Intended and Actual Changes in Study Behaviors in an Introductory and Upper-Level Psychology Course

Abstract

We examined self-directed studying of students in an introductory (Study 1) and upper-level (Study 2) psychology course. Students reported their study behaviors for Exam 1 and 2, and wrote Exam 2 study plans. In both studies, students planned to and ultimately did use more active strategies for Exam 2 than Exam 1. However, they struggled to follow through on plans to space studying over time. In Study 1, we also found that greater use of active strategies (e.g., retrieval practice) was associated with higher exam scores when controlling for factors such as study time. Our findings highlight that students across course levels are interested in changing their study behaviors and we note implications for instructors.

Keywords

study strategy study habit study plan retrieval practice performance exam

Considerable evidence from psychological research shows that students learn more when using certain study strategies. One way to differentiate strategies is using the “desirable difficulties” framework (Bjork, 1994), which distinguishes between strategies that feel more difficult during study but lead to better long-term learning (i.e., “active”) and strategies that feel easier during study but lead to worse long-term learning (i.e., “passive”). In the present research, we examined the extent to which college students planned to use and actually used active strategies in two psychology courses (an introductory and an upper-level course). We also examined whether active strategy use was related to exam performance.

Active strategies, including retrieval practice, elaborative interrogation, and summarization, have greater evidence of effectiveness than passive strategies, including rereading material and copying information verbatim. Retrieval practice includes a variety of strategies that require retrieval of information from memory, such as flashcards, answering practice questions, and describing concepts from memory. The benefits of retrieval practice over restudying have been shown in a large literature including studies in both laboratory and classroom contexts (e.g., Roediger & Karpicke, 2006; Trumbo et al., 2016; for a review, see Roediger & Butler, 2011). Elaborative interrogation includes asking “why” and “how” questions as well as thinking about similarities and differences among concepts. This strategy has considerable evidence from laboratory research and some from classroom contexts (e.g., Pressley et al., 1987; Smith et al., 2010; for a review, see Dunlosky et al., 2013). Summarization involves identifying and organizing key points from larger amounts of material. There is less research on this strategy, but there is evidence that it is more effective than rereading or copying notes verbatim, at least if used correctly (for a review, see Dunlosky et al., 2013).

Additionally, aspects of students’ studying context, such as study time allocation and distraction, are important for learning. For example, spacing out the same amount of study or instructional time across separate sessions, rather than massing it closely together, leads to better long-term retention (e.g., Budé et al., 2011; Rawson & Kintsch, 2005; for a review, see Janiszewski et al., 2003) and students who text and use Facebook more while studying have lower GPAs when controlling for other related factors (Junco & Cotten, 2012; for a review, see May & Elder, 2018). In this paper, we refer to the active and passive study strategies described previously and contextual factors of study time and distraction together as “study behaviors.” We examined students’ study behaviors outside of the classroom during their own self-directed study time.

While college students are aware of some evidence-based study behaviors, they also endorse less effective behaviors: In a survey of college students across many course domains, students at one university rated using flashcards and creating examples as some of the most effective strategies, but they rated highlighting notes and copying notes as even more effective (Blasiman et al., 2017). Moreover, students tend to study in ways that they know are less effective (Morehead et al., 2016; Susser & McCabe, 2013; Wissman et al., 2012). For example, when rating hypothetical studying scenarios, 69% of students said that spaced study was better than massed study, but at the same time 53% said they regularly cram the night before a test (Morehead et al., 2016).

Research in introductory courses in other STEM fields has shown that many students would like to change at least some of their study behaviors (Cook et al., 2013; Gezer-Templeton et al., 2017; Lovett, 2013; Sebesta & Speth, 2017; Stanton et al., 2015). However, to our knowledge, studies have not specifically examined this in psychology courses. Additionally, although students may plan to make changes, this intention may not translate into actual changes in study behaviors. In fact, previous research in other STEM courses has not shown much change between the first and second exams in a course even though students wanted to make changes (Gezer-Templeton et al., 2017; Sebesta & Speth, 2017). Research has also found that students report using a wide range of strategies less often throughout the semester than they had intended at the beginning, and this gap is particularly large for active strategies compared to passive strategies (Blasiman et al., 2017). In the present studies, our primary goal was to examine which specific self-directed study behaviors students in an introductory (Study 1) and an upper-level (Study 2) psychology course intended to change for their next exam and whether they actually made changes.

A secondary goal was to further study the association between study strategies and exam performance. Much prior evidence for effective study strategies either comes from laboratory studies or classroom studies where the strategies were built into the class structure (e.g., weekly quizzes, Trumbo et al., 2016; for a review, see Dunlosky et al., 2013). Although these studies provide the controls needed to establish causality, they do not show whether effects would generalize to self-directed study in actual classes. If instructors intend to recommend that students use particular strategies during their self-directed study time, it is important for research to demonstrate a relationship with course performance when the strategies are used in that context.

Research suggests there is a relationship between study behaviors and GPA. However, in some studies the measures of study behaviors are not specific enough to separate study strategies from study context (e.g., spaced practice) or do not include all strategies that students use (Credé & Kuncel, 2008; Credé & Phillips, 2011). Even when more specific study behaviors are measured (Hartwig & Dunlosky, 2012; Morehead et al., 2016), using GPA as the outcome is problematic because students may use different strategies across different courses and final course grades contain components that may be less related to study behaviors (e.g., participation).

Looking more closely at performance in individual courses, there is evidence of small correlations between greater self-reported use of practice testing during self-directed study time and higher performance on multiple-choice exams (Bartoszewski & Gurung, 2015; Gurung et al., 2012). However, there are inconsistent findings regarding relations of other active and passive strategies to exam performance (Bartoszewski & Gurung, 2015; Gurung et al., 2012). Importantly, in these studies, students only reported on their study strategies once at the end of the semester. Students may change their strategies during the semester and so there is a need to research the relationship between performance on an assessment and study strategies reported specifically for that assessment.

The Present Research

In the present studies we addressed three research questions (RQs) in an introductory course (Study 1). We also extended two of these questions to an upper-level psychology course (Study 2).

RQ 1. What types of self-directed study behaviors do college students intend to maintain and change between Exam 1 and Exam 2? Consistent with prior research in other STEM fields, we expected to find that most students would be interested in making changes (e.g., Sebesta & Speth, 2017), but had no specific hypothesis about which behaviors students would be most interested in changing.

RQ 2. Which behaviors do students actually change between Exam 1 and Exam 2? In contrast to prior research in other STEM courses (Gezer-Templeton et al., 2017; Sebesta & Speth, 2017), we did expect to find changes in students’ study strategies by using more sensitive measures. Previous studies either used an open-ended question or used a broader survey that did not focus on all likely strategies. We used a list of specific strategies, and examined which strategies students selected and which they reported spending the most time on.

RQ 3. What is the relationship between the self-directed study strategies used for a specific assessment and performance on that assessment in introductory psychology (Study 1 only)? We predicted that students who spent most of their study time for an exam using active strategies would perform better than students who mainly used passive strategies. To strengthen our conclusions, we statistically controlled for potential confounds using ACT score, previous exam score, and study time.

Study 1

Method

Participants

Participants were students from two sections of a large, face-to-face, introductory psychology course during the same semester (∼500 students) at a selective research university. Three instructors co-taught the course, with each instructor teaching both sections for five consecutive weeks. Demographic information about ACT scores, year in school, sex, and race/ethnicity were gathered from the university registrar’s office. Only data from students who signed informed consent forms authorizing use of their data were included in the study.

Out of 497 total students in the class, 449 consented as part of a broader project on STEM education at the university. We report on results from 54.3% of those students (N = 244) who completed all components of the study. Compared to the other consented students in the course, students in this sample performed better on Exam 1 and there were proportionally more women, more first-year students, more for-credit students, and fewer underrepresented racial minority students (see Supplemental Materials for further detail).

Materials and Procedure

Participants completed a reflection survey after Exam 1, an exam-planning exercise before Exam 2, and a second reflection survey after Exam 2. Students voluntarily participated in the surveys and exercise as part of course activities and did not receive any credit.

Exams

There were three non-cumulative exams, one at the end of each instructor’s course segment. There was an optional cumulative final exam that could replace the score of one of the previous exams. All exams were composed of 50 multiple-choice questions. The course did not have any other assessments (e.g., homework, quizzes). We report on Exam 1 (Research Methods, Biology and Behavior, Consciousness, and Sensation and Perception; Cronbach’s α = .80) and Exam 2 (Memory; Learning; Thinking, Language, and Intelligence; Motivation and Emotions; and Development; Cronbach’s α = .77).

Exam 1 Reflection Survey

Four days after Exam 1, when the exam grades were released, students were invited to complete an online survey about how they prepared for the exam. Students had 4 days to complete the survey. The survey had questions about both study context and study strategies. Students reported how many days in advance they started studying for the exam, how much total time they studied, and how often they were distracted while studying (1-Never to 5-Always). Then they were asked to select all the strategies they used to prepare for the exam as well as their two most-used strategies from the following list: Reading textbook modules, reading your class notes, reading the lecture slides, rewriting your class notes word for word, paraphrasing or outlining your class notes, answering review questions in the textbook, quizzing yourself or having someone else quiz you, explaining concepts to yourself or others, and other. The survey also included questions (not reported here) about attendance and student satisfaction with their grade.

Exam-Planning Exercise

One week before Exam 2, students had the opportunity to complete an exam-planning exercise for about 10 minutes in class. The exercise included prompts about habits to maintain, habits to change, and making a specific study plan. While students completed the exercise, the study strategies from the first reflection survey and a list of suggested study strategies were projected on a screen. These suggestions came from a 10-minute lecture the instructor gave on a previous class day and included avoiding re-reading, verbatim note taking, and cramming, and instead using retrieval practice, spacing, teaching-to-learn, and synthesizing notes.

Exam 2 Reflection Survey

One week after Exam 2, students had 3 days to complete another online reflection survey with the same questions as the Exam 1 survey.

Coding

The first two prompts of the exam-planning exercise about habits to maintain and change were coded to get students’ intended study behaviors. Table 1 shows the codes, which were developed using the study strategies from the reflection survey and by identifying common responses. All responses were coded by the first author and an undergraduate assistant trained using a subset of responses. Students could write more than one idea per prompt, thus each response could have more than one code. The responses that were not used for training (n = 222) were used to calculate interrater reliability (see Table 1; Maintain: Cohen’s κ = .77 to .95, Change: Cohen’s κ = .66 to 1.0). Coding discrepancies were resolved through reexamination by the first author.

Table 1.

Study 1: Planned Study Behaviors of Introductory Psychology Students.

Behavior	Description	% of Students		κ Reliability
Study Strategies		Maintain	Change	Maintain	Change
Active
Quizzing	Any form of retrieval practice by oneself or with others, e.g. flashcards (Change ↑)	43.3	19.3	.94	.92
Paraphrasing or outlining class notes	Any organization of materials that was not verbatim, e.g. paraphrase notes, combine slides and notes, make study guide, make concept maps (Change ↑)	18.3	2.6	.91	1.0
Explaining concepts to self or others	Explain, teach, or discuss concepts with themselves or others (Change ↑)	14.6	4.3	.92	.87
Passive
Reading, rereading, or watching videos	Read, reread, or review notes, slides, the textbook, or websites; watch online videos (Change ↓)	32.9	16.3	.88	.89
Rewriting class notes word for word	Copy notes verbatim, e.g. make them neater (Change ↓)	8.8	4.3	.78	1.0
Other strategy changes	Change only = All other strategy changes, e.g. increase passive strategy, decrease active strategy, increase active strategy not included in other codes	—	10.3	—	.69
Study Context
Total study time	Study same amount of time as exam 1 (Change ↑)	17.5	12.0	.88	.66
Spacing or starting early	Start studying in advance; distribute studying over multiple sessions (Change ↑)	25.4	33.9	.87	.87
Limiting distractions or changing environment	Limit distractions; a related strategy (e.g., study in the library, turn phone on silent) (Change ↑)	24.6	30.5	.95	.95
Studying with others	Study with peers (in general); specific strategies involving peers (e.g., quizzing each other) were also coded in addition to their primary code (Change ↑)	22.1	5.6	.77	.79

Note. A student could have listed more than one behavior that they wanted to maintain or change, and occasionally students listed the same behavior for both. Some students did not answer one of the questions (Maintain = 4, Change = 11) and the results show the percentage out of students who answered. κ = Cohen’s kappa interrater reliability estimate. The description column shows the code description for responses to the Maintain question and in parentheses indicates whether responses to the Change question were coded for starting, increasing, or improving the corresponding study behavior (indicated by “Change ↑”) or were coded for decreasing or stopping a behavior (indicated by “Change ↓”).

Results

RQ 1: Exam planning. Table 1 shows which study behaviors students intended to use. The most common behavior that students wanted to maintain was quizzing, an active strategy recommended by the instructor. The second most common was rereading, a passive strategy not endorsed by the instructor. However, some students instead said that their change would be to spend less time rereading. The most commonly planned changes were to students’ study contexts—to increase spacing and to limit distractions. Although these contextual factors were individually more common than any one study strategy, combining across strategies, 46.4% of students mentioned at least one strategy-related change, 43.8% mentioned at least one timing-related change, and 30.5% mentioned limiting distractions.

RQ 2: Study context. Our second question asked how study behaviors changed for Exam 2, including the study context. The amount of time did not change: Students studied for both exams on average between 4 and 5 days in advance (Exam 1: M = 4.69, SD = 3.64; Exam 2: M = 4.85, SD = 3.02) and spent a total of about 9.5 hours studying (Exam 1: M = 9.52, SD = 7.05; Exam 2: M = 9.41, SD = 6.44), though there was considerable variability in both measures of study time. There was no significant difference between Exam 1 and 2 for either measure, t(243) = 0.86, p = .391, d = .06 and t(243) = 0.31, p = .758, d = .02, respectively.

The majority of students reported being “never” or “rarely” distracted while studying (Exam 1 = 53.2%, Exam 2 = 63.5%). A third reported being distracted “about half the time” (Exam 1 = 39.3%, Exam 2 = 32.4%). A small percentage reported “usually” or “always” (Exam 1 = 7.4%, Exam 2 = 4.0%). A Wilcoxon signed rank test showed a significant difference between Exam 1 and Exam 2, p < .001. While 53.7% of students reported no change in distraction level, about a third reported less distraction on Exam 2 (30.0%) and only 16.4% reported more distraction.

RQ 2: Study strategies. Next, we examined students’ study strategies, categorizing each strategy as either passive or active. Passive strategies included reading course materials (slides, notes, or textbook) and rewriting notes verbatim. Active strategies were paraphrasing or outlining notes, answering textbook questions, quizzing, and explaining. The review questions and quizzing strategies are both a type of retrieval practice and the explaining strategy is a type of elaborative interrogation. For students who selected “other,” when possible, their response was recoded into one of the existing response options (e.g., “flashcards” = quizzing). Eight students were excluded from all remaining analyses because they reported more than two strategies as their “top two” strategies.

As shown in Figure 1 (top panel), the most common study strategy was reading class notes, which was reported by almost all students for both exams. However, for both exams, about 92% of students reported using at least one active strategy. Individually, all the active strategies were reported by more than half of the students, except for answering textbook questions. We used McNemar’s tests to compare strategy use between the two exams; significant results are indicated in Figure 1 and details of the tests are provided in the Supplemental Materials. More students reported paraphrasing or outlining notes on Exam 2 than Exam 1. However, the proportion of students who reported using quizzing did not change, which may reflect the already high proportion of students using this strategy. Fewer students reported explaining concepts for Exam 2 compared to Exam 1, although a large proportion of the class still reported this strategy.

Figure 1.

All strategies (top panel) and most-used strategies (bottom panel) for Exam 1 and Exam 2 in the Introductory Psychology course (Study 1). *p < .05.

We were most interested in whether students devoted more time to active strategies for Exam 2. Figure 1 (bottom panel) shows the proportion of students who reported each study strategy in the top two strategies that they spent the most time on. More students reported quizzing and explaining concepts in their top two strategies for Exam 2 compared to Exam 1. Thus, while there were not more students using quizzing or explaining for Exam 2, students who did use these strategies reported spending more of their time on them. The proportion of students who reported paraphrasing or outlining notes in their top two strategies was not significantly different between Exam 1 and Exam 2.

RQ 3: Exam performance. Last, we addressed our third research question: Do students who use active study strategies score higher on exams? Performance on Exam 2 was measured as the percentage that a student answered correctly out of 50 questions (possible score range: 0–100). Students were grouped based on their top two strategies for Exam 2: Two passive strategies (N = 50), one passive and one active strategy (“mixed,” N = 94), or two active strategies (N = 76). Additional predictors included ACT score, performance on Exam 1, and study time for Exam 2. Inter-correlations of the predictors and zero-order correlations with Exam 2 performance are reported in the Supplemental Materials. Table 2 shows the results of the regression analysis. When controlling for the other predictors, students in the active group performed a few percentage points better on Exam 2 than students in the passive group, p = .003.

Table 2.

Study 1: Predictors of Exam 2 Performance for Introductory Psychology Students.

	Base Model				Strategies Model
	b	95% CI	β	p	b	95% CI	β	p
(Intercept)	29.93	15.59–44.28	0	<.001	27.49	13.25–41.74	0	<.001
ACT Composite	0.56	0.14–0.98	0.16	.009	0.61	0.19–1.02	0.17	.004
Exam 1 score	0.44	0.33–0.55	0.49	<.001	0.44	0.33–0.55	0.49	<.001
Exam 2: Study time	0.20	0.09–0.31	0.20	<.001	0.17	0.06–0.28	0.17	.004
Exam 2: Top strategy group
mixed					0.76	−1.05–2.58	—	.408
active					2.90	0.98–4.81	—	.003
N = 220
R²/adjusted R²				.309 / .299				.342/.327
Δ R²/adjusted R²								.033/.028F(2, 214) = 5.42, p = .005

Note. The top strategy groups were: active = two active strategies (N = 76), mixed = one active and one passive strategy (N = 94), passive = 2 passive strategies (N = 50). The passive strategies group was the reference level in the analysis. Exam 1 and 2 scores are on a scale of 0–100 representing percentage of questions correct. The analysis excluded students who reported more than two strategies in response to the question about their top two strategies (N = 8) or who did not have ACT scores available (N = 16).

Discussion

We found that almost half of the introductory psychology students mentioned wanting to change something about their study strategies, which is similar to findings from previous research about students’ study plans in an introductory biology course (Stanton et al., 2015). The most common study strategy that students planned to change was to start or increase their use of quizzing, which is a strategy with a large evidence base for its effectiveness (Roediger & Butler, 2011). While many students intended to maintain or increase their use of active strategies, students also intended to continue using passive strategies such as rereading even though their instructor had suggested they avoid such strategies.

Examining students’ actual studying, most students were already using at least one active strategy for Exam 1. Moreover, students increased their use of active strategies, such as quizzing, for Exam 2. To our knowledge, this is the first demonstration of changes in strategies within a course. Previous research has either focused on students’ retrospective perceptions of change (Stanton et al., 2015) or has measured study behaviors across the semester and found few changes (Gezer-Templeton et al., 2017; Sebesta & Speth, 2017). For example, the only change that Sebesta and Speth (2017) found in an introductory biology course was an increased use of practice exams. However, they measured study behaviors more broadly (e.g., goal-setting, asking for help) and did not include all likely strategies. In the present study, we asked students to select specific strategies from a list and to indicate which strategies they spent the most time on when studying. Importantly, even though the overall proportion of students using certain active strategies (quizzes and explanation) stayed about the same or decreased slightly, more students reported these as their most-used strategies for Exam 2. Thus, this more sensitive measure of strategy use may have helped us find changes where previous studies did not.

Conversely, although students intended to make changes to their study context, they did not necessarily do so. There was no increase in how far in advance students started studying for Exam 2. We did find that some students reported less distraction for Exam 2. However, we are cautious in interpreting this finding given that it relied on a retrospective rating and we did not specify what constituted distraction. Considering that students reported low levels of distraction overall, it is possible that their estimates were biased or that they were not counting all possible distractions, such as multitasking.

We found an association between greater use of active strategies and higher performance on Exam 2 when controlling for ACT score, Exam 1 performance, and study time. Though this effect was small, it shows that even when accounting for some potential confounding factors, the types of strategies that students use are related to exam performance. This agrees with previous research that has found that using active study strategies such as retrieval practice, elaborative interrogation, and summarization enhances learning compared to using more passive study strategies such as rereading (Callender & McDaniel, 2009; McDaniel et al., 2007; Pressley et al., 1987; for reviews, see Dunlosky et al., 2013; Roediger & Butler, 2011).

Study 2

Study 1 showed that introductory psychology students were interested in changing their study behaviors and that they did, in fact, make some changes between Exam 1 and Exam 2. However, a limitation to the generalizability of those findings was that the students were primarily first years and sophomores. In Study 2, we examined study behaviors in an upper-level psychology course with mostly juniors and seniors. This is potentially important for instructors of upper-level courses because it could help inform whether and how to implement interventions aimed at changing their students’ study behaviors. Prior research on students’ plans to change and on their actual study behaviors has mainly focused on introductory psychology and other introductory STEM courses (e.g., Blasiman et al., 2017; Morehead et al., 2016; Sebesta & Speth, 2017; Stanton et al., 2015). A few studies have examined upper-level student’s evaluations of their study behaviors (Dye & Stanton, 2017; Stanton et al., 2019) and intentions to use more effective behaviors (Bugg et al., 2008; Susser & McCabe, 2013). For example, after psychology research methods students participated in a memory demonstration, the majority of them said they would try using the demonstrated strategy when studying (Bugg et al., 2008). However, this study did not examine whether the students actually changed their study behaviors. Our aim was to see whether students in an upper-level psychology course, like students in introductory psychology in Study 1, wanted to and were able to make changes to their study behaviors during the semester, or whether their behaviors were more fixed.

Method

Participants

Participants were students in a face-to-face, upper-level personality psychology course. Demographic information was obtained from the university registrar. Only data from students who signed informed consent forms authorizing the use of their data were included in the study. Out of 82 total students in the class, 69 consented as part of a broader project on STEM education. We report on results from 78.3% of the consented students (N = 54) who completed all parts of the study. There were smaller proportions of men and underrepresented racial minority students in this sample compared to other students who consented but did not have complete data (see Supplemental Materials for further detail).

Materials and Procedure

The upper-level course followed a modified version of the procedure from Study 1, as explained below.

Exam Reflections and Exam-Planning Exercise

Seventeen days after Exam 1, both the Exam 1 reflection survey and the exam-planning exercise for Exam 2 were released. Students had until 11 days before Exam 2 (1 week) to complete the assignment for extra credit. The reflection survey included the questions from Study 1 and a few additional questions not reported on here. We added two more active strategies based on advice that the instructor gave to students (“comparing or contrasting different concepts” as another type of elaborative interrogation and “coming up with your own examples”) and we removed one strategy that was not relevant (“answering review questions in the textbook”). In addition, the quizzing and explanation strategies were separated into solitary and group versions (e.g., “quizzing oneself” and “quizzed by others”), again based on how the instructor suggested students should study. For the planning exercise, rather than completing a worksheet, the students wrote a one-page essay addressing the question prompts from Study 1. There were two other modifications: First, the instructions mentioned how course content about personality change could relate to the exercise. Second, before completing the exercise, students were asked to read materials about six evidence-based study behaviors (Weinstein et al., n.d.): Spaced practice, retrieval practice, elaboration, interleaving, concrete examples, and dual coding. Nine days after Exam 2, the Exam 2 reflection survey was posted online. It was due in 1 week and included the same questions as the Exam 1 survey.

Exams

The course included three exams that comprised a mixture of multiple-choice and short-answer questions and an optional cumulative final exam that could replace a previous exam score. In addition, the course included weekly writing assignments and a final paper.

Coding

Students’ entire essays were coded using three categories that provided an overview of their plans to change their behavior: 1) studying more or earlier; 2) changing at least one strategy (increasing or decreasing an active or passive strategy); 3) limiting distractions. The general training and coding procedures were the same as Study 1, except that discrepancies were resolved through discussion between the two raters.

Results

RQ 1: Exam planning. We first examined which behaviors students intended to incorporate into their studying from the advice they read. Overall, 81.5% of students mentioned studying more or earlier, 59.3% mentioned at least one strategy change, and 38.9% mentioned a change to limit distraction.

RQ 2: Study context. On average, students started studying more days in advance and spent more total hours studying for Exam 2 (started: M = 4.25, SD = 2.56; total: M = 9.52, SD = 5.40) compared to Exam 1 (started: M = 3.20, SD = 2.41; total: M = 6.35, SD = 3.86), t(53) = 3.21, p = .002, d = .44, and t(53) = 5.71, p < .001, d = .78, respectively. This is consistent with the responses on the exam planning exercise, where many students said they wanted to increase their study time or spacing.

The majority of students reported being “never” or “rarely” distracted (Exam 1 = 61.1%, Exam 2 = 68.5%). Most of the remaining students reported being distracted “about half the time” (Exam 1 = 29.6%, Exam 2 = 29.6%) and a small percentage reported being distracted “usually” or “always” (Exam 1 = 9.3%, Exam 2 = 1.9%). Most students’ distraction levels did not change between Exam 1 and Exam 2 (59.3%), about a quarter decreased (25.9%), and a smaller proportion increased (14.8%). A Wilcoxon signed rank test showed no significant difference between distraction while studying for Exam 1 and Exam 2, p = .268.

RQ 2: Study strategies. As in Study 1, we categorized each strategy as either passive or active. Two students were excluded from the analyses in this section because they reported more than two strategies in their “top two.” To be consistent with Study 1, we combined the solitary and group versions of the quizzing and explaining strategies.

As shown in Figure 2 (top panel), the most common study strategy was reading class notes, followed closely by reading the slides. For Exam 1, 82.7% of students reported using at least one active study strategy and this increased to 98.1% for Exam 2. We used McNemar’s tests to compare strategy use between the two exams; significant results are indicated in Figure 2 and details of the tests are provided in the Supplemental Materials. More students reported using quizzing and explaining for Exam 2 compared to Exam 1. Additionally, more students reported that they compared/contrasted concepts for Exam 2. The use of paraphrasing or outlining notes and generating examples were not different between Exam 1 and Exam 2.

Figure 2.

All strategies (top panel) and most-used strategies (bottom panel) for Exam 1 and Exam 2 for the upper-level psychology course (Study 2). *p < .05.

Next, we examined whether students were devoting more of their study time to active strategies. Figure 2 (bottom panel) shows the proportion of students who reported each study strategy in the top two strategies that they spent the most time on. More students reported quizzing in their top two strategies for Exam 2 compared to Exam 1. Thus, there were both more students using quizzing for Exam 2 and students spent a larger proportion of their time on this strategy. Individually, few students selected any of the other active strategies in their top two.

Discussion

Between Exam 1 and Exam 2, more of the students in the upper-level course reported using quizzing (i.e., retrieval practice) and two forms of elaboration (explanation and compare/contrast). Moreover, more students reported quizzing in their top two most-used strategies for Exam 2. Previous research on students’ intended and actual study behavior changes has focused on introductory STEM courses (e.g., Cook et al., 2013; Gezer-Templeton et al., 2017; Lovett, 2013; Sebesta & Speth, 2017; Stanton et al., 2015). Our study shows that upper-level students are willing to change their study strategies and therefore may benefit from receiving study-strategy instruction.

Students also intended to make changes to their study context. In fact, studying more or earlier was the most common behavior that they wanted to change. There was an overall increase in the number of days in advance that students reported studying for Exam 2. However, this should be interpreted cautiously because we cannot rule out the possibility that the different timing of the surveys for Exam 1 (17–24 days after) and Exam 2 (9–16 days after) contributed to these findings.

General Discussion

We found that in both an introductory (Study 1) and an upper-level (Study 2) psychology course, for the first exam almost all students reported using at least one active study strategy, but fewer students reported spending the majority of their study time on these strategies. Between the first and second exams, students in the introductory psychology course most commonly wanted to start or increase their use of retrieval practice, space out their studying more, and limit distractions. Ultimately, students in both courses used retrieval practice and other active strategies more for Exam 2. However, changes in spacing and distraction (study context) were less consistent between the two courses. Students may need additional information or planning tools to help with time management and distractions.

A potential limitation to the generalizability of these findings was that the reflection and planning assignments may have influenced students’ study behaviors. Thus, it is possible that we have overestimated the amount of behavior change that would have occurred without the assignments. Future research specifically comparing study strategy interventions with a control group would be valuable. Additionally, in both courses we had fewer low-performing students, men, and underrepresented racial minority students participate in the assignments. It is possible that those students changed more or less than the students who participated. Still, we have shown that students from first years to seniors were willing and able to make changes to their study behaviors and did so in a way that was consistent with advice provided by their instructors.

Prior classroom research in favor of active study strategies has focused on interventions that were built into a course, such as weekly quizzes (e.g., Trumbo et al., 2016), rather than how students spent self-directed study time. We found that students in the introductory psychology course (Study 1) who spent more of their self-directed study time on active strategies had higher exam performance when controlling for some potential confounds. Considering that we also found that students changed their strategies during the semester, our results provide a better indication of the relationship between study strategies and performance than prior research that either correlated general study behaviors with GPA (Credé & Kuncel, 2008; Credé & Phillips, 2011; Hartwig & Dunlosky, 2012; Morehead et al., 2016) or that focused on a specific course but only assessed study strategies once for the semester (Bartoszewski & Gurung, 2015; Gurung et al., 2012).

A limitation to the relationship we found with performance is that we did not measure whether active strategies were implemented well. It is possible, for example, that students who reported using flashcards looked at answers too soon without truly implementing retrieval practice. Second, strategies that we categorized as passive can be effective when used appropriately (for a review, see Miyatsu et al., 2018). For example, students may reread material that they did not initially understand or reread after retrieval practice (Kuhbandner & Emmerdinger, 2019), which could have benefits for learning (McDaniel et al., 2015). Future research that incorporates these nuances may find stronger associations between more active, effortful strategies and performance.

Conclusion and Practical Implications

As psychologists discover more about the best ways for students to learn, it is necessary to also consider how best to encourage students to adopt effective strategies. Given that students across course levels were interested in improving their study behaviors, instructors in introductory and upper-level courses could capitalize on this by providing information about effective studying and opportunities within their courses for students to try new behaviors. Additionally, although students did make some of their desired study behavior changes with only minimal intervention, there was still considerable room for improvement. This was particularly true for increasing spacing of study over time. Instructors might consider providing more structure within their courses to promote spaced study (e.g., weekly quizzes or assignments) and additional resources or assignments to help students plan their study time and follow through.

Supplemental Material

Supplemental_Materials - Intended and Actual Changes in Study Behaviors in an Introductory and Upper-Level Psychology Course

Supplemental_Materials for Intended and Actual Changes in Study Behaviors in an Introductory and Upper-Level Psychology Course by Shaina F. Rowell, Regina F. Frey and Elise M. Walck-Shannon in Teaching of Psychology

Footnotes

Acknowledgments

The authors would like to acknowledge and thank Jan Duchek, Heather Rice, Mitchell Sommers, and Tammy English for allowing data to be collected from their courses.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research and/or authorship of this article: This research was supported by an internal grant named “Transformational Initiative for Education in STEM,” which aimed to foster the adoption of evidence-based teaching practices in science classrooms at Washington University in St. Louis.

Supplemental Material

The supplemental material for this article is available online.

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