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Keywords:

  • underrepresented minorities;
  • study strategies;
  • organic chemistry;
  • academic performance

Abstract

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

This study sought to identify ethnically diverse students' study strategies in organic chemistry and their relationships to course outcomes. Study diaries, concept maps, and problem sets were used to assess study outcomes. Findings show that students engage in four commonly used reviewing-type strategies, regardless of ethnic group affiliation. However, these common strategies were rarely associated with students' problem solving, concept mapping, or course performance. In addition, students seldom engaged in metacognitive and peer learning strategies despite their reported benefits in the literature. Implications for research and practice are discussed in light of these findings. © 2013 Wiley Periodicals, Inc. J Res Sci Teach

As minority groups increase as a percentage of the US population, increasing their participation rate in science and engineering is critical if we are just to maintain the overall participation rate in science among the US population. Perhaps even more important, if some groups are underrepresented in science and engineering in our society, we are not attracting as many of the most talented people to an important segment of our knowledge economy (National Academy of Sciences, 2007, p. 167).

This quote underscores two importance points: (1) the urgent need to increase the number of scientists and engineers to maintain overall participation, and more important (2) the need to attract the most talented individuals, regardless of their ethnicity, to ensure America's global competitiveness in the knowledge economy. Researchers have noted the United States' reliance on foreign-born talent to fill STEM positions (Nelson, 2007; Rochin & Mello, 2007) and suggested underrepresented minority students hold the key to replenishing America's demand for highly skilled STEM workers (Morrissey, 2005).

The National Council on Education Statistics (2011) reported that higher numbers of underrepresented minority (URM) students are entering college with an interest in pursuing Science, Technology, Engineering, and Math (STEM) disciplines now more than any time in the past decade, largely due to the Latina(o) population. Unfortunately, the gap between URMs' aspirations to pursue STEM and completing STEM degrees is considerable and persistent (Hurtado, Eagan, & Chang, 2010). This gap increases as one moves along the continuum from undergraduate to post-graduate education (National Academy of Sciences, 2011; Rochin & Mello, 2007). If the U.S. is to realize its true potential and leverage the knowledge of all its citizens, it is important to examine the factors that contribute to students' academic achievement, especially among URM groups.

Background Literature

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

Contrary to students' experiences in primary and secondary schools, postsecondary education demands students be proactive, maintain motivation, and put into effect goals and learning strategies (e.g., study strategies) to succeed academically (Bembenutty, 2011). Many students enter postsecondary institutions lacking basic skills such as goal setting and failing to engage in appropriate studying strategies (Bembenutty, ibid). For nearly three decades, the self-regulated learning (SRL) framework has been used to examine study activities and academic success across various levels of education (Karoly, Boekaerts, & Maes, 2005; Pressley, 1995). However, few investigations have focused on SRL strategies among ethnically diverse students and academic success in science. This is especially important when considering the increasing ethnic diversity in college classrooms coupled with the National importance of science and technology. We pursue this important line of inquiry by drawing from the SRL literature to investigate diverse students' study activities and their relationship to key course outcomes in first-semester organic chemistry.

What Is Self-Regulated Learning?

There are a variety of definitions that have been used to describe SRL that vary depending on the theoretical perspective one uses. We apply Zimmerman's definition of SRL, which states that SRL is “the degree to which students are metacognitively, motivationally, and behaviorally active participants in their own learning process” (Zimmerman & Schunk, 2001, p.5). According to this definition, students take a proactive role in monitoring their learning, maintaining motivation, and engaging in behaviors (e.g., study strategies) that lead to academic success. For the purposes of this study, we focus on one particular aspect of self-regulation—the use of SRL study strategies—and its relation to course outcomes.

Self-Regulated Learning Strategies and Academic Success

Several studies have shown unique differences between high- and low-achieving students in the specific strategies they engage in to achieve academically (Zimmerman & Martinez-Pons, 1990). Researchers have noted the benefits of metacognitive strategies such as self-monitoring and regulation of thinking on problem-solving in chemistry—a crucial skill (Rickey & Stacy, 2000; Sandi-Urena, Cooper, & Stevens, 2012; Schraw, Crippen, & Hartley, 2006). Sandi-Urena et al. (2012) applied a mixed methods design to study the effects of cooperative problem-based instruction on general chemistry students' problem solving ability and metacognitive activity. Using the IMMEX assessment instrument, findings indicate that students who were given the collaborative metacognitive intervention treatment significantly outperformed the control group on solving non-algorithmic chemistry problems of higher difficulty. The authors concluded that providing an environment where students can engage in social interactions and reflection allows students to develop critical problem-solving skills.

Engaging in SRL study strategies has also been shown to benefit student understanding and course achievement, above and beyond prior knowledge (Azevedo, Moos, Johnson, & Chauncy, 2010; Plant, Ericsson, Hill, & Asberg, 2005). For example, Plant et al. (2005) examined the relationships between undergraduate Psychology and Education students' SRL activities and overall course performance. University records, study time allocation, academic performance questionnaires, and weeklong study diaries were analyzed using correlations and hierarchical regression modeling. After controlling for previous achievement (e.g., high school GPA and SAT scores), findings show that higher achieving students, as measured by course grades, studied in quiet environments and showed higher frequencies of time management and goal setting strategies. The authors concluded that SRL activities are an effective means of improving academic performance above and beyond prior knowledge. Studies incorporating the use of interviews and study diaries have shown similar results among more and less successful undergraduate bioscience students (Nandagopal & Ericsson, 2012).

Other researchers have investigated the use of technology as a vehicle to train students on SRL strategies. Azevedo and Cromley (2004) applied a pre- and post-test experimental design to examine the impact of SRL training on students' (n = 131) understanding of the circulatory system. Findings revealed that participants in the experimental condition (SRL training) showed a significant shift towards more scientific views in their mental models (as measured by written essays) compared to the control group (no SRL training).

These studies have shown the positive benefits of engaging in specific SRL strategies and informed academic interventions aimed at increasing students' achievement in gatekeeper courses such as organic chemistry. Academic interventions have reported significant and positive relationships between SRL strategies and student understanding, retention, and attitudes towards science (Sandi-Urena et al., 2012). Although these interventions may not directly apply the SRL framework in their interventions, the study activities implemented are well within the bounds of self-regulated study strategies. For example, the Science Gateway Workshop Program was developed to increase student performance and retention across many science disciplines. Students attended a 2-hour workshop sessions per week where they engaged in peer-learning strategies to solve complex problems in their respective domains (biology, chemistry, and physics). After controlling for prior achievement (SAT scores and prior GPA) findings show that workshop participants earned higher final grades and increased retention compared to non-participants in biology and chemistry. Effect sizes were greatest among underrepresented students.

Specifically in organic chemistry, similar interventions such as the Peer-Led Team Learning (PLTL) intervention has used small group peer learning opportunities (seeking assistance from peers) and explicitly encouraged students to reflect and monitor their problem-solving processes (self-evaluation and keeping records and monitoring understanding). Researchers found that PLTL participants show a significant improvement in course performance, retention, and attitudes towards the course compared to students in a traditional lecture course (Tien, Roth, & Kampmeier, 2002).

SRL Strategies Across Ethnic Groups

Despite the demonstrated benefits of SRL strategies on students' learning and performance, few investigations have examined study strategies across ethnic and cultural groups. Paul Pintrich, a leading figure in the development of SRL, pointed out that much of the research on self-regulation has been conducted in North American settings, which can be problematic when generalizing to non-Western cultures and ethnicities (Schunk, 2005). Indeed, researchers have posited that SRL skills and strategies are acquired through social processes, and therefore potentially vary across ethnic and cultural groups (McInerney, 2011).

The few investigations published in this area have shown both similarities and differences in SRL strategies across ethnic groups (for a review, see McInerney, 2011). Overall, research suggests that engaging in specific SRL strategies is positively related academic achievement (typically measured by grades and/or standardized exams). Second, a mastery orientation towards learning is a consistent predictor of self-regulation, regardless of ethnicity. SRL differences have been found in the specific types of strategies (e.g., use of memorization) between ethnically diverse students and have been attributed to the cultural context in which students are immersed (e.g., Eastern Asians collectivism and Westerners individualism orientations; McInerney, 2011).

Before proceeding, it is important to define culture. The definition of culture used in this article is borrowed from cultural anthropologist (D'Andrade, 1995) who defines culture as a socially inherited body of past human behavioral patterns that serves as the resource for a social group. We view ethnicity and culture as inseparable constructs. The important aspect of this definition is that culture is passed on from generation to generation (inherited) and manifests itself in behavioral patterns (e.g., use of specific study strategies).

Although not explicitly using the SRL framework, Treisman (1992) informally compared the study activities of Chinese and African American students enrolled in calculus at the University of California, Berkeley. Findings revealed that 18 out of 20 African American students studied in isolation and were more likely to keep their academic and social lives separate. Conversely, Chinese students formed an “academic fraternity” (p. 366) where students would complete homework assignments, check each other's answers, and engage in discussions. The author concluded that peer learning activities and the opportunity for feedback were the keys to Chinese students overall success in college calculus.

Purdie and Hattie (1996) investigated cultural differences in the use of SRL strategies between secondary school Australian students, Japanese students studying in Australia, and Japanese students studying in Japan. Self-report survey data revealed similarities and differences in the pattern of SRL strategies. Findings indicate that all three groups displayed a similar range in strategy use, but patterns of strategy use varied between groups. Japanese students in Japan placed significantly greater importance on memorization and reviewing strategies, while Australians placed greater emphasis on strategies such as goal setting/planning and self-checking. The authors suggest the growing multicultural nature of our classrooms requires the need to test Western models of learning.

More recently, Chiu (2007) examined the relationship between 15 year olds uses of SRL strategies (memorization, elaboration, and metacognition) and academic achievement in math, science and reading domains across 34 countries based on the Program for International Student Assessment (PISA) data. Using multi-level regressions, the authors found that metacognitive strategy use (e.g., reflection, planning, and evaluation) was positively related to students' academic achievement in math, science, and reading domains. The strength of this relationship varied by cultural dimensions. In individualistic cultures, the relationship between metacognitive strategy use and academic achievement were strongest. In contrast, the relationship between schoolmates' reported use of metacognitive strategies and academic achievement were strongest in cultures that placed a higher value on community. These findings suggest “that students in individualistic cultures rely more on individual learning strategies while students in collective cultures rely more on group learning strategies” (p. 360). Engagement in memorization-type strategies was negatively correlated with students' academic achievement in math, science, and reading domains, regardless of culture. In contrast to Purdie and Hattie (1996), Asian cultures did not report using memorization strategies more frequent than other cultures.

These studies highlight both similarities and differences in the uses of SRL strategies across ethnic and cultural groups. The few studies explicitly investigating strategies among diverse groups underscore the importance of considering students' ethnicity when examining the relationships between study strategies patterns and academic performance. It remains to be seen what similarities and differences in SRL strategy use exists between ethnically diverse students enrolled in a critical gatekeeper course such as organic chemistry. The increasing ethnic diversity of college classrooms and research demonstrating lower STEM graduation rates among URMs (National Academy of Sciences, 2011) emphasizes the importance of pursuing this line of inquiry. We build on previous investigations by not only identifying the influence of SRL strategies on students' academic achievement (as measured by course grades), but also focus on the relationships between specific strategies and performance on problem solving and conceptual understanding measures. Specifically, this study addressed the following research questions:

  • 1a.
    What types of studying strategies do students use in organic chemistry?
    • b.
      Do strategy frequencies vary between ethnic groups?
  • 2a.
    Are commonly used strategies related to measures of conceptual understanding, problem-solving performance, and course grades?
    • b.
      Are commonly used strategy correlations consistent across ethnic groups?

Methods

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

These questions were examined by collecting multiple sources of information regarding students' study activities and course outcomes. These sources include study diaries, concept maps, problem sets, and final course grades. For the purposes of this study, performance on course outcomes is defined as scores from assessment instruments (problem sets and concept maps) and final course grades. This section begins by describing the study context and participant characteristics, followed by specifics of the research materials, and concludes with a description of the data collection procedure.

Study Context and Participant Characteristics

This study was situated at an ethnically diverse State University located in Northern California. The University will be referred to as Golden State University in order to protect their anonymity. The top five ethnicities at the university include: White (32%), Latino (24%), Asian (23%), Filipino (9%), and African American (6%). Students that are traditionally categorized as “underrepresented” in STEM make up approximately 39% of the schools population. Females outnumber males by a percentage of approximately 58–42%, respectively. Approximately, 40% of students live 1–4 miles off campus, 19% live 5–7 miles off campus, and 30% live outside of the city/county border. Finally, students typically enroll in first-semester organic chemistry at the beginning of their sophomore year, although this is not consistent across all universities. In this study, freshmen, sophomores, juniors, seniors, and post baccalaureates made up approximately 3%, 24%, 26%, 37%, and 11% of our sample, respectively.

First-semester organic, in particular, has been singled out as a “gatekeeper” with high attrition rates (Grove, Hershberger, & Bretz, 2008; Paulson, 1999). Organic chemistry is a mandatory course for students' aspiring to matriculate in many STEM domains, and thus, a gatekeeper to various STEM careers and advance degree programs. The course can be considered high-stakes because it is highly influential for acceptance into many STEM-related professional schools (e.g., medical, dental, and pharmacy). First-semester organic chemistry has been specifically identified by students as a course that discourages them to remain in the sciences, especially among URMs, and led them to question their ability to succeed in science (Barr, Gonzalez, & Wanat, 2008). This can ultimately create a bottleneck to replenishing America's scientific workforce and stand as a barrier to diversifying STEM careers.

Recruitment took place during students initial organic chemistry lecture. Researchers explained the study protocol (discussed below) and interested students completed a questionnaire requesting information on age, ethnicity, and consent to access official transcripts. The questionnaire was used to classify students as Asian, Latino, or White based on their self-labeled ethnicities. Asian students composed the largest group in our sample (n = 41), followed by White (n = 31) and Latino (n = 17) students.

Materials

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

This study used three assessments to capture information regarding students' study strategies and course outcomes—weeklong daily diaries, problem sets, and concept maps. Each instrument is described below.

Development

Weeklong Study Diary

Students' study strategies were gauged through self-report weeklong study diaries. Previous investigations have supported the use of interviews and study diaries to capture information regarding students' study behaviors (Nandagopal & Ericsson, 2012; Plant et al., 2005) across similar education levels (undergraduates) and disciplines (science majors). Study diaries were created as a digital text file that contained three columns labeled—“time, location, and activity” and rows divided into 15-minute increments (midnight to midnight). Students were encouraged to be as specific as possible about study activities when focusing on organic chemistry. For example, students were to document their study times, durations, locations, materials used (e.g., textbook or notes), and study mates. This information provided researchers with a detailed view of the strategies students engage in for organic chemistry and the context of that study.

Problem Sets

The ability to problem solve is a highly valued skill among scientist and science instructors. A student's course grade is primarily determined by their performance on problem solving measures (mid-terms and final exam). Therefore, this study assessed students' problem-solving performance in relation to SRL strategies.

Problems set questions were collected from two sources: commonly used organic textbooks and the Web-Based Enhanced Learning Evaluation And Resource Network (WE-LEARN; for detailed descrption, see Penn, Nedeff, & Gozdzik, 2000). WE-LEARN is a database containing a wide assortment of vetted organic chemistry problems. A draft problem set was created and emailed to both organic chemistry course professors in order to identify the most salient questions and receive feedback on the content validity. Feedback was used to refine problem sets until agreement was reached with respect to the number of problems, content coverage, and degree of difficulty (factual to conceptual). Learning Unit 1 contained 15 organic chemistry problems, while remaining units contained 10 problems each. The same process was used to develop problem sets for subsequent learning units. Problem sets were used for research purposes and did not count towards students' grades.

Concept Maps

Previous investigations have found that students can successfully solve chemistry problems, yet fail to grasp concepts underlying the problems (Bhattacharyya & Bodner, 2005; Cartrette & Bodner, 2010). Therefore, this study also assessed students' understanding through concept maps, which have been shown to differentiate between varying degrees of understanding—naïve to conceptual (Novak & Gowin, 1984).

Concept maps are thought to capture a snapshot of students' knowledge. Briefly, a concept map is a graphical tool for organizing and representing one's knowledge and is intended to represent meaningful relationships between concepts in the form of propositions (i.e., semantic unit; Novak & Gowin, 1984; Shavelson, Ruiz-Primo, & Wiley, 2005). Concept maps include three components: concept terms, linking arrows, and linking phrases. Concept terms are key ideas/concepts pertaining to particular phenomena in a domain. Linking arrows provide a directional relationship between two concepts, while linking phrases, the words over the arrows, represent the specific relationships between a pair of concepts (for a review of the theoretical underpinnings, see Novak & Cañas, 2006). Concept map terms were not limited to concepts, but also included factual and procedural terms. This was done to obtain a richer picture of students' integration of factual, procedural, and conceptual terms in their knowledge structures.

Concept maps were constructed over several iterations as follows. Six commonly used organic chemistry textbooks1 were reviewed to identify key terms within each learning unit. Separately, two organic chemistry professors compiled a list of terms they believed to be essential for the same learning units. Textbook and professor-created terms were cross tabulated and overlapping terms were included. Draft terms were sent to organic chemistry professors for feedback and revisions were made as necessary until a minimum of 10 and a maximum of 14 terms were agreed upon. Learning Units 1, 2, 3, and 4 contained 14, 10, 12, and 11 concept map terms, respectively.

Coding and Scoring

Weeklong Study Diaries

Student-generated study diaries were coded using 14 SRL strategies (Zimmerman & Martinez-Pons, 1986; Table 1). Strategies ranged from memorization (Strategy 8) to seeking assistance from peers, professor, or teaching assistant (Strategies 9, 10, 11, respectively) to metacognitive strategies (Strategies 1 and 3). It was possible for a student to receive multiple codes and/or the same code multiple times in their diaries since students studied at various times throughout the day or used more than one strategy at a time. A sample study strategy diary with strategy codes can be found in the Supporting Information (Table S1).

Table 1. Self-regulated learning strategies used to code weeklong study diaries
StrategyExamples
1: Self-evaluationGauging own understanding of materials; re-reading own completed work; quizzing self
2: Organizing and transformingMaking own notes from resources (e.g., books, articles, problems); making flashcards
3: Goal setting and planningMaking a to-do list; planning study schedule
4: Seeking informationSeeking information from other resources (e.g., online, class materials, library)
5: Keeping records and monitoringPrinting/copying out notes; keeping records of questions got wrong/didn't understand in assignments/tests/practice problems
6: Environment re-structuringChanging location and/or environment to maximize studying
7: Self-consequenceSetting up personal rewards system; motivated by course grade/GPA
8: MemorizationRehearsing materials repeatedly
9: Seeking assistance from peersAsking friend for help; studying with friend for class enrolled in
10: Seeking assistance from professorAsking professor for help on course related materials (not extra credit opportunities)
11: Seeking assistance from TASame as professor, but with TA or other resources (e.g., parents, siblings, tutor/help centers)
12: Reviewing previous problemsReviewing past problems, tests, and quizzes
13: Reviewing notesReviewing notes from class, handouts, and study guides
14: Reviewing textReading course textbook and other assigned materials (e.g., articles, courseware, solutions manual)
Problem Sets

Problem set scoring consisted of a straightforward correct or incorrect score. Students were either given 1 point for a correct response or 0 points for an incorrect response. The number of correct responses was summed and recorded for statistical analyses.

Concept Maps

Concept maps were scored based on proposition accuracy. Propositions are thought to be the smallest unit of meaning in concept maps that can be used to judge the relationship between two terms (Ruiz-Primo & Shavelson, 1996). Two organic chemistry subject matter experts2 scored student-generated concept map propositions on scientific accuracy. Scientific accuracy was defined as the overlap between students' responses and scientifically accepted relationships. The following four-level ordinal scale (with underlying continuum) was used: 0—incorrect or scientifically irrelevant, 1—partially incorrect, 2—technically correct, but scientifically “thin” or vague, and 3—scientifically correct and scientifically stated (cf. Yin, Vanides, Ruiz-Primo, Ayala, & Shavelson, 2005). Thus, each student-generated proposition within a concept map received a score between 0 and 3. Proposition scores were then summed to calculate a final concept map score for each student. This process was repeated for each learning unit. Interrater agreement ranged between 0.77 and 0.91 (average 0.84) across the four learning units. If a consensus between raters could not be reached the proposition score was averaged between the two raters. Sample high and low scoring concept maps can be found in the Supporting Information (Figs. S1 & S2).

Analyses

Descriptive statistics on strategy frequencies across ethnic groups and learning units were used to address research question 1a. With respect to research question 1b, study strategy codes were tallied for each learning unit and across ethnic groups. Next, a 3 × 4 × 4 (ethnicity [Asian, Latina(o), White] × Strategies [2, 12, 13, 14] × units [1, 2, 3, 4]) split-plot ANOVA with repeated measures on strategy and unit variables was computed to investigate significant main effects, as well as, possible interactions.

Correlations were computed between study strategy frequencies and course outcomes to address research questions 2a and 2b (data were disaggregated by ethnicity for 2b). Common strategy frequencies were averaged across all four units for each ethnic group. The same was done for concept map proposition scores and problem set scores. Data were averaged across units because no significant interactions were found. Finally, students' letter grades were converted to numerical scores (A = 4.0, A− = 3.7, B+ = 3.3, B = 3.0, B− = 2.7; C+ = 2.3, C = 2.0, C− = 1.7, D+ = 1.3, D = 1.0, D− = 0.7, and F = 0 points). Correlations were calculated for each ethnic group to examine the relationships between common study strategy frequencies and concept map proposition scores, problem-set scores, and course grades. A common argument is that study frequencies may be misleading because it is possible to apply the strategy for a short period of time several times throughout the day. Taking this into consideration, study durations were also examined and resulted in statistically similar findings. In fact, study frequency and duration showed a strong correlation, r = 0.99. Therefore, we report study strategy frequency data.

In sum, various sources of information were collected to examine students' study activities, which included study diaries, concept maps, problem sets, and course grades. Diaries were coding using SRL strategies. Concept maps incorporated proposition scoring and problem-set questions were scored as correct/incorrect. Data were analyzed using ANOVA techniques and correlations in order to observe significant strategy differences between ethnic groups, and strategy relationships to conceptual understanding (as measured by concept maps) structures, problem solving, and course grades.

Procedure

Data collection took place over two separate semesters (Fall 2009 and Spring 2010) and covered four learning units previously and unanimously identified by instructors as important to the course:

  • Learning Unit 1—structure and bonding
  • Learning Unit 2—stereochemistry
  • Learning Unit 3—alkyl halide reactions
  • Learning Unit 4—reactions of alkenes

Individual interview sessions were used to train and assess students on study outcomes. All sessions were procedurally similar, however, session 1 was twice as long (2 hours) as subsequent sessions. In addition, interview sessions for each learning unit took place immediately after students completed the same unit in lecture.

The interview session began with a concept map training activity. Students were given instruction on the components of a concept map (e.g., nodes, linking arrows, and linking phrases) and how to properly complete the map, while the researcher talked through a completed example. Following this description, students were asked to complete a practice map on the water cycle (included seven terms) with interviewer feedback. After this training period, students were allowed to construct an organic chemistry concept map. In total, each student completed four concept maps (one per learning unit).

Following the completion of the concept map, students were asked to begin working on their organic chemistry problem set. A time limit of 3 minutes was applied to each problem (with multiple-part problems receiving 3 minutes per part) to increase the chances of students completing the problem set during the interview session. However, the 3-minute time limit was seldom exceeded. In total, each student completed four problem sets (one per unit).

Next, students were trained on how to properly complete study strategy diaries. Students were provided with a laptop containing the exact computerized study diary template they would use to complete their diaries. Students were guided through the process of properly filling out the study strategy (using the student's school schedule as an example). Emphasis was placed on providing details regarding organic chemistry related events like class time, studying activities, locations, and the materials used. Finally, students were informed that study diaries should be completed and emailed to researchers on a daily basis for seven consecutive days, with the exception of Unit 1 that was completed 7 days after students' initial interview session. After 7 days, researchers compiled daily diaries into a weeklong study diary for each student. In total, each student completed four weeklong study diaries (one per learning unit) throughout the semester. Interview sessions 2–4 were similar to Session 1, except interviews were scheduled for 1 hour and contained fewer concept map terms and problem set questions. It should be noted that problem sets and concept maps were not included in students' course grades. However, students were compensated (a total of $140) for completing assessments in order to encourage effort.

Results

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

Research Question 1a: What Types of Studying Strategies Do Students Use in Organic Chemistry?

Mean study strategy frequencies were plotted to visualize strategy patterns across ethnic groups and units. Figure 1 is presented to the reader to illustrate the consistent patterns found across all units (remaining graphs can be found in the Supporting Information, Figs. S3 & S5). Figure 1 shows the mean strategy frequencies across ethnic groups for Unit 2 and highlights a striking and consistent trend found across all units. It is clear that four strategies dominate, regardless of ethnicity. These common strategies include strategy 2: Organizing & Transforming, strategy 12: Reviewing Previous Problems, strategy 13: Reviewing Notes, and strategy 14: Reviewing Text. All other strategies combined were used approximately one-tenth of the time, or less, compared to the common four strategies. When examining the four commonly used Strategies (2, 12, 13, and 14) we see that Latina(o) students apply the strategies more frequently followed by Asian and then White students.

image

Figure 1. Unit 2 mean strategy frequencies by ethnic group. Displays the mean strategy frequencies for each strategy.

Download figure to PowerPoint

It is not only important to identify the specific strategies that students engage in, but also to reflect on the numerous strategies seldom used. The diary-coding scheme incorporated peer, metacognitive, and environment restructuring strategies. For example, peer-learning strategies that require students to take an active role in seeking information or assistance were rarely used (Strategies 9, 10, and 11). In addition, metacognitive strategies such as gauging one's own understanding (Strategy 1) and goal setting and planning (Strategy 3) were seldom used. This is especially interesting given that previous studies have demonstrated the positive benefits of using metacognitive (Azevedo, 2005; Plant et al., 2005), peer learning (Treisman, 1992), and environment restructuring (Plant et al., 2005) strategies on learning and academic success. Unfortunately, it is unclear what impact these strategies have on performance and grades because these strategies were rarely used.

Students' uses of the four common strategies were further explored to understand if strategies were applied differently across ethnic groups on Unit 2. Unit 2 was selected because study strategies frequencies peaked and would offer more frequent instances of SRL strategy use to inspect (a table containing representative samples of students' strategy use can be found in the Supporting Information, Table S2). Overall, it was unclear whether qualitative differences exist between ethnic groups actual use of SRL strategies in Unit 2. Diaries provided limited evidence towards actual strategy use. It is not clear whether students used Strategy 14 (reviewing the text) simply for memorization or while reading the chapter engaged in reflective questioning. The latter would be considered metacognitive. For example, one student specifically mentions reading the text to gain a “better understanding of the lecture” (Latino) while another student writes, “At home reading my organic chemistry textbook—fell asleep” (Asian). Based on these diary entries, we are able to identify the type of strategy used (reviewing textbook), but more research is needed to be able to clearly identify “how” students used specific SRL strategies.

A clear pattern that emerged from diary entries was related to the study context. Students were asked to explicitly state in their diaries if they studied with others. Although a couple of exceptions exist, students overwhelmingly studied in isolation, regardless of ethnicity. Whether students were studying at home, school, or on public transit, they tended to study alone. This finding is further unpacked in our discussion.

Research Question 1b: Do Strategy Frequencies Vary Between Ethnic Groups?

The four common strategies previously identified were further analyzed for statistical differences across ethnic groups and learning units using a 3 × 4 × 4 (ethnicity [Asian, Latina(o), White] × strategy [Strategies 2, 12, 13, 14] × unit [Units 1, 2, 3, 4]) split-plot ANOVA with repeated measures on the strategy and unit variables. Main effects (i.e., ethnicity, strategy, and unit) were statistically significant; all interactions were not. Of particular interest in this article, a significant ethnicity main effect indicated that average strategy frequencies differed between Asian, Latino, and White students, F(2,70) = 4.15, p < 0.05. Tukey's post hoc tests reveal that Latino students applied the common strategies (mean = 1.60) significantly more often, on average, than White students (mean = 0.75). No mean frequency differences were observed between Asian (mean = 1.00) and White or Asian and Latino students.

Main effects on strategy and unit were also found. A strategy effect indicates that students used certain strategies more often than others, F(3,210) = 6.85, p < 0.05, on average. Similar to descriptive statistics, post hoc tests revealed that students reviewed the textbook (Strategy 14) significantly more than other strategies. Finally, a unit effect was found indicating that mean strategy frequencies were greater on some units than others, F(2.40,168.23) = 18.33, p < 0.05. Specifically, students used strategies more frequently on Unit 1 (mean = 1.2) than Unit 4 (mean = 0.74), and significantly more frequent on Unit 2 (midpoint in the semester) compared to all other units (means = 1.2, 0.91, 0.74, respectively). Indeed, students' self-reported studying peaked at Unit 2 and then continually declined until reaching their lowest point at Unit 4.

Research Question 2a: Are Commonly Used Strategies Related to Measures of Conceptual Understanding, Problem-Solving Performance, and Course Grades?

Correlations incorporating all students revealed mixed results (Table 2). Organizing and transforming (Strategy 2) was related to concept maps scores (r = 0.27) and problem set scores (r = 0.26). Reviewing previous problems (Strategy 12) showed a trend toward significance with problem set scores (r = 0.19, p = 0.07). No strategies were directly related to course grades. In addition, Strategies 13 and 14 showed no relation to any outcomes. This is interesting because reviewing the text (Strategy 14) was the most frequently used strategy of all.

Table 2. Correlations between study strategies and concept maps, problem sets, and course grades
VariableStrategy 2Strategy 12Strategy 13Strategy 14Avg. CM ScoreAvg. PS ScoreCourse Grade
Note
  • avg. CM score, average concept map score; avg. PS score, average problem set score.

  • *

    p < 0.05.

  • **

    p < 0.01.

  • p < 0.10.

Strategy 21      
Strategy 120.47**1     
Strategy 130.50**0.45**1    
Strategy 140.60**0.39**0.44**1   
Avg. CM score0.27**0.15−0.020.051  
Avg. PS score0.26*0.190.04−0.020.58**1 
Course grade0.160.100.030.070.53**0.58**1

Research Question 2b: Are Commonly Used Strategy Correlations Consistent Across Ethnic Groups?

Another picture emerged after students were disaggregated by ethnicity and correlations computed. Study strategies were no longer related to students' concept map performance (a proxy for conceptual understanding) or course grades. Strategy 12, reviewing previous problems, was moderately related to Asian students' problem set performance (r = 0.39, p < 0.05) and Strategy 2, organizing and transforming, was related to Latino (r = 0.64) and White (r = 0.36) students' problem set performance (r = 0.64, r = 0.36, respectively). Correlation differences between ethnic groups may suggest interaction effects, however this was not found in our ANOVA analysis. This may be associated to the relatively large standard errors in students' study strategies.

In sum, examination of students' study strategies revealed three main findings. First, student's predominately engage in four common study strategies, regardless of ethnicity. This finding contradicts previous studies indicating differences in study activities between ethnic groups (Purdie & Hattie, 1996). The lack of a significant interaction effect provides further support for this finding. Second, common strategies are mainly reviewing-type strategies. Diary entries provide no clear evidence that ethnic groups apply the common strategies differently. Third, only one out of the four commonly used study strategies per group show a relationship to problem solving, but not conceptual understanding (as measured by concept maps) or course grades. These findings are further unpacked below.

Discussion

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

The goals of this study were to examine the types of study strategies ethnically diverse students' engage in, and the strategies relationships to course outcomes. Analysis of variance results showed that reviewing the textbook was the most frequently used strategy by students. One student specifically mentions reading the text to gain a “better understanding of the lecture.” This is interesting because using the textbook to clarify one's understanding may assist or hinder. Prior research examining vocabulary load in commonly used high school science textbooks (operationalized as the number of vocabulary terms per page) found that chemistry, in particular, contained more vocabulary terms per page than the recommended level for foreign language courses (Groves, 1995). Brian Coppola, science educator and organic chemistry instructor, notes that his experience with students' and chemistry textbooks has been discouraging. Referring to his students, Coppola states “… the majority of them read their texts as they might a novel, in a linear deliberate march that presupposes that every nuance on page 251 needs to be assimilated before they go on to page 252.” (Coppola, 1995, p. 92). These findings raise questions about the vocabulary load in chemistry textbooks and students approaches to reviewing the textbook. Students did not provide enough fine-grained details in diaries to ascertain “how” they engaged the textbook. Thus, it is unclear whether relying on the textbook alone to clarify one's understanding is a useful strategy. However, the lack of a significant relationship between this study strategy and all outcome measures may suggest that reviewing the text is not a particularly useful strategy, when used alone.

Our results show that study activities primarily include reviewing-type strategies. One argument may be that students engage in reviewing-type strategies because they carry less cognitive load compared to more complex cognitive and metacognitive strategies. Although reviewing strategies may carry less cognitive load and allow students to cover large amounts of information in short periods of time, a problem arises when stored information is needed to perform a task. When students rely only on reviewing strategies as a form of memorization, information is typically stored as disparate pieces of knowledge in long-term memory making it difficult to draw connections between information and develop a conceptual understanding (Bransford, Brown, & Cocking, 2000). This may also explain the lack of a significant relationship between reviewing-type strategies and concept map performance. Concept maps are thought to directly assess relationships between concepts/terms (in the form of propositions; Novak & Cañas, 2006).

An alternative explanation to our results is the convergence of the commuter environment (Academic Planning and Development, 2011) and financial realities at Golden State University. Although the relationship between commuter campuses and students' uses of SRL strategies was not the focus of this study, it provides a plausible alternative explanation for our results. Approximately 40% of students live 1–4 miles off campus, 19% 5–7 miles off campus, and 30% live outside of the city/county border. Research has shown that commuter students are less likely to persist in college and form fewer friendships, while residential students are more likely to persist, form tighter connections to the university, and have more friends attending the college (Skahill, 2003). This is compounded by the fact that the university eliminated many organic chemistry tutorial sections due to severe budget cuts during our data collection. It is possible that the confluence of budget cuts and commuter nature of the university impacted students' study practices, however, more research is needed before drawing conclusions.

Finally, previous literature has reported differences in the types of strategies students engage in among ethnically diverse groups (Purdie & Hattie, 1996; Treisman, 1992). Our data, however, provide contradictory evidence. Students, regardless of ethnicity, predominately engage in reviewing-type strategies used in isolation. Our findings suggest cautiously interpreting studies attributing academic performance to differences between ethnic groups' study strategies and suggest that all students can benefit from interventions aimed at promoting a repertoire of study behaviors, rather than assuming certain groups are more likely to engage in effective strategies compared to others.

Practical Implications for Chemistry Instructors

Course instructors can go a long way in assisting students by providing opportunities to engage in various strategies during course time (Azevedo & Cromley, 2004; Rickey & Stacy, 2000). Creating environments that allow students to participate in a variety of SRL strategies such as reviewing strategies, but also peer learning and metacognitive strategies can potentially improve students' conceptual and academic performance in organic chemistry. Weinstein, Acee, and Jung (2011) suggest that students need to develop a repertoire of strategies that help them “mindfully determine their preferences and to access alternative strategies when their preferences do not work” (p. 49).

The use of communication technologies such as clickers is one relatively inexpensive avenue towards reaching this goal. Research investigating the effects of active learning strategies through peer teaching (Mazur, 1997) using clickers has shown significant increases in students' conceptual understanding and attitudes in physics (for a review, see Crouch & Mazur, 2001) and chemistry (Hoekstra, 2008; MacArthur & Jones, 2008). Tutorial sections are another area that might be used to promote diverse sets of study strategies. Tutorials have the additional benefits of including students who may not normally seek out study mates and work to strengthen skills in a low risk environment, albeit more costly than clickers. In sum, instructors can go a long way to expand students' study strategy repertoire by building a community of practice that values diverse sets of study strategies that are known to contribute to students' academic performance, and ultimately mediate success in first-semester organic chemistry.

Conclusions and Directions

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

This study sought to identify students' study strategies in first-semester organic chemistry and their relationships to knowledge structures, problem-solving performance, and course grades. Special emphasis was placed on investigating differences between Asian, Latino, and White students. These inquiries were examined by capturing multiple forms of student data including study diaries, problem sets, concept maps, and course grades. Findings showed that student's predominately engaged in reviewing type strategies. The majority of the common strategies were not related to students' conceptual understanding or course grades. In addition, students rarely engaged in metacognitive and peer learning strategies, despite their reported benefits. These findings underscore the importance of creating communities of practice that promote meaningful study activities, such as peer learning and metacognition during problem solving. This is especially important in gatekeeper courses that form a barrier to students' pursuit of STEM related degrees, particularly for students of color.

At a time when many institutions of higher education are facing larger enrollments and an increasingly diverse student body it is crucial to investigate areas that can benefit all students, such as SRL study activities. This is even more relevant when considering the gatekeeping nature of organic chemistry and its differential impact on ethnic minorities access and motivation to pursue future STEM opportunities.

Directions for Future Work

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

Our study used ethnicity as a proxy for students' ethnic and cultural experiences. Although ethnicity can serve as a useful variable to understand similarities and differences between groups, future studies should aim to capture other information that can provide a fuller representation of the variation that exists within groups (e.g., duration in U.S.). For example, future studies should aim to collect more fine-grained demographic information about specific ethnicities within broad Asian, Latina(o), and White categories. Therefore, we situate our findings within our study context and student sample.

Second, study diaries provided researchers with useful information about the types of strategies students engage in and are relatively easy to administer across large sample sizes. As with all assessment instruments, diaries are limited in the information they capture. Future studies may benefit by incorporating study diaries, as well as focus group interviews where researchers can probe students' specific experiences using strategies. Doing so may uncover “how” students specifically apply strategies. Understanding the “how” will allow researchers to better illuminate why some strategies may be more (or less) beneficial to organic chemistry performance.

Finally, our study used the student as the unit of analysis. Future work should consider the influence of instructor's teaching styles on their student's uses of SRL strategies in and out of class. Previous work examining undergraduate chemistry students' suggests that students filter their perceptions of instructional practices through the lens of their own epistemological assumptions (Hofer, 2004), and therefore, can potentially influence students' uses of SRL strategies. Instructors grading policies is one such practice. This line of inquiry can potentially illuminate patterns that can be used to improve teaching and learning of organic chemistry.

Any opinions, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Special thanks to Erin Furtak for her comments on this manuscript.

Notes

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

1Organic chemistry textbooks included: P. Y. Bruice 3rd Edition, 2001; F. A. Carey 2nd Edition, 1992; J. McMurry 7th Edition, 2008; J. G. Smith 1st Edition, 2006; K. P. Volhardt & N. E. Schore 3rd Edition, 1998; L. G. Wade 5th Edition, 2003.

2Organic chemistry PhD candidates served as subject matter experts.

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  3. Background Literature
  4. Methods
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  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Background Literature
  4. Methods
  5. Materials
  6. Results
  7. Discussion
  8. Conclusions and Directions
  9. Directions for Future Work
  10. Notes
  11. References
  12. Supporting Information

Additional Supporting Information may be found in the online version of this article at the publisher's web-site.

FilenameFormatSizeDescription
tea21095-0001-SuppFig-S1-S2.pdf411K

Figure S1. Example of high scoring organic chemistry concept map (score of 22).

Figure S2. Example low scoring organic chemistry concept map (score of 4).

tea21095-0002-SuppFig-S3-S5.pdf371K

Figure S3. Mean strategy frequencies by ethnic group. Displays the mean strategy frequencies for each strategy (S) and ethnic group for Unit 1.

Figure S4. Mean strategy frequencies by ethnic group. Displays the mean strategy frequencies for each strategy (S) and ethnic group for Unit 3.

Figure S5. Mean strategy frequencies by ethnic group. Displays the mean strategy frequencies for each strategy (S) and ethnic group for Unit 4.

tea21095-0003-SuppTab-S1.pdf69KTable S1. Self-regulated learning strategies used to code weeklong study diaries
tea21095-0004-SuppTab-S2.pdf70K

Table S2. Correlations between study strategies and concept maps, problem sets, and course grades

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