Factors that influence general chemistry students’ decision making in study strategies
Pallavi
Nayyar
a,
Betül
Demirdöğen
b and
Scott E.
Lewis
*a
aDepartment of Chemistry, University of South Florida, Tampa, FL, USA. E-mail: slewis@usf.edu
bDepartment of Mathematics and Science Education, Zonguldak Bülent Ecevit University, Kdz. Ereğli, 67300 Zonguldak, Turkey
First published on 24th April 2024
Abstract
This qualitative study delves into the intricate landscape of general chemistry students' study strategy decision-making processes, examining the guiding factors that shape their choices. Past work in chemistry education has shown that students’ study behaviors are dynamic in nature. Employing self-regulation theory, the study aims to provide a deeper understanding of how students decide to maintain or change their study behaviors. Semi-structured interviews were conducted to capture the study processes of nine students enrolled in first-semester general chemistry classroom. The results indicated these students’ study behavior decision-making process was either driven by metacognition or affect. Students who adopted metacognitive decision-making showed evidence of enactment of declarative, procedural, and conditional knowledge which could be influenced by either the nature of the content studied (content-driven), or the time-efficiency of the strategies employed (time-driven) during their self-regulation. On the contrary, students who adopted affective decision-making based their choices regarding their study behaviors on the emotional aspects and the value they attribute to the study strategies (intrinsic-value or instrumental-value driven). The findings of the study are foundational yet highlight the nuanced nature of changes and constancy within the study strategy decision-making process. This suggests a one-size-fits-all approach to improve student study behaviors may not yield fruitful outcomes and therefore, distinct methods should be devised to reach students with different decision-making processes.
Introduction
Introductory chemistry courses have been noted for their high withdrawal rate when compared to other courses (McKinney et al., 2018), which is an important issue considering its function as a gateway for the majority of STEM majors including engineering and life sciences (Harris et al., 2020). Literature has indicated that the nature of the reasons for withdrawal is linked to students’ study behaviors, which are influenced by course structure (e.g., class resources), student personal factors (e.g., time commitment or preparedness), chemistry content (e.g., amount of material), and study strategies (e.g., self-studying) (Guo et al., 2022). Therefore, chemistry education researchers have directed their attention to course specific study habits, their relation to other variables, and methods to improve these specific habits for enhanced outcomes, however, our current understanding of the reasons behind students’ choices of specific study strategies remains limited. More importantly, the literature pointed out the impact of interventions in improving students’ study strategies. Yet, to effectively create targeted interventions, it is crucial to address a gap in the existing literature: understanding the reasons behind why students alter or maintain their study strategies. Therefore, the focus of the current study was to explore guiding factors in students’ study behaviors including the change and constancy in the employed study strategies for exams in introductory general chemistry.Self-regulation theory
Self-regulation has been described as the capability (Zimmerman, 2000) to shape and control personal (e.g., cognition and affect), behavioral (e.g., action), and environmental factors (e.g., context) to attain goals (Bandura, 1986). That is, self-regulation has its strength to delve into the complexity of human behavior through recognizing fundamental factors. In this study, self-regulation theory was used to gain an in-depth understanding about various factors that guide students’ decision-making process in their study behavior, namely, the change and constancy in study strategies.Self-regulation is defined as ‘‘self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals’’ (Zimmerman, 2000, p. 14) and self-regulated individuals are “… metacognitively, motivationally, and behaviorally active participants” in their actions (Zimmerman, 1990, p. 4). Self-regulated individuals set goals for their actions, persist through controlling and monitoring their behaviors to attain their goals, and reflect on their own progress toward self-set goals. Although there have been various models of self-regulation (Pintrich, 2000; Zimmerman, 2000; Boekaerts and Corno, 2005; Efklides, 2011), the models agree upon the aspects and phases of self-regulation although they differ on the emphases of the aspects and sub-processes within phases (Panadero, 2017).
Among various self-regulation models, Zimmerman's model (Zimmerman, 2000) has been evidenced for its applicability in educational settings (Panadero, 2017) since there is a clear distinction among the phases and their subprocesses in the model. Therefore, students’ self-regulation of their study behaviors was examined from Zimmerman's three phases: forethought, performance, and self-reflection (Zimmerman, 2000). The forethought phase includes the subprocesses of goal setting and strategic planning in relation to the action that is regulated by the individual. Hence, students setting goals for studying for the upcoming exam (e.g., understanding the content and improving the grade) and planning how to manage their time using various study strategies (e.g., spacing out study time or going to the tutoring center) constitute subprocesses in the forethought phase when students regulate their study behaviors. In the performance phase, individuals employ the planned strategies to achieve their self-set goals by controlling and monitoring the strategies to persist toward the action. Finally, individuals not only evaluate their performance making causal attributions (i.e., judge) but also through emotional (e.g., satisfaction) and responsive (e.g., adaptive) reactions to their performance in the self-reflection phase. Students’ use of different standards when they evaluate their study behaviors was the significant aspect of self-reflection when exploring the data with the aim of this study.
Personal factors identified in social cognitive theory (Bandura, 1986) determine the aspects that are important in characterizing self-regulation. A review of self-regulation models indicated that metacognition, motivation, and emotions are fundamental aspects (Panadero, 2017). What type of metacognitive knowledge (e.g., declarative, procedural, and conditional, described further in the methods section) and which motivations and emotions (e.g., intrinsic value vs. instrumental value, described further in the results section) are influential in characterizing students’ self-regulation were investigated to determine which major aspects guide students’ self-regulation of their study behaviors. Describing the major aspects evidenced in students’ self-regulation provides insight into the factors guiding students’ decision-making process in their study behavior, a pivotal step prior to advancing how to promote effective study behaviors.
Significance of metacognition
Metacognition can be understood as an internal process that involves knowledge of cognition (declarative, procedural, and conditional knowledge) as well as its regulation (planning, monitoring, and evaluating) (Jacobs and Paris, 1987; Schraw and Dennison, 1994). Metacognition manifests in different ways such as adaptability to changing circumstances, discrimination as a form of wise-judging, self-observation, and regulation (Drigas and Pappas, 2017). Researchers in various fields, including chemistry, have argued that metacognition is important in the development or enhancement of various skills and abilities such as problem solving, posing complex questions, scientific inquiry, meaningful understanding of concepts, self-assessment, and monitoring of learning (Martin et al., 2000; Zion et al., 2005; Cooper et al., 2008; Chiu and Linn, 2012; Herscovitz et al., 2012; Veenman, 2012; Eilam and Reiter, 2014). In addition, students’ metacognitive regulation influences the choice of study behaviors employed during exam preparation (Fakcharoenphol et al., 2015; Stanton et al., 2019) and ultimately their learning outcomes (Thomas and McRobbie, 2001).Task-specific metacognition-based pedagogical interventions have been utilized to enhance specific metacognitive skills (Blank, 2000; Koch, 2001; Georghiades, 2004; Zion et al., 2005; Ben-David and Zohar, 2009) including efforts that relied on self-report surveys to determine the efficacy of the intervention (Sandi-Urena et al., 2011; Chiu and Linn, 2012; Herscovitz et al., 2012; Thomas, 2013). Due to its internal nature, evidencing metacognition in learners is challenging; however, it is plausible by employing multiple methods to collect data (Georghiades, 2004) or by carrying out assessment of the same learner across different times. Quantitative tools to investigate students’ metacognition in specific disciplines are rare (Dori et al., 2018). Although existing tools can be adjusted for application across different disciplines, Thomas and colleagues suggested to create tools specifically designed for individual disciplines (Thomas et al., 2008). This approach considers the need to delve into the complexities of subject-specific learning and the development of distinct metacognitive skills that may not readily transfer across domains (Wang and Chen, 2014). This study therefore examined evidence of metacognition for the same student across multiple time points within the discipline-specific context of general chemistry.
Literature review on study strategies and behavior
Researchers in different disciplines (e.g., chemistry, psychology, and food science) have attempted to understand college students’ studying strategies and behaviors (Gezer-Templeton et al., 2017; Walck-Shannon et al., 2021). It was revealed that researchers used different terms to label what students do: study/learning strategies (Walck-Shannon et al., 2021), study/learning approaches (Zeegers, 2001; Sinapuelas and Stacy, 2015), study skills (Crede and Kuncel, 2008), and study habits (Ye et al., 2015). Although termed differently, existing research in the literature focused on characterizing what students do when they study, features of the strategies used, how those strategies relate to student affect or achievement, and methods to promote effective strategies (Muteti et al., 2021; Gamby and Bauer, 2022; Muteti et al., 2023).Studies with college students revealed that on average, undergraduate students use 2.9 total strategies such as reading a textbook, doing practice problems, or self-testing while studying for the exams (Karpicke et al., 2009). More importantly, students utilizing more active study strategies (that involve generating knowledge) such as self-testing perform better when compared to students utilizing more passive strategies such as watching an online video (Hartwig and Dunlosky, 2012; Rodriguez et al., 2018; Walck-Shannon et al., 2021; Ewell et al., 2023). In terms of type of learning outcome, researchers have revealed that rereading and immediate testing enhance long term retention of the content (Rawson and Kintsch, 2005; Roediger and Karpicke, 2006), and distributed practice improves students’ conceptual understanding of the subject (Budé et al., 2011).
Within the realm of chemistry education research, investigators have concentrated their inquiries into students' study strategies, delineating their focus into three primary categories: the correlation with performance outcomes, the association with affective characteristics, and the influence of interventions. Studies in the first category revealed that students using deeper approaches to learning (Zeegers, 2001; Sinapuelas and Stacy, 2015; Bunce et al., 2017) and are more self-regulated when studying (Miller, 2015) performed better in the course. Moreover, there is a shift in students’ approach to learning from surface to deep over the semester (Sinapuelas and Stacy, 2015) and upper level students reported deeper strategies than beginning college students (Zeegers, 2001). Ye and colleagues (2015) associated the frequency of studying to exam scores by categorizing students as: students that knowingly do not study, students who describe mandatory course components as studying, and students who study in addition to the mandatory course components. They revealed that the use of more frequent study habits was associated with higher scores on the final exam. Based on self-reports, as the semester progressed, they also found that students' study strategies adapted. However the study could not capture the cause of this adaptation due to the quantitative nature of the study. Directing attention towards general chemistry students in the bottom quartile of math SAT scores, Ye and colleagues (2016) found that both frequency and quality are pivotal to their academic performance, but quality of studying is not necessarily linked to frequency of studying. This means the decisions students make on how to study may lead to interventions that can improve student retention and performance in the course more than efforts to increase their frequency of studying.
Switching to affective characteristics, Chan and Bauer (2016) explored the relationship of these characteristics to study strategies. Students were clustered into either a high or low affect group based on emotional satisfaction, intellectual accessibility, chemistry self-concept, math self-concept, self-efficacy, and test anxiety. Students with high affective characteristics reported relying less on tutors and teaching assistants for help when preparing for exams and exhibiting stronger intellectual engagement during the use of strategies. In addition, the high affective students found their study strategies effective while students in the low affective group reported ineffectiveness of their strategies and expressed a willingness to modify their approaches in the future to enhance their performance. The outcomes of each chemistry-specific study describe the evolution or alteration of students' study strategies throughout a semester and relate these strategies to performance; however, the underlying reasons associated with these changes remain unanswered.
Considering the significance of metacognition in student approaches to learning and student achievement in STEM (Schraw and Moshman, 1995; Vanderstoep et al., 1996; Tomanek and Montplaisir, 2004; Miller, 2015; Bene et al., 2021), impact of explicit metacognitive instruction has been explored as a potential intervention to improve students’ studying in chemistry courses and ultimately, their performance. Muteti et al. (2021) first investigated the impact of explicit metacognitive instruction on changes in students’ study strategies and performance in general chemistry class. They found that 66% of students reported the metacognitive instruction had a positive influence on their study strategies and reported more higher order study strategies for potential adoption after the instruction. However, the remaining 34% reported no influence. This could potentially be attributed to certain students already possessing knowledge of effective study strategies before receiving instruction. However, it is also plausible that various factors influencing their decision-making regarding study strategies were not specifically addressed by explicit metacognitive instruction. The study outcomes also indicated that merely 1.7% of students acknowledged the efficacy of metacognitive instruction in enhancing a crucial component of metacognition: the capacity to self-reflect or evaluate the learning (Drigas and Pappas, 2017). This implies that explicit instruction may not afford students significant opportunities for self-reflection. Following the same method of metacognitive instruction, Muteti et al. (2023) later investigated equity gains in the reported study strategies between student demographic groups: gender, race/ethnicity, and first-generation status in general chemistry I course. Their findings revealed the potential impact of the metacognitive instruction in increasing participants’ awareness of and the potential adoption of effective study strategies, thereby reducing equity gaps. However, the findings also indicated low reported adoption of metacognitive strategies among all demographic groups, which again calls for an understanding in how students make decisions regarding their studying. Gamby and Bauer (2022) developed a curriculum-embedded metacognitive instruction module for general chemistry students in community college to help them build and adapt metacognitive knowledge of cognition and incorporate effective study strategies. The study's results indicate that instructors can help students become more aware of effective study strategies, but the instructors need to challenge their perceptions and beliefs about learning in order to do so. The evidence regarding whether students will continue to employ the strategies learned in the module was described as weak, implying there are unaccounted factors guiding students in making these decisions.
Review of the literature highlights three important aspects. First, despite the extensive focus on investigating students’ strategies and assessing their impact on performance in chemistry, there remains a gap in understanding the underlying factors that guide students in making the decisions they do concerning their study strategies. Indeed, while existing research highlights the significance of timing and the nature of study strategies in optimizing learning outcomes, there is a notable gap in our understanding of why students prefer certain study strategies over others. Research in fields other than chemistry have demonstrated that students may select study behaviors based on exam format (Feldt and Ray, 1989; Entwistle and Entwistle, 2003; Kember et al., 2008), perceptions of the course (Wilson et al., 1997; Kember et al., 2008), and perceptions of the discipline (Prosser et al., 1996; Kember et al., 2008). Further research has quantitatively described the differences in chosen study strategies with assessment type (Bunce et al., 2016), while the decision process in choosing study behaviors requires further exploration. Second, quantitative studies that capture students’ study behaviors at a single time point do not provide a comprehensive understanding of how students approach their studying for a course. The outcomes of studies in different disciplines, including chemistry, have described the evolution or alteration of students’ study strategies throughout a semester (Sinapuelas and Stacy, 2015; Ye et al., 2015; Gezer-Templeton et al., 2017); however, the underlying reasons associated with these changes remain unanswered. Third, metacognitive interventions have a positive influence on some, but not all, students’ studying. To design specific interventions aiming to enhance students’ study strategies, one must understand students’ decision-making process guiding their study behaviors. This study therefore delves into the dynamic nature of students’ study behaviors over time, examining how and why they adapt or maintain certain strategies using the theory of self-regulation. Therefore this work is guided by the research question: What factors guide students’ decision-making processes of their study behavior in first-semester general chemistry?
Methods
Ethical considerations
The study received approval from the Institutional Review Board (Study #005086), and students provided written consent to participate in the study. Recruitment for this study was conducted by a research team member who was not involved in the instruction in the course to minimize potential appearances of coercion. The students were compensated with a $20 gift card for each of the first three interviews and a $40 gift card for the last interview. All names presented are pseudonyms; student gender was not collected in this study so the presentation will use the gender-neutral pronouns “they”, “them” and “theirs”.Study participants and research setting
The study was conducted with students enrolled in a first-semester general chemistry class in a research-intensive university in the southeastern United States of America during the Spring 2023 semester. During this semester four general chemistry classes were offered, each led by one of three instructors, and class size ranging between 144 and 206. The four classes were coordinated, using a common syllabus, textbook, weekly homework, weekly practice problems, test reviews, and exams. All the resources were provided to students via an online learning management system. Online homework points were capped, allowing students to miss some questions and still earn full credit, and accounted for 10% of the total points with two attempts available per homework. Upon completion of the assignment, students could review how each question was scored and for questions that were missed they were given directions to resources pertaining to that topic. Weekly practice problems included a comprehensive set of problems based on the content covered in the class during the week and made available at the beginning of each week of classes. The test review was a set of twenty multiple-choice problems spanning the content of the exam and made available to the students a week before each exam. Other than course materials provided, the students also had access to weekly instructors’ office hours and academic success tutoring center. Answer keys and student scores were posted on the learning management system at the end of each exam day except for the final exam for which only exam scores were posted. No additional form of feedback was explicitly provided to students in class. However, the students had the option to attend the instructors’ office hours to discuss their exam scores. During week 5 of the semester, the first author made a brief presentation in each of the four classes regarding the study details, voluntary participation, compensation, and benefits and risk associated with the study before collecting the consent. The first nine students who volunteered to participate were invited to the first interview. For the following interviews, the same students were contacted via email and/or text. Students were asked to bring all the study materials they utilized during preparation for the tests. No attrition in the study occurred as all nine participants remained part of the study until the final interview.Data collection: interviews
There has been an emphasis on collecting students’ descriptions of their exam preparation activities that capture a memorable period when students are likely to be actively involved in self-directed learning activities (Tomanek and Montplaisir, 2004). Therefore, the first author conducted semi-structured interviews (60–90 minutes each) to explore factors guiding students’ study behaviors during their preparation for test 1, test 2, test 3, and the final exam during weeks 6, 8, 14, and 17 in the Spring 2023 semester (see Fig. 1). The content coverage by test was: | ||
| Fig. 1 Data collection timeline. | ||
• Test 1: structure of the atom, ionic charges, nomenclature, mass to mole conversions, molarity, and balancing chemical equations
• Test 2: reactions in solutions, stoichiometry, and enthalpy
• Test 3: quantum theory, electron configurations, periodic trends, Lewis structures, molecular geometry, and polarity
• Final exam: all past content and molecular orbital theory and gas laws
Each exam consisted of a multiple-choice format comprising twenty questions for test 1, test 2, and test 3, and thirty-two questions for the final exam.
The interview protocol included four sections. The first section focused on how students prepared for the exam, duration of studying, and strategies employed during studying and was adapted from the literature (Sinapuelas and Stacy, 2015). In the second section, they were presented with a list of strategies and inquired whether they had utilized them or not. Subsequent questions were posed to gain insight into the reasons behind their decisions to use or abstain from using each strategy in the list. In the third section, students were asked to engage in questions related to the content covered for the exam, however, these were not considered for the current analysis. The fourth section was designed to determine students’ study plans for the upcoming exam and whether they intended to make any changes. An example question at each section is presented in Table 1 and the complete interview protocols can be found in the Appendix, Table 4. All the interviews were audio-recorded and then the interview recordings were transcribed verbatim.
| Section | Example questions |
|---|---|
| First section: preparation for the exam | When did you start preparing for the exam? |
| Tell me about how you studied for the exam. | |
| Second section: use of listed strategies | Did you work with others while preparing for the first exam? If yes, how? How did it help you prepare better for the exam? |
| Third section: content of the test | What particular difficulty (hardness or large amount or lack of understanding) did you face with the content for the exam? |
| Fourth section: study plans for the next test | What changes, if any, are you planning to make while studying for the next exam? Why? |
Data analysis
To identify the factors that guide students’ decision-making process of their study behaviors, interview data were analyzed inductively and deductively in three phases (Strauss and Corbin, 1990; Thomas, 2006), focusing on the first, second and fourth interview sections where students explain the way they used study strategies for the exams and guiding factors for those strategies.In the first phase, which is inductive, two authors developed and applied a codebook concurrently and coded the data cooperatively to analyze what is constant, what is the change, and guiding factors accompanying the constancy and change. At this stage, study strategy, study behavior, and the change and constancy in study strategies were operationalized. Study strategy refers to what students do while studying for the exam and duration of time spent on what is done. In other words, it includes not only various activities that students engage to prepare for the exam but also preparation time. The strategies can include note taking, reviewing lecture slides, reading the textbook, going to tutoring, using instructors’ office hours, solving practice problems, taking test review, utilizing online resources, or spacing study sessions. Study behavior is a broader term encompassing the change and constancy in students’ study strategies (Sebesta and Bray Speth, 2023). Constancy is operationalized as a study strategy which remains unchanged from the previous exam preparation. A change is operationalized as a distinct study strategy for a subsequent exam from the previous exam. Since the purpose was to determine the change and constancy, we only reviewed the interview that followed Test 1 to note study strategies enacted for Test 1 and their planned study behaviors for Test 2. Therefore, three interview transcripts (post Test 2, Test 3, and Final Exam) were subjected to inductive coding (Patton, 2002). During the inductive development of codes for the change and constancy, researchers focused on the students’ responses describing their utilization or avoidance of specific study strategies. The change manifested itself in three forms as described with the following codes: adoption of new strategy, stopped using a previously used strategy, and revising the use of strategy. The constancy emerged as either not using a strategy or using the strategy. Concurrent with inductive coding of the change and constancy, the students' responses explaining why they change or keep constant their study strategies for the consecutive exams were simultaneously analyzed. This helped to determine students’ causes for the change and constancy, which was later used to identify factors that guide the decision-making process about study behaviors. As a result, the codebook was refined and applied to classify the causes for either changing or maintaining study strategies. However, the same causes were retained, albeit with distinct code descriptions. These causes are affective factors, chemistry content, gaining proficiency, time management, and maintain/improve the grade. The codebook was developed and applied concurrently through an iterative process where the authors analyzed the data from all nine participants through negotiation to reach a consensus. To address the issue of consistency of the coding scheme, another chemistry education researcher not directly a part of the project coded the set of transcripts for one participant. Cohen's kappa was used as the measure of inter-rater reliability and calculated to be 0.82.
In the second phase, factors guiding the decision-making process regarding study behaviors were extracted using constant comparison analysis. To do this, the first author wrote a brief memo for the study behaviors of each participant to make a case comparison between the participants (Glaser, 1978). During memoing, the frequency with which different codes were applied to each participant was analyzed, which helped to identify the primary factors that influence students’ decision making. That is, the code(s) that were most frequently applied to a participant were utilized in determining the primary factor that guided their decision-making process regarding their study behaviors. After creating memos for each participant, researchers read the memos through constant comparison to extract the primary guiding factors. Four factors were extracted from the code(s) as listed in Table 2 and described later in the results section.
| Guiding factor | Code(s): causes | Code description for change/constancy “The student changes or maintains using or not using a study strategy as deemed appropriate…” |
|---|---|---|
| Content-driven | Chemistry content for the exam | for nature of content for the exam. Nature of exam refers to type, amount, or difficulty level. |
| Gaining proficiency in the exam content | for gaining a better understanding of the content. | |
| Time-driven | Time management | for optimizing time utilization/enhance time efficiency. |
| Intrinsic value-driven | Positive emotions | due to more positive/positive connotations attached to the use of a resource such as feeling more confident or happy. |
| Negative emotions | due to more negative/negative connotations attached to the use of a strategy. | |
| High value | due to high value perception associated with the strategy. | |
| Low value | due to low value perception in the specific study strategy. | |
| Instrumental value-driven | Improving the grade | for obtaining a better performance outcome. |
| Maintaining the grade | for obtaining a similar performance outcome. |
In the third coding phase, self-regulation theory was applied deductively (Zimmerman, 2000) to categorize students regarding their decision-making process when they plan, execute, and evaluate their study behaviors. More specifically, the focus was on evidence of metacognitive components in students’ self-regulatory phases where they change or keep constant their study strategies, which helped to gain an in-depth understanding about factors guiding study behaviors. While doing this, we re-operationalized the phases of self-regulation concurrent with the purpose of this study. Planning phase includes students’ goals in relation to change and constancy in the strategies they plan to use in the upcoming exam. Performance phase is where students enact the change and constancy they planned and therefore, it includes types of students’ study behaviors. Evaluation enables students to appraise the products and processes of their study behaviors. To analyze students’ self-regulation of their study behaviors and metacognitive components evident in these phases, we focused on parts of student interviews indicating their decision-making process to change or keep constant their study strategies.
Through re-reading of memos where evidence of change and constancy were described in detail, one of the sub-components of metacognition, knowledge of cognition, was captured (Schraw and Moshman, 1995). Knowledge of cognition is composed of three sub-components. First, declarative knowledge is the knowledge about students themselves as learners (i.e., preferred study strategies to learn and strengths and limitations as learners) and factors influencing their performance. Second, procedural knowledge is described as knowledge about strategies (e.g., enhancing a study strategy through revising) while third, conditional knowledge includes knowledge of why and when to use a particular strategy (e.g., utilizing a strategy after evaluating its relevancy for the content and duration of studying). After agreeing on operationalization of three sub-components of knowledge of cognition, authors looked for instances that indicate enactment of those knowledge types when students change and keep constant their study behavior. As a result, two main categories of students’ decision-making process emerged that differ in the degree to which they are metacognitive. Results are presented to describe these two decision-making processes of students and their sub-groups through providing evidence for similarities within the group and differences between the groups.
Results
Two distinct decision-making processes were identified as metacognitive and affective. For each decision-making process, two distinct factors that guide the process were observed. Metacognitive decision-making can be influenced by either the content being studied for the exam, or considerations on time efficiency. Affective decision-making is driven by either an intrinsic value or instrumental value. The following sections provide a detailed description of these decision-making processes and the factors that guide them.Metacognitive decision-making
Five students’ (Mila, Mayank, Mia, Mike, and Megan) decision-making process regarding their study behavior was rooted in metacognition. In essence, when these students planned, executed, and reflected on their study strategies, they drew upon metacognition in the form of declarative, procedural, and conditional knowledge, and made choices to adopt, avoid, or modify their study behavior. This metacognitive decision-making process could be influenced by either the nature of the content being studied, or the time-efficiency of the strategies employed. This led to the existence of two sub-groups within metacognitive decision-making, which are content-driven (Mila, Mayank, and Megan) and time-driven (Mia and Mike). Students in both groups expressed contentment with their test results.When students in this category recognized a change in the nature of content, they enacted an alteration in their study behaviors. That is, if the students perceived upcoming exam material to be more or less conceptual, bulkier or harder, they made changes in their study behaviors. For instance, while planning for test 2 Mayank mentioned “I think I will keep it the same for right now just because I was happy with the score I got on the first exam. But if we have any other topic pop up or heavy calculations, then I would be prepared to make some changes.” During the interview after test 2, the student mentioned “It [test 2 covering stoichiometry and calorimetry] was definitely more intensive with calculations and practicing problems… There were a lot of calculation questions unlike the first exam [covering atoms, compounds, and chemical equations] … As much as I like reading over the notes or reading over a definition from the book…they don't help you…with such math heavy content… So the best way to help you do the questions is by actually trying them and doing like other ones…” In this quote, the student mentioned enacting a change by prioritizing problem-solving instead of reading, in response to a more perceived algorithmic content as a result of active monitoring the content for test 2, enacting their plan described in the first quote.
The change in study behavior for these students appeared in various forms in the performance phase, such as adopting a new strategy, revising the utilization of a familiar strategy, or ceasing the use of a strategy. An instance for how type of content for different tests drove the change in the use of flashcards and how metacognitive knowledge was enacted was observed in Mila. They prepared flashcards while studying for test 1 to study polyatomic ions and explained: “They [flashcards] are kind of like a test in a way. You only have one option you have to kind of quiz yourself and then you check it and see if you got it right or wrong, kind of tells you what you need to focus on…” However, for test 2 they did not prepare flashcards as they perceived it to be unsuitable for the type of content (i.e., stoichiometry, limiting reactants, and calorimetry) and expressed this as “…The only ones I did use were the polyatomic ions, just because it was a lot of different…formulas and I don't feel like this unit had a lot of a lot to remember.” However, Mila planned to make flashcards for molecular geometries during test 3 preparation as they knew they were “… going to be overwhelmed and just looking at the chart [for molecular geometries] is not really helpful.” Once more, the decision to create flashcards was influenced by the specific content for the test. The student realized that simply referring to a chart wouldn't be beneficial for learning molecular geometries evidencing their planning and prompting them to make flashcards instead.
Megan revised study strategies based on the amount of content. For test 1 and test 2, Megan used multiple strategies (e.g., using the test review as a test simulation, visiting the tutoring center, or creating flashcards) to understand the content and adapted the use of lecture slides for test 3. For test 1 and test 2, they reviewed lecture slides to reference a solved problem mentioning “When I'm studying, if there's like a certain concept that I know that I'm just like, iffy on, I need to see another example, I'll go back [to the lecture slides]” but for test 3, they “…went through all PowerPoints and lectures and look through that just because a lot of the stuff was so long ago and wanted to go through what was different.” They adapted the use of a strategy (i.e., switching from reviewing select lecture slides to all lecture slides) through active monitoring to address the greater amount of content covered for test 3 (quantum chemistry, periodic trends, Lewis dot structures, and molecular geometry) as compared to previous tests.
Mayank was among the ones utilizing a new strategy, in this case watching internet videos, to understand the application of enthalpy, a topic they struggled with, while studying for test 2. They expressed this as “I did enthalpy questions and at that point I already had a question wrong. So, I went to YouTube…just like [to] make sure I was good in that area.” While studying, the student actively monitored where they were challenged and adopted the use of a new strategy to better understand the topic.
In terms of constancy in study behaviors, these students employed metacognitive knowledge through engaging in study behaviors, with considerations of time management, employing multiple strategies, and help seeking when needed. Regarding time management, the students in this category stayed consistent in terms of their studying throughout the semester. The students intentionally planned and managed their outside of classroom time studying to master the content. For instance, Megan expressed consistently studying across the semester as:
• [Test 1]: “…I just like, take notes during class, on my iPad, like on the PowerPoints and everything, just like work everything out…do like the homework for the week. And to normally I go through those [the practice questions for the week.], and…like sporadically, I'll go to tutoring if I look a certain question that just didn't make sense to me.”
• [Test 2]: “I kind of do like the same kind of like way of setting for testing…kind of did like week by week.”
• [Test 3]: “I mean, I was intentional about …not waiting until like the last minute or like the last week to like, study everything.”
Throughout the semester, the student not only planned but also enacted a consistent approach to time management outside of class, ensuring that the material was spaced out.
In addition to being consistent in time management, these students also attempted to address and overcome learning difficulties they encountered in their learning process through the use of multiple strategies. The initial resource these students turned to was either the lecture slides or their own notes. When the students discerned that the lecture slides or notes do not address their concern through active monitoring, they tended to use additional resources such as the instructors’ office hours, tutoring, or internet. This occurs through the enactment of procedural and conditional knowledge as students monitor their learning and recognize when and how to use different strategies while studying. In addition, they were constant in the type of additional resources they utilized. For instance, Mila benefited from office hours for the first three tests and explained how it was helpful when preparing for test 3: “If I'm still not understanding where I went wrong that's when I went to [instructor's] office hours and…He helped me understand where I went wrong.” As an additional resource Megan mentioned utilizing tutoring as “…the ones [practice problems] that I don't understand, I normally just bring it to one of the tutors and… then I am able to figure it out with just their leading.” They continued to go to the tutoring center to address difficulties while learning.
In summary, these students engaged in strategies that facilitated their learning and mastery of the content, reflected on how those strategies improved their learning, and adapted their strategies based on the content of the upcoming test.
The constancy in study behaviors was noticed in either maintaining the use of a strategy due to its time efficiency or refraining from using a strategy because it was deemed time inefficient. Through active monitoring of their strategies in terms of time, the students continued to engage in strategies that they believe are time efficient. For instance, after test 1, Mia started adding notes to the lecture slides instead of writing them and was persistent in engaging in this strategy and conveyed it during the interview after test 3 as “…I still add notes to the lecture slides themself as it helps save so much time than writing them.” After initially evaluating the strategy to be more timesaving, the student persisted in practicing the study strategy as the semester progressed. The student enacted declarative knowledge as they understood adding annotations to the lecture slides is helpful for them as a learner. The continued engagement of adding notes to the lecture slides also provided evidence for utilization of procedural (how to write notes) and conditional (why to writes notes on the lecture slides) knowledge. Similarly, the students consistently avoided strategies they consider inefficient in terms of time usage. Mike consistently described their choice of not making flashcards for test 1 as “I will spend a lot of time making them [flashcards] and not use them enough.” and again after the final exam, they mentioned “…flashcards, they are just too time consuming to make.” Whether they adhered to a particular strategy or avoided it, the constancy in these students' study behaviors was shaped by their active metacognitive processes, which are oriented towards optimizing time efficiency.
Affective decision-making
The primary decision-making process of students Avni, Alice, Aadi, and Ava was influenced by the affective domain over metacognitive considerations. This means that these students based their choices regarding their study behaviors on the emotional aspects and the value they attribute to the study strategies. The nature of value associated with affective decision-making led to the categorization of students into two groups: intrinsic value-driven (Aadi and Ava), and instrumental value-driven (Avni and Alice).Another instance of a change in study behavior was observed for Ava. They stopped going to the lecture classes during test 3 preparation and mentioned “Because it [lecture class] was boring, and it was slow and I was hoping I could not go and then study hard and then get a good grade.” While talking about their performance on test 3: “I definitely didn't feel confident about the material on the test, but I can handle it [test result].” The student's decision to stop attending the lecture class was motivated by their negative emotions and the perception of boredom associated with going to the class instead of metacognitive knowledge. In other words, there was no evidence of metacognitive knowledge enactment, as the student did not reflect or evaluate how attending the lecture could have improved their understanding. The student voiced emotional aspects related to the class more than their metacognitive knowledge in describing their choice.
The students consistently chose to either engage or not engage in strategies, with the associated emotion and/or value remaining unchanged. An unchanged association of a positive emotion and/or high value with a strategy resulted in continued engagement with it. For instance, Aadi continued to visit the tutoring center as they said, “tutor is like my golden resource” for test 2 and “I would cry if the tutoring center was not there, they are so helpful” for test 3. The student consistently went to the tutoring center as they associated a high value and positive emotions with it. There was no evidence for the use of metacognitive knowledge behind their choice. That is, the student never drew upon how going to the tutoring center is beneficial for them to learn chemistry (declarative knowledge), when and why to use tutoring for preparing exams throughout the semester (conditional knowledge), or their own strategies to enriching the tutoring for learning (procedural knowledge). Similarly, an unchanged association of a negative emotion or low value with a strategy resulted in continued avoidance of the strategy. This was observed when Aadi refrained from utilizing the lecture slides during their studying for the semester. They expressed “They [lecture slides] are not beautiful. They are ugly.” (Test 1) “I am morally opposed to doing lecture slides because I don’t believe in them.” (Test 2); “Lecture slides are garbage…they are boring. I had to be really desperate to look at lecture slides.” (Test 3). Throughout the semester, the student consistently avoided using lecture slides during test preparations. Their decision voiced affective considerations more than metacognitive since they mentioned negative emotions associated with the lecture slides (boredom) and the low perceived value attributed to using the lecture slides. However, there was no evidence of the student's engagement in metacognitive processes during the semester to reflect on the impact of refraining from the use of lecture slides. This means they did not consider how this choice was affecting their learning, how they could have revised their use of lecture slides, or how their use could have assisted them for test 2 when they encountered difficulties with the equations. Ultimately, the student voiced low intrinsic value they associated with the strategy more than metacognitive knowledge for their decision.
Students within this category persisted in conducting strategies they perceived as conducive to achieving their desired performance outcomes. In other words, the consistency in their study behaviors was linked to their association of an instrumental value to certain specific strategies. For instance, Avni kept doing practice problems before each test and expressed this as “…I feel practicing before the test can really boost my score. I could like do it [practice problem] continuously with different numbers.” (Test 1); “I practice with different examples like different wordings and because then I would kind of like familiarize myself with all the different wordings. Even though my score did not get any better, I feel doing practice problems did help me get the score I got.” (Test 2); “…Yes, I did chapter six [practice problems]. Honestly, I practice them before the test because it helps me with performing the same if not any better.” (Test 3) The student continually dedicated time to practicing problems during their test preparation routine as they perceived practicing problems play a pivotal role in achieving their expectations. In examining their use of word choice, affective considerations were more manifest than metacognitive ones. Hence, the guiding factor in the decision-making process was the attribution of the instrumental value to doing practice problems. However, during interview post test 3 the student described their approach to solving practice problems as: “Basically, I wrote down any supplemental information that had to do with the question unless I could solve it right away, which there wasn't many of them that I was super confident on. I wrote down a lot of supplemental information from my notes from class, from book notes…that is how I always do the practice questions.” The student maintained a consistent practice of solving practice problems, convinced that they enhanced their performance, without an explicit evaluation or modification in their method of engaging in these practice problems. When asked, Alice avoided utilizing the textbook throughout the semester because they perceived it to be not helpful for achieving the desired performance outcome. They said “I don’t read or use the textbook because it doesn’t help for the test.” (Test 1); “No, I did not utilize the textbook…I feel it is not worth reading it because so much content in it never comes on the test. I just feel it is not going to help me get a good score.” (Test 2); “Textbook…like I said last time, it just doesn’t help for the test at all.” (Test 3) The student consistently avoided the use of textbook due to the low instrumental value associated with it, voicing affective considerations more than metacognitive knowledge and determining how valuable a behavior is for getting closer to a desired outcome in the test.
Discussion
Utilizing the theory of self-regulation, this study provided evidence for the existence of four distinct groups of students characterized by the factor central to the regulation of study behavior (Table 3). Content-driven students prioritized the nature of content (type, amount, and difficulty) in their decision-making and voiced metacognitive knowledge more than affect while justifying their decisions. In terms of change, these students adopted, discontinued, and revised their study strategies in response to the nature of the content. This outcome aligns with the assertion that metacognitive skills are discipline-specific (Thomas et al. 2008). Although students may employ similar study strategies across various disciplines, their choices to adopt, discontinue, or modify the strategies herein are shaped by the specific nature of the chemistry content. Time-driven students were solely different from content-driven students regarding the factor that guides their decision-making (i.e., time efficiency of strategies vs. nature of content). These two groups were similar in the degree of voicing metacognition and the type of changes enacted. Both these groups showed evidence of adapting to changing circumstances, in the form of revisions to study strategies (Drigas and Pappas, 2017). Intrinsic value-driven students voiced value and emotions associated with the strategy more than metacognitive knowledge when explaining the factor guiding their decision-making. In addition, this group was different than the content and time-driven ones since they did not revise a strategy. Instead, this group adopted or discontinued strategies as forms of change which indicates a lack of evidence for adaptability to changing circumstances (Drigas and Pappas, 2017). The instrumental value-driven group was unique since they were the only ones prioritizing the perceived potential of a strategy to achieve their expected performance outcome and enacting only one type of change, namely, adoption of a strategy. However, this group was similar to the intrinsic value-driven group in the sense they voiced affect more than metacognition when justifying their decision-making.| The group | Factor guiding decision-making | Voice of metacognition or affect evident in decision-making | Type of changes in study strategies |
|---|---|---|---|
| Content-driven | Nature of content (type, amount, and difficulty) | Metacognition | Adopt |
| Discontinue | |||
| Revise | |||
| Time-driven | Time-efficiency of strategies | Metacognition | Adopt |
| Discontinue | |||
| Revise | |||
| Intrinsic value-driven | Value and emotions associated with the strategy | Affect | Adopt |
| Discontinue | |||
| Instrumental value-driven | Performance outcome | Affect | Adopt |
Students either voiced metacognitive knowledge (content and time) or affect (intrinsic and instrumental value) more when explaining their decision-making in study behaviors which was captured by collecting data from participants across multiple time-points. This is consistent with the finding indicating that metacognition and affective factors are influential on studying (Ye et al., 2016). Categorization of students’ decision-making process as metacognitive and affective can be explained with the type of information processing system in decision-making (Epstein, 1994). Students in the affective group can be experiential or intuitive individuals who learn from experience without conscious attention (Hogarth, 2001) since they are under time pressure (Finucane et al., 2000) and cognitive load (Gilbert, 2002). On the contrary, students in the metacognitive group can be analytical in their decision making where they enact cognitive resources lowering the influence from affect (Stanovich and West, 2000).
The existence of two major groups can also be explained with the assumptions from the model of adaptable learning (Boekaerts, 2008) that highlights priorities that individuals inherently self-regulate in their behavior. One priority is the desire to extend knowledge and skills, which was observed in students voicing metacognition as they either aimed to gain proficiency in chemistry content (“I did enthalpy questions and at that point I already had a question wrong. So, I went to YouTube there just like make sure it was good in that area.”) or improve their time management (“…I still add notes to the lecture slides themself as it helps save so much time than writing them.”). Another priority is the wish to maintain available resources to avoid distortions of well-being. Consistent with this priority, affect-driven students either preferred the strategies they have positive emotions towards or the strategies they considered influential for their grade. The model also identifies the sources of information during regulation as the perception of the learning situation (e.g., the task), domain-specific knowledge relevant to the situation (e.g., meta/cognitive knowledge), and students’ self-system (e.g., goal and value). Although the task, preparing for the exam, was the same for the participants, students’ goals and values were different from each other. Content-driven students aimed to gain proficiency in chemistry content while time-management students’ goal was to avoid spending more time than needed. Existence of students guided by time concerns is consistent with the literature indicating that students prioritize resource management during regulation (Xiao and Yang, 2019). Intrinsic value-driven students preferred strategies that they value and have positive emotions whereas instrumental value ones were interested in the degree to which a strategy is instrumental to improve or maintain their grade. This type of decision-making lacks a crucial component of metacognition, namely discrimination as a form of wise judging (Drigas and Pappas, 2017). Discrimination requires an individual to identify what is valid and relevant beyond affective considerations. The influence of either an intrinsic value or instrumental value in the affective group is compatible with the literature indicating variability of goals in learning contexts (Bürger, 2015).
Another finding of this study is how the groups of students differ in their exhibited changes in study strategies summarized in Table 3 (for more information on the changes in study strategies, Table 5 in the Appendix details the changes for each participant). Students engaging in metacognitive decision-making processes revised their existing strategies which was not observed with students engaging in affective decision making. Hence, students in the affective group solely relied on the available resources in the context (i.e., provided by the instructor and university). Since this group did not revise any strategy, it was an indication of students’ lack of evaluation of the current strategy in terms of how it can be made more effective for studying (Desjarlais and Smith, 2011; Efklides, 2011). This is consistent with the priority to maintain resources to avoid distortions to well-being (Boekaerts, 2008) as revising a strategy requires acknowledging a flaw in the behavior which may serve as a threat to well-being. While not all study strategies are equally effective, some can be enhanced through revision for improved efficacy by the enactment of procedural knowledge (Brown et al., 1983). For instance, Mia in the time-driven group revised the note-taking strategy by adding annotations on the lecture slides instead of writing full notes to save time but Aadi in the intrinsic value-driven group completely stopped taking notes during the class. This is consistent with the literature where it is argued that students who do not exhibit metacognitive skills do not convey an awareness of the limitations posed by their learning style (Martin et al., 2000). This was also evidenced during the last interview with Avni when they mentioned “I feel like without these interviews, I probably would not have reflected nearly as much as I have. I normally never do that. I'd get a bad grade, or I'd get an okay grade and I'd be like, ‘Okay, well hopefully next test I can do a little better.’ I wouldn't put that much thought into it.” This means students guided by the affective decision-making may not engage in this process themselves. If so, it would prevent them from effectively examining the study strategies employed through enactment of knowledge of cognition. On the other hand, students guided by the metacognitive decision-making process appear to engage in self-reflection with concurrent evaluation during their self-regulation process. This allowed them to modify existing strategies rather than discarding them entirely since they prioritized knowledge and skill enrichment, one of the priorities in self-regulation described in the model of adaptable learning (Boekaerts, 2008).
Implications
As foundational work, instructional implications can be inferred but have not been evidenced. Previous research has demonstrated that active study strategies are more productive than passive ones (Hartwig and Dunlosky, 2012; Walck-Shannon et al., 2021). This leads to an important question: how can instructors promote the use of productive, active study strategies? The results of this study indicated that students differ in the factor guiding their decision-making when they regulate their study behavior. This suggests that a one-size-fits-all method would not work to improve students’ study behaviors to adopt more active strategies. Numerous metacognitive interventions (Muteti et al., 2021; Gamby and Bauer, 2022; Muteti et al., 2023) have been employed to enhance students' studying. However, these interventions are generalized and lack tailoring to accommodate students with different decision-making processes. Instructors should be aware of various types of students with different decision-making and devise different methods to promote active study strategies. Also, the results highlight the dynamic nature of study strategies, therefore, we recommend that instructors regularly collect students' lists of study strategies to be aware of changes in their strategies. For students who metacognitively evaluate their strategies, providing information on examples of active study strategies and the efficacy of active study strategies and examples of study strategies may be sufficient for students to consider, adopt, and revise active study strategies. For students who are enacting affective considerations in adopting their study strategies, a study cycle approach (Cook et al., 2013) where students are prompted to assess their adopted strategies may be more productive.Another way instructors can benefit is by employing a personalized study recipe (Lawson et al., 2021) with addition of post-exam activities such as exam wrappers (Gezer-Templeton et al., 2017; Hodges et al., 2020). These activities are designed to target students individually and, as a result, are better suited for students guided by different factors in their decision-making processes. The detailed novel recipe can be found in the original paper by Lawson et al. (2021). In brief, the personalized study recipe depicts a systematic self-regulatory time-management approach with embedded prompts to promote metacognition, which aim to increase students’ accountability for their learning (Lawson et al., 2021). In addition, following each exam, instructors can incorporate self-reflection activities, such as exam wrappers (Gezer-Templeton et al., 2017; Hodges et al., 2020), to encourage students, particularly those driven by affect, to assess their own strategies after implementation. Exam wrappers have the potential to serve as an intervention to enhance specific components of metacognition such as self-evaluation and discrimination as a form of wise judging (Drigas and Pappas, 2017) for affect-driven students. These activities may help students evaluate their approaches and offer instructors valuable insights to gauge the effectiveness of the metacognitive intervention, and guide feedback to students in need of assistance in regulating their study behaviors. This work did not explore these possibilities, so further exploration is necessary to thoroughly investigate the outcomes of potential interventions.
Limitations
The data were collected from the first nine students to volunteer at the research setting where the study was conducted. This sampling method was not meant to generate generalizable information. Instead, this work seeks to detail the applicability of self-regulation theory (Zimmerman, 1990) to students’ study behavior including the change and constancy in the used strategies as prior work with this theory has been mostly focused on learning (Zimmerman and Kitsantas, 2014). Available resources and instructional style might differ across universities, which can influence students’ study behaviors. More importantly, students’ characteristics are linked to why they change or keep constant the strategies they used. Therefore, researchers can utilize the self-regulation theory to examine whether conclusions reached in this study are valid in their institutions. The results are limited by students’ communication and recollection of their studying for the exam even though attempts were made to interview students soon after each exam to capture students’ studying plans and processes. Finally, the findings could be somewhat constrained by the varying extent of optional feedback (such as visiting instructors’ office hours after an exam to discuss preparation and grade) each student received after every exam; however, the interviews with the participants did not bring forth any impact from the feedback.Conflicts of interest
Researcher SEL receives funding from the Royal Society of Chemistry. The Royal Society of Chemistry did not play a role in the data collection, data analysis or presentation of the research results.Appendix
Table 4 includes the complete interview protocol and Table 5 presents the changes in study strategies for each student with each test.| Interview | Interview protocol questions |
|---|---|
| Interview 1 | How do you feel about the past exam? |
| When did you start preparing for the exam? | |
| Do you think the time was enough to prepare for the exam? Why/why not? | |
| How have your study habits changed from high school to college? | |
| Tell me about how you studied for the exam: their resources (notes, flash cards, solved practice problems) serve as a guide. | |
| How do you use a particular resource?/Why did you not use it? | |
| When do you use it? | |
| Are you planning to make any changes for the next test? Why/why not? | |
| How/Why do you think these changes will be helpful? | |
| Here is a practice problem from test 1 material. Describe how you would go about doing/solving this problem. | |
| Interview 2 | How do you feel about Test 2? |
| How do you feel about your performance on Test 2? | |
| How has the course content changed after T1? | |
| When did you start preparing for the exam? | |
| What resources did you use for preparation of Test 2? | |
| Why did you use the *specific resource*? | |
| Tell me about how you studied for Test 2. Take me on your journey of preparation for T2. | |
| In what ways was it different from Test 1? | |
| Here is a learning objective from T2. Tell me how you prepared for this objective. | |
| Here is a practice problem based on the same LO. Show me how you would work it out while you prepare for the test. | |
| How did your studying change post T1? | |
| Now that you have had a little more time in the course, how do you feel about the course content challenge? | |
| Generally, in the course have you changed your study habits (regarding use of resources: reference materials, practice problems, working with others) at all? | |
| Why did you make these changes? | |
| How have these changes helped you? | |
| Did these changes help you better deal with the course content challenge? | |
| How? | |
| How did these changes affect your performance on the exam? | |
| Which change in your study habit is the most useful to deal with the course content challenge? | |
| What changes are you planning to make while studying for the next test? | |
| In what ways can these changes help you overcome the challenge successfully? | |
| Why? | |
| Interview 3 | How do you feel about Test 3? |
| How do you feel about your performance on Test 3? | |
| How has the course content changed after T2? | |
| When did you start preparing for the exam? | |
| What resources did you use for preparation of Test 3? | |
| Why did you use the *specific resource*? | |
| Tell me about how you studied for Test 3. Take me on your journey of preparation for T3. | |
| In what ways was it different from Test 2? | |
| Here is a problem based on one of the LOs of Test 3. Show me how you would work it out while you prepare for the test. | |
| How did your studying change post T2? | |
| Generally, in the course have you changed your study habits (regarding use of resources: reference materials, practice problems, working with others) at all? | |
| • How do you know what habits are working for you? Habits not working for you? | |
| • Why did you make these changes? | |
| • How have these changes helped you? | |
| • Did these changes help you better deal with the course content challenge? | |
| • How? | |
| • How did these changes affect your performance on the exam? | |
| • Which change in your study habit is the most useful to deal with the course content challenge? | |
| • What changes are you planning to make while studying for the next test? | |
| What are some study habits you use while studying for other courses? | |
| Why/why not you use them for studying chemistry? | |
| How do you plan to study for the final exam? | |
| How will it be similar/different from the previous tests? | |
| Interview 4 | What were your expectations from GC1 at the beginning of the semester? |
| How do you feel about your overall performance in GCI? | |
| Did your performance meet your expectations? Why/why not? | |
| What was your goal while preparing for the final exam? | |
| How different was the preparation from the earlier tests? | |
| How did you allocate your time for different learning objectives? | |
| What are the most significant changes you have made in your study habits this semester? How do you know you made these changes? | |
| • Why were they significant? | |
| • What prompted you to make these changes? | |
| • Which change in your study habit proved to be the most useful to deal with the course content challenge? | |
| Looking back, what do you think you could have done differently if at all? | |
| How were the resources helpful? | |
| Which resources were the most helpful? Least helpful? | |
| What resources might have been an additional help for you in the course? | |
| What are your expectations from GC2? How do you expect it to be different or the same? | |
| What are you going to do same or differently for GC2? | |
| Which study habits would you follow and not follow for Gen Chem 2 course? | |
| What suggestions regarding the study habits would you give to a student who is just starting with Gen Chem 1? | |
| Text in red depicts study strategies that were discontinued after the previous test. Text in green depicts study strategies that were adopted after the previous test. Text in blue depicts study strategies that were revised after the previous test. Text in purple depicts study strategies that remained constant from the previous test. |
|---|
|
List given to students as a guide to recall the different resources/strategies they utilized during the exam preparation. Follow up questions were then asked regarding the use or avoidance of each resource/strategy:
Textbook
Lecture slides
Notes
Practice problems
Internet: videos
Internet: websites
Study guide/test review
Flash cards
Peers: peer leading
Peers: except peer leading
Tutor
Office hours
Other: please mention
Acknowledgements
The authors would like to thank the instructors for allowing data collection and the students for participating in this study. The authors would also like to acknowledge Ayesha Farheen and J. D. Young for their feedback on the interview protocols and development of the coding scheme. The authors would also like to acknowledge Dianna Kim and Isaiah Nelsen for their feedback during the manuscript writing.References
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