or resources to change. Whether motivation should be targeted as part of a habit-formation intervention will depend on whether target populations understand the need for change and prioritize the target behavior above alternatives.

Fewer than half of the 15 interventions appear to have addressed factors that may moderate the relationship between repetition and habit development. Theory and evidence suggest that the mental associations that underlie habit will develop most strongly or quickly where actions are more simple or intrinsically rewarding and in response to cues that are salient and consistently encountered (Lally & Gardner, 2013 ; McDaniel & Einstein, 1993 ; Radel et al., 2017 ). Several of the reviewed interventions purposively promoted habit formation for simple behaviors (Beeken et al., 2017 ; Fleig et al., 2016 ; Lally et al., 2010 , 2011 ; Matei et al., 2015 ; Mullan et al., 2014 ; White et al., 2017 ). Kaushal et al. ( 2017 ) emphasized the importance of intrinsic reward in their physical activity promotion intervention, and Fournier et al. ( 2017 ) targeted intrinsic motivation. These studies highlight how interventions may move beyond simply promoting repetition toward targeting factors that may reduce the number of repetitions required for a target behavior to become habitual.

How Should Habit-Based Interventions Be Evaluated?

Previous interventions attest to the potential for habit-based approaches to change behavior. Although many intervention studies were not designed to test effectiveness, 13 of the 15 interventions were associated with positive change on at least one index of behavior or behavior-contingent outcomes (e.g., weight loss) at one or more follow-ups. Process evaluations pointed to the strengthening of habit as a key mechanism underpinning behavioral change based on increases in self-reported automaticity scores or qualitative reflections on the subjective experience of automaticity (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Kliemann et al., 2017 ; Lally et al., 2011 ; Matei et al., 2015 ). Additionally, acceptability studies have suggested that recipients find the concept of context-dependent repetition—which distinguishes habit-based and non-habit-based interventions—easy to understand and follow (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Lally et al., 2011 ; Matei et al., 2015 ).

Limitations of evaluation methods preclude understanding of how best to support habit formation. It is not yet clear whether promotion of context-dependent repetition is necessary for habit to develop or, indeed, whether it represents the most “active” ingredient of a habit formation intervention. One study found that a control group that did not receive habit-based advice reported similar physical activity habit gains to those among a group that received habit guidance (White et al., 2017 ). Conversely, another study showed that intervention recipients deviated from habit-based advice (e.g., by setting goals that were not specific, measurable, or achievable), yet habit strengthened (Gardner, Sheals, et al., 2014 ). Habit formation may therefore arise as a byproduct of interventions that do not explicitly target habit development. The unique contribution of context-dependent repetition to behavior change remains unclear because none of the reviewed studies compared a habit-based intervention with an otherwise identical non-habit-based equivalent. Indeed, most studies have evaluated habit formation interventions against minimal-treatment control groups or used uncontrolled designs. Future research should seek to compare matched habit- and non-habit-based interventions or otherwise use factorial designs, which allow testing for isolated effects within a multicomponent intervention, or mediation analyses, which can assess whether habit change underpins intervention effects.

Intervention evaluations have also been limited by short follow-up periods, which is ironic given that the key purported benefit of incorporating habit formation into interventions is the potential to increase longevity of behavior change. Few studies evaluated outcomes over 12 months or longer, with the longest observed follow-up being 24 months (Beeken et al., 2017 ). Beeken et al.’s ( 2017 ) “Ten Top Tips” intervention showed greater impact than did a non-habit-based usual-care treatment on dietary and physical activity habits, and weight loss, at the 3-month follow-up, which the authors found to be due in part to habit development (Kliemann et al., 2017 ). Yet, while weight loss was maintained at 24 months, the advantage conferred by the habit-based intervention over usual care was lost, suggesting that any habit gains may have dissipated, or alternatively, that for those who were successful in maintaining the behaviors over the 2-year period, habit formation had occurred regardless of condition. These possibilities cannot be investigated because habit strength was not evaluated at 24 months. Elsewhere, however, a small exploratory (non-intervention) study suggested that habit gains may erode over time: Among a group of participants forming dental flossing habits over 8 weeks, habit strength had considerably eroded in the subgroup of participants who provided data at a 6-month follow-up (Judah et al., 2013 ). Until more is done to assess the longevity of habit-based intervention effects, the hypothesis that habit persists over time, and so supports behavior maintenance, remains insufficiently tested.

Theory proposes that, through consistent performance, behaviors become habitual such that they are initiated automatically upon encountering cues via the activation of learned context-behavior associations. Habitual behaviors are thought to be self-sustaining, and so forming a habit has been proposed as a means to promote long-term maintenance of behavior. Interventions that seek to promote habit formation should include not only advice on context-dependent repetition, but also techniques that support the motivation and action control needed to repeat the action and that may enhance the reinforcing value of repetition on habit development. Fifteen interventions were found to have used habit formation principles to encourage engagement in health-promoting behaviors, and these have tended to supplement advice on repetition with action control techniques. Previous research suggests a habit-based approach has much to offer to behavior change initiatives; habit formation offers an acceptable, easily understood intervention strategy, with the potential to change behavior and yield favorable health outcomes. Yet, the unique effects of habit-specific techniques, and the longevity of effects, have not been adequately explored. The central assumption of the habit-based approach—that habit gains translate into long-term behavior maintenance—remains largely untested.

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1. Rhodes and colleagues have extended this line of thinking by incorporating preparatory actions into the process, showing that habitual preparation for an activity (e.g., packing a gym bag) can influence frequency of engagement in the focal behavior (in this case, exercise; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). However, this differs from the instigation–execution distinction in that it focuses on the role of habit in different behaviors (preparatory actions vs. focal actions) rather than different roles of habit in the same behavior.

2. This is perhaps inevitable given the present review criteria, which excluded studies that used context-dependent repetition to study the habit formation process itself. However, real-world studies of the formation of health habits have not been based on context-dependent repetition alone; both Lally et al. ( 2010 ) and Fournier et al. ( 2017 ) instructed participants to use prompts and cues and set action plans or implementation intentions (see also Judah et al., 2013 ).

Methods of Health Behavior Change
Theoretical Approaches to Physical Activity Promotion
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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 ).

Authors’ contributions

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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DOI : https://doi.org/10.1186/s40594-017-0055-6

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Memory Part 1: Overview

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A common layman's conception of memory is the simple storage and retrieval of learned information that often evokes comparison to a filing system. Our everyday experience of memory, however, is in fact a complicated multifactorial process that consists of both conscious and unconscious components, and depends on the integration of a variety of information from distinct functional systems, each processing different types of information by different areas of the brain (substrates), while working in a concerted fashion. 1 ⇓ ⇓ – 4 In other words, memory is not a singular process. It represents an integrated network of neurologic tasks and connections. In this light, memory may evoke comparison with an orchestra composed of many different instruments, each making different sounds and responsible for different parts of the score, but when played together in the proper coordinated fashion, making an integrated musical experience that is greater than the simple sum of the individual instruments.

Memory consists of 2 broad categories ( Fig 1 ): short- and long-term memory. 5 Short-term memory is also called working memory. Long-term memory can be further divided into declarative and nondeclarative memory. These are also referred to as explicit/conscious and implicit/unconscious memory, respectively. Declarative or explicit memory consists of episodic (events) and semantic (facts) memory. Nondeclarative or implicit memory consists of priming, skill learning, and conditioning. In everyday experiences ( Fig 2 ), declarative memory refers to things you know that you can tell others, and nondeclarative or procedural memory to things you know that you can show by doing. 6 An example of episodic memory is remembering your first day of school, whereas semantic memory is knowing the capital of Canada. Nondeclarative memory manifests as knowing how to ride a bicycle (skill learning), being more likely to use a word that you recently heard (priming), and salivating when you see a favorite food (conditioning).

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Classification of memory.

The everyday experience of different aspects of memory. Adapted with permission from Breedlove SM, Watson NV, Rosenzweig MR. Biological Psychology: An Introduction to Behavioral, Cognitive, and Clinical Neuroscience . 6th ed. Sunderland, Massachusetts: Sinauer Associates; 2010.

The process of memory is dynamic with continual change over time. 5 Memory traces are initially formed as a series of connections between the hippocampus and various cortical areas that are responsible for processing the particular characteristics of an event ( Fig 3 ). Over time, with repetitive conscious retrieval of the memory and/or sleep, connections are formed between the cortical areas relevant for that memory, while the hippocampal connections become less important. Eventually, the hippocampal connections become unnecessary and the cortical connections stand alone as an integrated representation of the memory. This occurs when the memory is consolidated. Figure 4 illustrates a more concrete example of this phenomenon. A particular event (such as a birthday party) with auditory, visual, and spatial information is acquired (encoded). The hippocampus holds a unified representation of the event and the auditory information is distributed to the superior aspect of the temporal lobe, the spatial information is distributed to the parietal lobe (inferior parietal lobule), and the visual information is distributed to the occipital cortex. The memory traces are formed as connections between the hippocampus and these cortical areas. At this point in time, the memory has yet to be consolidated so that retrieval of the memory requires reactivation of the particular connections between the hippocampus and cortical regions. After the memory is consolidated, the cortical-to-cortical connections strengthen and the hippocampal-to-cortical connections weaken or become unnecessary. The memory is then directly accessed from the neocortex, bypassing the hippocampus. A key point is that the early unconsolidated memory consists of hippocampal-to-cortical connections that are relatively rapid forming but transient, whereas the consolidated memory consists of cortical-to-cortical connections that are slow forming and long-lasting.

Memory consolidation. Connections between the hippocampus and various cortical modules are critical in early memory formation (left). Over time, the cortical modules form connections among themselves and the connections with the hippocampus become less important (center). When the memory is fully consolidated, only the cortical to cortical connections are important and the hippocampus becomes unnecessary (right). Adapted with permission from Frankland PW, Bontempi B. The organization of recent and remote memories. Nat Rev Neurosci 2005;6:119–30.

Retrieval of nonconsolidated and consolidated memory. A , An event with audio (A), spatial (S), and visual (V) information is encoded. The hippocampus contains a unified representation of the event and forms connections with the relevant cortical areas to process the audio, spatial, and visual information. B , When a retrieval cue containing only spatial and visual information of the event is encountered early in the memory formation process before the memory has been consolidated, the hippocampus plays a critical role by accessing its connections with the pertinent cortical areas for the entire memory. C , After the memory is fully consolidated, the connections with the hippocampus become unnecessary and the retrieval cue accesses the memory directly from the cortical to cortical network of connections that form the unified representation of the memory. Adapted with permission from Purves D, Cabeza R, Huettel SA, et al. Principles of Cognitive Neuroscience , 2nd ed. Sunderland, Massachusetts: Sinauer Associates; 2013.

Théodule Armand Ribot, a noted French psychologist, first described the concept of memory consolidation in 1882. 7 He noted a temporal gradient to memory loss after brain damage. The tendency for recent memories to be affected to a greater extent than remote memories has become known as Ribot's law. This commonly observed phenomenon among trauma victims underscores the transient and less stable nature of recent memories that have yet to be fully consolidated, especially if the insult occurs early in the memory processing timeframe. Memories in close chronologic proximity to the traumatic event are often lost, whereas memories that are more remote tend to be less affected. The loss of memory for events that happened before an insult/injury to the brain is called retrograde amnesia. The inability to form new memories after brain injury is called anterograde amnesia. When bilateral hippocampal formation injury occurs, the ability to form new memories can be greatly impeded because of the critical role the hippocampal formation plays in the early formation of memories. Figure 5 is a coronal T1-weighted image from an MR examination performed in 1992 showing bilateral medial temporal lobe injury to the extensively studied patient, H.M., who in 1953 underwent bilateral resection of the entire piriform-amygdaloid-hippocampal complex for seizures and subsequently experienced severe anterograde amnesia. 8 ⇓ ⇓ – 11

Patient H.M. and medial temporal lobe damage. A 1992 coronal T1-weighted image of patient H.M. shows abnormal hippocampal formation hypointense signal after previous bilateral medial temporal lobe resection performed decades earlier for intractable seizures. After the surgery, the patient experienced anterograde amnesia and has been extensively studied for years afterward. Adapted from Corkin S, Amaral DG, González RG, et al. H.M.'s medial temporal lobe lesion: findings from magnetic resonance imaging. J Neurosci 1997;17:3964–79.

Many components of the brain (prefrontal cortex, temporal lobe, parietal lobe, occipital lobe, medial diencephalon) are involved in the processes that form memories; however, this review will focus more on the medial temporal lobe and fornix. These medial temporal lobe components consist of the hippocampal formation, parahippocampal gyrus, parahippocampal cortex, entorrhinal cortex, and perirhinal cortex. The contribution of these structures to the function of memory will be the subject of the next article in the Functional Vignettes series.

Zola-Morgan S ,
Yancey SW ,
Frankland PW ,
Breedlove SM ,
Watson NV ,
Rosenzweig MR
Amaral DG ,
González RG , et al
van der Kouwe AJ ,
Tuch DS , et al
Schmolck H ,
Kensinger EA ,
Corkin S , et al
Ullman MT ,
Received April 9, 2014.
Accepted after revision April 14, 2014.

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American Journal of Neuroradiology: 35 (11)

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Mathematics, health & fitness, business & finance, technology & engineering, food & beverage, random knowledge, see full index, chapter 6 checkpoint flashcards preview, cs 115 computer science > chapter 6 checkpoint > flashcards.

True or False: A slate tablet typically does not include a physical keyboard

True or False: On a personal computer, all functions of the processor usually are on a single chip

True or False: A dual-core processor doubles the processing speed of a single-core processor

True or False: In general, multi-core processors are less energy efficient than separate multiple processors

True or False: The speed of the system clock affects the speed of peripheral devices, such as a printer or disk drive

True or False: Today’s personal computer processors do not require additional cooling

True or False: In the binary system, the digit 1 represents the electronic state of on (presence of an electronic charge).

True or False: Common sizes for memory are in the bit and byte ranger

True or False: The processor interprets and executes a program or application’s instructions while the program or application is in nonvolatile memory

True or False: You can change a removable flash memory device without having to open the system unit or restart the computer

True or False: As with processors, manufacturers state the clock speed for a bus in hertz.

True or False: The power supply converts the wall outlet DC power into AC power

The __________ is the main circuit board of the computer

motherboard

A _____-____ _________ is a single chip with 2 or more separate processor cores

multi-core processor

__________ ___________ include basic calculations such as addition, subtraction, multiplication, and division.

arithmetic operations

In the machine cycle, the _________ operation obtains a program or application instruction or data item from memory.

A processor contains small, high-speed storage locations, called _______, that temporarily hold data and instructions.

A _____ ____ is a small ceramic or metal component with fins on its surface that absorbs and disperses heat produced by electrical components, such as a processor.

An aspect of cloud computing that describes a computing environment where an Internet server hosts and deploys applications is known as _________

Software as a Service (SaaS)

A(n) _______ _____ is a circuit board that enhances the functions of a component of a desktop or server system unit and/or provides connections to peripheral devices

adapter card

the main circuit board of the computer

small piece of semiconducting materials, usually silicon, on which integrated circuits are etched

term used by some computer manufacturers to refer to a personal computer chip

microprocessor

small, high-speed storage locations contained in a processor

term used to describe processors that can execute more than one instruction per clock cycle

superscalar

applications that incorporate data from multiple providers into a new application

widely used coding scheme to represent a set of characters

a unique number that identifies the location of a byte in memory

ROM chips that contain permanently written data, instructions, or information

determines the number of bits that the computer can transmit at one time

What are the 2 components contained in the processor?

Processors contain a control unit and an arithmetic logic unit (ALU).

Differentiate between arithmetic operations and comparison operations.

Arithmetic operations include basic calculations such as addition, subtraction, multiplication, and division.

Comparison operations involve comparing one data item with another to determine whether the first item is greater than, equal to, or less than the other item.

What is clock speed?

Clock speed is the pace of the system clock, that is measured by the number of ticks per second.

How does clock speed affects the processor?

The faster the clock speed, the more instructions the processor can execute per second.

What should you consider when selecting a processor?

(1) determine your needs (2) determine your current processor (3) research processor models

What eCycling options are available?

(1) Wireless carriers and bog box retailers (2) not-for-profit organizations (3) secondary market (4) EPA

List some reasons that home and businesses choose cloud computing.

(1) Accessibility (2) Cost Savings (3) Space Savings (4) Scalability

In reference to cloud computing, describe the front end and back end.

Front end includes the hardware and software with which a user interacts to access the cloud. Back end consists of the servers and storage devices that manage and store the resources accessed by users.

Differentiate between volatile and nonvolatile memory.

Volatile memory is temporary, because when the computer’s power is turned off it loses its contents. Nonvolatile memory is permanent, because it does not lose its contents when power is removed from the computer.

Differentiate between RAM and ROM.

RAM (random access memory), also called main memory, consists of memory chips that can be read from and written to by the processor and other devices. ROM (read-only memory) refers to memory chips storing permanent data and instructions. RAM is volatile memory and ROM is nonvolatile.

Decks in CS 115 Computer Science Class (30):

Chapter 1 Checkpoint
Chapter 1 Notes Pt. 1
Chapter 1 Notes Pt. 2
Chapter 3 Notes Pt. 1
Chapter 3 Notes Pt. 2
Chapter 3 Checkpoint
Chapter 6 Notes Pt. 1
Chapter 6 Notes Pt. 2
Chapter 6 Checkpoint
Chapter 4 Notes
Chapter 4 Checkpoint
Chapter 7 Checkpoint
Chapter 7 Notes Pt. 1
Chapter 7 Notes Pt. 2
Chapter 8 Notes
Chapter 4 Terms
Chapter 7 Terms
Chapter 8 And 9 Terms
Chapter 8 Checkpoint
Chapter 9 Checkpoint
Chapter 11 Checkpoint
Chapter 2 Checkpoint
Chapter 10 Checkpoint
Chapter 5 Checkpoint
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