Peer Reviewed Articles About Study Strategies for College

Abstract

Previous studies, such equally those by Kornell and Bjork (Psychonomic Bulletin & Review, xiv:219–224, 2007) and Karpicke, Butler, and Roediger (Memory, 17:471–479, 2009), have surveyed college students' utilise of various report strategies, including self-testing and rereading. These studies have documented that some students do utilize self-testing (but largely for monitoring memory) and rereading, but the researchers did non assess whether individual differences in strategy utilise were related to student achievement. Thus, nosotros surveyed 324 undergraduates virtually their study habits besides every bit their college form point average (GPA). Importantly, the survey included questions nigh cocky-testing, scheduling one'south study, and a checklist of strategies commonly used by students or recommended by cerebral inquiry. Use of cocky-testing and rereading were both positively associated with GPA. Scheduling of written report fourth dimension was also an important gene: Depression performers were more probable to engage in late-night studying than were high performers; massing (vs. spacing) of study was associated with the use of fewer study strategies overall; and all students—but especially low performers—were driven by impending deadlines. Thus, self-testing, rereading, and scheduling of study play important roles in real-world educatee accomplishment.

When college students study for their classes, what strategies do they apply? Some study strategies—such as rereading text materials and cramming for tests—are ordinarily endorsed by students (e.g., Karpicke, Butler, & Roediger, 2009; Taraban, Maki, & Rynearson, 1999), even though they may not always yield durable learning. Other strategies—like self-testing—have been demonstrated to be quite effective (Roediger & Butler, 2011), but are mentioned less frequently when students report their strategies (e.k., Karpicke et al., 2009). Of grade, not all students report using the same strategies—individual differences be betwixt students with regard to their written report habits. Are these private differences in study habits related to educatee achievement? If so, what differences exist betwixt the written report habits of high achievers and low achievers? A main goal of the present study was to answer these two questions, focusing on when students schedule their study as well equally which strategies they use to learn course content. Our target strategies included those that appear popular with students or that cognitive inquiry has indicated could promote student operation, such every bit cocky-testing, asking questions, and rereading. Nosotros will first provide a brief review of studies that take investigated these specific strategies, followed by an overview of the present written report and its contribution to understanding strategy employ and student achievement.

Two large-scale studies accept surveyed students virtually their regular use of specific, concrete study strategies and their rationale for using them. One survey was administered past Kornell and Bjork (2007), who sought to depict what students practise to manage their real-world report. A grouping of 472 introductory psychology students at UCLA responded to forced choice questions regarding topics such as how they decide what to study next and whether they typically read class materials more than than in one case. Kornell and Bjork's questionnaire and the percentages of students endorsing diverse scheduling practices and strategies are presented in Tabular array 1. Results relevant to our present aims included that the bulk of students (59%) prioritize for study whatever is due soonest, and that the bulk of students utilize quizzes to evaluate how well they have learned course content (68%).

Table 1 Report habit survey and response percentages

Total size tabular array

Some other survey focused more narrowly on a particular strategy—self-testing—that an abundance of enquiry has shown can boost student learning (for a recent review, see Roediger & Butler, 2011). In particular, Karpicke et al. (2009) had 177 undergraduates gratuitous-report and so rank-lodge the strategies that they used when studying. These reports were followed past a forced choice question regarding their preferences for rereading versus self-testing. In the complimentary reports, self-testing and other retrieval-blazon activities (e.g., using flashcards) were normally reported, but the strategy most frequently reported (past 83.six% of students) was rereading notes or textbooks. For the forced selection question, rereading was over again the most pop pick, and retrieval practice became similarly popular only when it was accompanied by the possibility of rereading (assuasive for restudy afterward practise testing). Students' explanations revealed that most students cocky-test for the feedback about what they do or do not know rather than every bit a means to enhance learning. These results were consistent with the general conclusions of Kornell and Bjork (2007), likewise as with the recent conclusions of McCabe (2011), who found that students often fail to empathize that certain activities—such as testing (vs. restudying) or spacing written report (vs. massing study)—are likely to raise learning.

Although these studies reported valuable information most the prevalence of self-testing and students' rationale for its utilize, cocky-testing is just ane of many strategies that students use. Thus, a goal of the present report was (a) to appraise a wider range of commonly used study strategies (in addition to those surveyed by Kornell & Bjork, 2007), such as underlining while reading and making outlines or diagrams, also equally (b) to assess how students schedule their study, such as when they study during the day and whether they infinite or mass their practice.

Almost important, the human relationship betwixt students' reported use of these strategies and their overall grades was investigated. In the studies by Kornell and Bjork (2007) and Karpicke et al. (2009), some strategies were more popular than others, but non all students endorsed using the same ones. Neither study examined whether these individual differences in strategy use were related to student achievement. Of course, individual differences in the use of report strategies are interesting from the perspective of how students regulate their learning, but the employ of these strategies will matter well-nigh if they are related to educatee accomplishment. Thus, when students are partitioned by class bespeak average (GPA), volition different patterns of written report strategies emerge?

Theories of self-regulated learning (SRL) claim that learners utilize a diverseness of strategies to achieve their learning goals, and that the quality of strategy use should be related to performance (e.k., Winne & Hadwin, 1998; for a general review, see Dunlosky & Metcalfe, 2009). Certainly, strategies such every bit self-testing improve performance in the laboratory and when administered in the classroom (McDaniel, Agarwal, Huelser, McDermott, & Roediger, 2011; McDaniel & Callender, 2008). Withal, information technology is not evident whether this attribute of SRL theory largely pertains to more than controlled settings (e.chiliad., in the lab or when administered by a teacher) or is more than broadly applicative to settings in which students are responsible for regulating their learning. Indeed, for several reasons, a relationship between strategy utilize and accomplishment level is not guaranteed. First, the effectiveness of laboratory-tested strategies may not be every bit robust when these strategies are practical in the real world of pupil accomplishment, in which numerous courses (spanning unlike contents and cerebral abilities) contribute to students' GPA. In fact, in a popular survey of learning strategies (i.e., the Motivated Strategies for Learning Questionnaire), the question virtually relevant to self-testing (#55, "I ask myself questions to brand sure I sympathize the fabric…") was non statistically correlated with grade grades (Pintrich, Smith, Garcia, & McKeachie, 1991). Moreover, the operation benefits of some strategies are oftentimes largest after longer memory intervals (e.chiliad., Roediger & Karpicke, 2006), and hence their contributions to exam performance may be limited past many students' propensity to cram the night earlier tests (Taraban et al., 1999). Second, even if recommended strategies are an constructive means of improving GPA, it is possible that successful students accomplish their success in spite of (1) using the aforementioned pattern of strategies as depression performers or (2) using even poorer strategy options. In the former case, perhaps high and low performers choose the same strategies, but high performers use them more than adeptly. In the latter case, perhaps other factors—such as intelligence, prior experience, or degree of motivation—overpower the differential use of study strategies in determining GPA.

Given that the human relationship betwixt strategy endorsement and GPA is uncertain, our master goal was to estimate the relationship between strategy employ and GPA, with a specific focus on students' employ of cocky-testing and how students schedule their study fourth dimension. To practise so, we administered an expanded version of Kornell and Bjork's (2007) survey. Boosted questions were essential for accomplishing our most disquisitional aims (see Tabular array 1): Kickoff, three questions (8–ten) addressed how students scheduled their study time. The offset two were relevant to when during the day students studied and what fourth dimension they believed would be most effective. Afternoon and evening studying would better match college students' peak diurnal rhythms (May, Hasher, & Stoltzfus, 1993), and hence might be related to GPA. Question 10 concerned whether students spaced or massed their studying, and given the literature on the superiority of spaced practice (over massed; Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006), nosotros expected a consistent relationship betwixt GPA and spacing (vs. massing) study. Second, a checklist of pop report strategies (Question 12) was included, because the original survey past Kornell and Bjork largely concerned why students used a particular strategy (e.g., self-testing), and then the add-on of this checklist was vital for estimating the relationship betwixt strategy use and GPA.

Method

Participants

A group of 324 participants (72% female, 28% male) were recruited from the KSU participant pool, which mainly consists of students enrolled in introductory psychology courses. Introductory psychology enrollment included 78% freshmen, fifteen% sophomores, 4% juniors, and two% seniors during the semesters the survey was administered. Given that introductory psychology is a pop class that is required by many programs across KSU, this puddle includes a diverse population of students cutting across colleges and majors. Students received credit in their courses for participation.

Procedure

The survey described higher up (come across Table i) was administered to these 324 students. They were instructed to fill out the survey, which typically took less than 10 min to complete.

Even though GPA was cocky-reported (Question 11, Table 1), it was expected to be highly related to actual GPA, with whatsoever systematic inaccuracy working confronting the hypothesis that strategy employ would be related to GPA. Namely, Bahrick, Hall, and Dunlosky (1993) investigated the accuracy of self-reported and actual grades, and they found that students with higher GPAs made authentic reports, whereas the largest discrepancies occurred for the poorest-performing students, who typically overestimated operation. Whatever overestimates of GPA from the poorer students in the present study would benumb strategy–GPA relationships, and hence would work against the expectations from SRL theory.

Results

Our focal analyses involved comparison GPA to strategy employ (and most notably, the use of cocky-testing strategies). Nonetheless, below nosotros first study the overall responses to the strategy questionnaire (a) to evaluate whether our survey results replicated those of Kornell and Bjork (2007), (b) to talk over our new results concerning how students schedule their study (Questions 8–10, Tabular array 1), and (c) to highlight possible individual differences in strategy employ (Question 12).

Response proportions

The offset seven questions were identical to those used by Kornell and Bjork (2007), and Table i shows a side-by-side comparison of the response percentages at UCLA and those of the present report. Most striking is the similarity between the two sets of responses. The median difference between the responses is only 6 percentage points, and the rank orders of the percentages of responses are nearly identical beyond the ii sites. Highlights include that most students reported using self-testing (Question 6) as a metacognitive tool to evaluate their progress and not as a means to boost performance, although self-testing can serve this dual purpose. Also, most students reported scheduling exercise past focusing on whatever was due the soonest (Question two), although private differences in scheduling did arise, with some students (13%) developing plans on how to schedule fourth dimension. 2 apparent differences betwixt the sites are that more than students in the present study endorsed that they apply self-testing because they acquire more (Question 6), and more claimed that a instructor taught them how to study (Question 1). Given the few differences across sites, however, we caution against whatever estimation of these credible differences and instead emphasize the consistency in student responding.

Results from the new questions (viii–10) provided further information about how students schedule study. Concerning time-of-day schedules, virtually students reported studying in the evening (and twenty% report studying late at night), and fewer than 15% reported studying during the afternoon or before, even though 42% of the students indicated that studying was about effective in the afternoon or morning. More important, students were about evenly divide (Question 10) with respect to whether they reported spacing or massing their studying. Those who reported massing their study as well were more than likely to report cramming (Question 12)—gamma correlation = .75—which provides a cross-validation of responses to these related questions.

Finally, as is evident from responses to students' regular use of strategies (Question 12), substantial individual differences occurred in reported strategy utilize. For example, many—only non all—students reported self-testing (71%) or rereading (66%) during study, which was consistent with the popularity of these strategies found by Karpicke et al. (2009). Other strategies, such as request questions or verbally participating during class, were endorsed less often and might indicate neglect of valuable strategies, if such strategies are indeed associated with higher achievement. Thus, nosotros at present turn to our primary question: Were any of these individual differences in strategy use or scheduling related to GPA?

Relationship betwixt GPA and strategy use

To evaluate whether diverse strategies from the checklist (Question 12) were related to GPA, we computed a Goodman–Kruskal gamma correlation between whether students endorsed using a given strategy (0, 1) and their reported GPA (where each was assigned the midpoint grade within the category chosen in Question 11).

GPA and Self-testing

As is reported in Fig. i, self-testing was best reflected past two strategies in Question 12: "test yourself with questions or practice issues" and "apply flashcards." Endorsing the general strategy of testing oneself was significantly related to GPA (gamma = .28, p = .001), whereas using flashcards was not (gamma = −.03, p = .76). This discrepancy between self-testing and flashcards was unexpected and will be addressed in the Discussion section.

Both Kornell and Bjork (2007) and the nowadays survey institute that students reported using cocky-testing for different ways (Question 6). Are these differences in how self-testing is used related to student accomplishment? Ane possibility is that students who endorse using self-testing as a metacognitive tool (vs. a learning strategy) benefit almost from its apply, considering the metacognitive feedback from cocky-testing is presumably used to allocate further report time. Although possible, responses to why students tested themselves (Question 6) were not related to GPA. As is shown in Fig. 2, the most mutual reason for cocky-testing—endorsed by 50%–60% of students at all GPA levels—was to determine how well information had been learned.

GPA and Scheduling

Relevant to scheduling, nosotros examined the responses from Questions 2 ("How do y'all make up one's mind what to study next"), 8 and ix (time-of-day schedules), 10 (pattern of spacing vs. massing report), and the checklist strategy (Question 12) pertaining to cramming. For the commencement three questions (2, 8, and ix), we plotted the numbers of students endorsing each response pick as a function of GPA. Figure iii includes values for Question ii, and Fig. 4 includes values for Questions viii (left panel) and ix (right panel) pertaining to time-of-day scheduling. Figure 3 shows that although many students are heavily influenced by impending deadlines (what's due soonest/overdue) when deciding what to report next, this scheduling practice is peculiarly true for low performers. By dissimilarity, although relatively few students overall (13%) schedule their study ahead of time, high performers are more than likely to do so. Regarding time of solar day, Fig. iv shows similar rates of behavior about effective report times for students at all GPAs (correct console), but somewhat different patterns of actual studying (left panel) in which the everyman performers are most probable to engage in late-night studying.

Regarding how study was scheduled (Questions x and 12 well-nigh cramming), we correlated the students' responses to these questions with GPA. Although the correlations were in the expected direction, quite surprisingly, they were non statistically significant for either Question 10 (about spacing study: gamma = .12, p = .fifteen) or 12 (virtually cramming: gamma = −.16, p = .08). Thus, students who report scheduling practise across sessions (which should be the more than effective strategy) did non appear to reap a clear benefit in terms of GPA.

Regression analysis for strategies in Question 12

The strategies from the checklist in Question 12 were just weakly intercorrelated (rsouth ranged from −.23 to .24), and an exploratory factor analysis did not yield any reliable or interpretable underlying factors. Thus, each strategy was treated as a dissever variable rather than existence combined with others. Given that the response format was identical for all strategies in Question 12, we evaluated their contributions to GPA past conducting a single regression analysis, which would hold the familywise error charge per unit to .05. The regression analysis was consistent with the conclusions drawn above. The significant standardized regression coefficients were as follows: test yourself (β = .18, p = .003), reread chapters and notes (β = .12, p = .035), make outlines (β = −.12, p = .045,) study with friends (β = −.11, p = .044), and, budgeted significance, cramming (β = −.11, p = .064). None of the remaining strategies significantly predicted GPA.

Discussion

Data from this survey replicated the major outcomes of Kornell and Bjork (2007) and Karpicke et al. (2009): College students reported using a cocky-testing strategy (Fig. 2) largely for monitoring their learning progress, and likewise reported the use of a variety of other strategies, such every bit rereading and not studying what they already know. Consistent with expectations from SRL theory, the present study also revealed that some of these strategies are related to higher students' GPAs. Perhaps most impressively, the use of a self-testing strategy—which boosts functioning when administered by an experimenter or teacher (Roediger & Butler, 2011)—is also related to student success when used spontaneously for academic learning. As is shown in Fig. 1, almost all of the most successful students (GPA > three.6) reported using this strategy, and its reported utilize declined with GPA.

A major issue is the degree to which these benefits of self-testing will generalize to different kinds of tests (e.one thousand., multiple selection, costless recall, or essay), different course contents (e.thousand., biological science, psychology, or philosophy), students with differing abilities, and so forth. Current evidence suggests that self-testing has widespread benefits beyond different kinds of tests, materials, and student abilities. For example, self-testing by recalling the target information boosts performance on subsequent think and multiple-choice tests of the target data, and it too boosts operation on tests of comprehension (for reviews, encounter Roediger & Butler, 2011; Roediger & Karpicke, 2006; and Tabular array S1 from Rawson & Dunlosky, 2011). Nevertheless, information technology undoubtedly volition not be useful for some courses, and if so, our present results may underestimate the power of self-testing, considering the blended GPA would reflect courses in which testing would (and would not) matter. On the basis of this rationale and the positive testify from the nowadays study, hereafter inquiry should examine self-testing and grades for specific classes that vary in the degrees to which they afford cocky-testing every bit a potentially effective strategy.

Reported use of rereading was also related to GPA, which might be viewed as surprising, given that rereading does not e'er amend performance in the laboratory (e.g., Callender & McDaniel, 2009). When used correctly, notwithstanding, information technology can heave retention and performance (e.yard., Rawson & Kintsch, 2005), and the present rereading–GPA human relationship may in role ascend from students who read (a lot) versus those who do not read. In dissimilarity to rereading, other reported strategies that presumably are effective did non predict GPA. In particular, the reported use of outlines and collaborative learning demonstrated slightly negative relationships, and the use of diagrams and highlighting were non significantly related to GPA. These outcomes are provocative, considering many students believe that these strategies are beneficial when in fact they volition not always boost learning. For case, although studying with friends may have some benefits, students may non e'er collaborate appropriately when studying together. Also, highlighting past a textbook publisher or instructor can ameliorate performance, just students' apply of highlighting has been shown to yield mixed results, depending on the skill of the user (east.m., Bell & Limber, 2010; Fowler & Barker, 1974). Thus, at least some of these strategies may actually be relatively inert when used past typical students. Based on the present report, however, it would be premature to conclude that these strategies concur admittedly no benefits for student success, because the survey did not measure how often a given student used each strategy and how well the strategies were used. Even self-testing (which was related to GPA) tin be used ineffectively, such equally when students test themselves by evaluating their familiarity with a concept without trying to recall information technology from retentivity (cf. Dunlosky, Rawson, & Middleton, 2005). An exciting avenue for future research volition be to develop methods that allow researchers to describe students' report behavior at a more fine-grained level, such as how often they apply self-testing and exactly how they use information technology to monitor learning.

Although cocky-testing predicted GPA, the use of flashcards—a pop grade of self-testing—unexpectedly did non. In fact, these two strategies were unrelated in the present written report (r = .02, p = .68) and might be perceived as different by students. Among students who reported regular apply of flashcards, approximately xxx% did not report self-testing, which suggests that many flashcard users do non utilise them to self-test. Flashcards may ofttimes be used nonoptimally in vivo, such as when students mindlessly read flashcards without generating responses. Even when they are used appropriately, flashcards may exist best suited to committing factual information to memory and not as effective for studying all types of materials. In contrast, self-testing could also include answering complex questions or solving practice problems, which might encourage deeper processing and yield larger payoffs in performance beyond many types of materials and courses.

Even those strategies that best predicted GPA were only weakly predictive, which might propose that students' strategy choices have footling consequence for their grades. Are other factors—such every bit motivation, interest, intelligence, environment, or competing demands—only more than important? Although possible, several reasons exist for why the correlations observed in the nowadays written report are expected to be small, fifty-fifty if some strategies are effective over a broad range of students, tests, and content (e.g., cocky-testing; Roediger & Butler, 2011). Starting time, unlike students might have had different courses in mind (eastward.chiliad., calculus vs. philosophy) when responding to the survey, which would create variability in responding and could obscure strategy–GPA correlations. Future enquiry might overcome this limitation with test–retest methods, longitudinal follow-ups, or more context-specific questions. Second, whatever strategy could be used well or poorly. This variability in how well strategies are used would obscure how valuable they might exist if used ideally. And, tertiary, the present survey asked students to report whether they did or did not use a given strategy regularly (binary responses), rather than how much or how often a strategy was used. Time to come research will do good from measuring the degree of usage (a continuous response scale), which might enhance the ability of written report strategies to business relationship for variance in performance.

A unique attribute of the present study was the investigation of students' fourth dimension management. Differences in scheduling did arise between the highest and lowest achievers, with the lower achievers focusing (a) more than on impending deadlines, (b) more on studying tardily at dark, and (c) almost never on planning their study time. Reports of spacing report (vs. cramming) were not significantly related to GPA, fifty-fifty though spaced (vs. massed) practice is known to accept a major impact on retentivity (Cepeda et al., 2006). Although this outcome is surprising, cramming the nighttime (and immediately) before an exam might support relatively good exam performance, even though students who use this strategy might think little of the content even a short time later on the exam. Furthermore, scheduling study sessions in a spaced manner may afford the employ of other strategies, which themselves better educatee success. Although these ideas are speculative, post-hoc analyses indicated that the reported utilise of spacing (vs. cramming) was significantly related to the use of more study strategies overall (r = .15, p < .009; combined Question 12 reports) and, in particular, was related to the use of cocky-testing (r = .xi, p = .05) and rereading (r = .15, p = .007). These relationships are small, but they do advise that spacing may support the use of more constructive strategies.

In summary, low performers were especially likely to base their study decisions on impending deadlines rather than planning, and they were also more than likely to appoint in late-night studying. Although spacing (vs. massing) study was non significantly related to GPA, spacing was associated with the use of more study strategies overall. Finally, and perhaps most importantly, cocky-testing was a relatively pop strategy and was significantly related to pupil achievement.

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Writer note

Many thanks to Katherine Rawson for comments on an earlier version of the manuscript. This inquiry was supported by a James South. McDonnell Foundation 21st Century Scientific discipline Initiative in Bridging Encephalon, Mind and Behavior Collaborative Award.

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Psychology Department, Kent Country University, Kent, OH, 44242, U.s.a.

Marissa M. Hartwig & John Dunlosky

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Correspondence to Marissa M. Hartwig.

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Hartwig, Grand.One thousand., Dunlosky, J. Written report strategies of higher students: Are self-testing and scheduling related to achievement?. Psychon Bull Rev 19, 126–134 (2012). https://doi.org/10.3758/s13423-011-0181-y

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Published: 15 Nov 2011
Effect Date: February 2012
DOI : https://doi.org/10.3758/s13423-011-0181-y

Keywords

Testing
Metamemory
Strategy use

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