Secondary students' strategies and achievement.

Abstract

This study expands previous research on whether college students' examination scores are influenced by their use of self-regulatory strategies during test preparation and performance. Sixty-one high school chemistry students were interviewed to assess learning strategies they used before, during, and after a testing period. Results indicate that high test scorers used more self regulatory processes to enhance their test preparation and performance compared to low test scorers, and self-regulatory skill and self-efficacy beliefs predicted subsequent test performance.

Purpose

This study seeks to extend Kitsantas' (2002) work on whether psychology college students' examination scores are influenced by their use of self-regulatory processes during test preparation and performance. Kitsantas' study is unique in that it was the first to follow the learning strategies that college students used before, during, and after an exam in a naturalistic setting. She called for expanding the research to include various populations as well as different subject areas. Our research addresses her recommendations by following high school chemistry students' self-regulatory processes during their test preparation and performance. In order to compare our results directly with the 2002 study, we are using similar research questions: (a) Will high test scorers use more self-regulatory processes to enhance their test preparation and performance compared to low test scorers?; and (b) Will self-regulatory skill, self-efficacy beliefs, and task value predict subsequent test performance?

Introduction

Regardless of the recent educational movement towards alternative assessment in determining academic achievement, many high school teachers still rely heavily on assessing students via traditional multiple-choice examinations. In fact, an argument can be made that scores on pen-and-paper examinations make up the majority of students' grades in certain high school courses such as math and science (Black, 1998). Students who excel in this assessment format are usually awarded higher grades than students who, although capable, may not have the necessary skills to do as well. For example, students who demonstrate proficiency and understanding in a science lab experiment may not demonstrate the same level of proficiency on a multiple-choice exam. Further, poor test scores may negatively affect students' college acceptance; many schools will not consider admitting a student who has a GPA below a 3.0. Thus, there may be students who are denied access to higher learning--not necessarily due to lack of ability, but because they may not possess or use the skills that would enable them to score higher on tests. Because of this, educators need a better understanding of the strategies, if any, of those students who perform well on examinations. A learning strategy is defined as the thoughts, behaviors, beliefs, or emotions of a learner that influence their ability to acquire, understand, or later transfer knowledge and skills (Weinstein, Husman, & Dierking, 2000). Using learning strategies is a significant way that students learn how to regulate their academic behavior (Zimmerman, 2002).

Self-regulation refers to self-generated thoughts, feelings, and actions that are planned and systematically adapted to affect one's learning and motivation (Zimmerman, 2002). In other words, self-regulating students are active participants in their own learning process. Students who do not engage themselves mentally, motivationally, and behaviorally in an academic task may not learn. Bandura's (1997) social cognitive theory represents the underlying foundation of this basic tenet: self-regulated learning is a function of the reciprocity between the student's environmental, behavioral, and cognitive processes. Students who purposefully change their environment, behavior, or their thinking will impact the other two factors. Illustrating this idea, a student who wishes to study for thirty minutes in order to prepare for a quiz can regulate her study efforts by acting on her study environment--removing distractions such as a TV set, for instance. This in turn affects her study behavior because she is able to study longer and persist through the academic task. When her efforts pay off with an A on the quiz, this increases her personal belief that she can accomplish similar tasks in the future. Self-regulating students use learning strategies as tools to make changes anywhere along this triadic feedback loop in order to accomplish an academic task. There are several specific self-regulation strategies that are related to students' academic performance: setting goals and planning (Locke & Latham, 2002), using appropriate strategies such as reviewing notes and elaboration (Kobayashi, 2006), directing one's study efforts through managing resources such as time and study environment (DeGroot, 2002), seeking help from social and nonsocial sources (Newman, 2000), and self-monitoring and self-evaluating progress (Cleary & Zimmerman, 2004).

Choosing the best strategy is a continuous and interactive process between the students' behavioral outcomes, personal efforts, and environment (Zimmerman & Schunk, 2001). In fact, students' specific choice of strategy use may be determined from several of these behavioral and personal factors, including the value students place upon the specific academic task (task value) and their belief that they can accomplish the academic task (perceived self-efficacy). These two beliefs have also been suggested as important predictors of students' academic performance (Schunk & Pajares, 2002).

Method

Participants

Participants were 61 male students, from 14-to 17-years-old, with a mean of 15.4 years. These students were enrolled in the author's three college-preparatory chemistry courses at an all-boys Catholic high school. All 61 students completed the study and received points for participating in each phase of the research, which were added to their final semester grade. Participation was voluntary and students and their parents completed informed consent forms.

Measures

Self-regulation pre-exam interview questionnaire. A 14-item questionnaire, modified from Kitsantas' (2002) study, was used to assess the self-regulatory strategies that each student generally employed before, during, and after test taking. Students were interviewed individually two to three weeks before taking the exam, for approximately twenty minutes, and their responses were audiorecorded and transcribed. The transcripts of the students' reported learning strategies were coded into different categories based on a slightly expanded version of Zimmerman's (1986) list of 14 self-regulation strategies. Two undergraduate students coded the data in the following way: If a student answered "yes" to a question and provided an appropriate strategy, then a point was scored for each strategy mentioned. No point was scored for students who answered "no," or for those who provided a nonstrategic answer. The agreement between the author and the other two coders was 89 percent and 84 percent, respectively. The agreement between the two coders was 85 percent.

Self-regulation post-exam interview questionnaire. To discover which strategies students actually used in their test preparation and performance for a specific exam, the students were interviewed again two days after they took an exam. The questionnaire instrument used in the post-exam interview consisted of the same questions found on the pre-exam interview, except that two questions were eliminated because they were only pertinent to the students' exam preparation. Student interviews were conducted similar to the pre-exam interview, except each interview lasted about ten minutes. Student responses were transcribed and coded into categories following the same procedures as the pre-exam interview. This time, the agreement between the author and the other coders was 97 percent and 95 percent, respectively, and the agreement between the coders was 90 percent.

Perceived self-efficacy. To measure students' perceived self-efficacy for chemistry exams in general, an 8-item self-efficacy subscale of the Motivated Strategies for Learning Questionnaire (MSLQ) was used. The questionnaire uses a 7-point Likert scale, from 1 ("not at all true of me") to 7 ("very true of me"). Reported scores were computed by taking the mean of the items that make up the scale.

Task Value. To measure students' perception of the task value of the test, a 6-item subscale of the MSLQ was used.

Chemistry achievement tests. To measure the academic achievement of the chemistry students, three multiple-choice tests were used. Each test covered the subject matter of one chapter and contained 50 multiple-choice questions.

Procedure

The first chemistry exam that was given to the students is typical in the course, but was also used as a means of allowing students to establish their learning strategies. Between the first and second exams (approximately 3 weeks), students were interviewed and their responses were audiorecorded using the 14-item pre-exam interview questionnaire in order to determine the strategies that students use before, during, and after a testing period. Immediately after the administration of the second exam, each student received the 14-item questionnaire. Two days after the second exam, students were re-interviewed and audiorecorded using the 12-item post-exam interview questionnaire to determine what reported strategies were actually used on the second exam. After students completed the third exam, all students were debriefed concerning the purposes and findings of the study.

Results

Students were placed into high or low-achieving groups based on their average score over three exams. The overall mean of all three tests was 68.4 with a standard deviation of 9.67. Because the students were college-preparatory and the average score was in the D+ range, the 13 students that were at least ten points above the mean, in the C+/B- range and above, were determined to be high test scorers. The 15 students who scored at least 10 points below the mean, in the F range, were considered as low test scorers. Significant differences were detected between the low and high-achieving groups in grade point average: the t-test (26) was -7.56, therefore the p was less than .001.

Overall strategies reported in the pre-exam interview

To determine whether high test scorers used more overall strategies than low test scorers during test preparation and performance, strategies reported for each time frame (before, during, and after) were added separately. Significant differences were found between the two groups on these three summed scores of self-regulatory strategies: Wilks' lambda equaled 0.67, the F value (3, 27) equaled 4.01, therefore the p was less than .01. No difference of overall strategy use or specific strategy use was found between the two groups in the post-exam interview.

Specific strategies reported before, during, and after exam in the pre-exam interview

High test scorers reported more self-regulatory processes (reviewing notes and seeking information and help) before test taking than did low test scorers: the F value (1, 27) equaled 6.92, therefore the p was less than .01. High test scorers also reported more self-regulatory processes (goal setting and planning) after test taking than by low test scorers: the F value (1,27) equaled 4.56, therefore the p was less than .05. No difference in reported strategy use was found between high and low test scorers during test taking.

Regression Analysis 

Strategy use, self-efficacy, and task value significantly predicted the total score on all three tests: the regression coefficient equaled 0.25, and the F value (3, 60) equaled 6.18, therefore the p was less than .001. The strongest single predictor was self-efficacy (beta was equal to 0.42), followed by strategy use (beta was equal to 0.24).

Discussion

Findings from this study corroborate Kitsantas' (2002) research that high test scorers use more self-regulatory processes to enhance their test preparation and performance compared to low test scorers. While these two studies are unique in that they followed students throughout their test preparation and performance, other studies support the idea that a strong relationship exists between the number of strategies that students possess and their academic achievement (Eshel & Kohavi, 2003; Schunk, 2001). These findings suggest that students who possess numerous strategies may experience a higher sense of control to accomplish an academic task than low achievers, and would have a higher self-efficacy for that particular task or subject domain. The fact that perceived self-efficacy was the most powerful predictor of exam scores in this study further supports this idea.

The results from students' post-exam interviews, however, did not support the hypothesis. Wood, Motz, and Willoughby's (1998) study showed that even though students may have more elaborate or strategic learning strategies, they do not always use them. One possible reason for this finding may be due to the difference between knowledge of strategies and use of strategies. This suggests that just asking students what strategies they actually use does not reveal the depth of their learning strategy repertoire. For this particular exam, high-achieving students may have only needed to use a few appropriate strategies to accomplish their academic goal.

High-achieving students reported strategies more often than low achievers before and after taking the test in the pre-exam interview: reviewing notes, seeking information and help, and goalsetting and planning. Kitsantas also found that high-achieving college students sought more information and help than low-achieving students. This implies that students who make better use of the resources available to them--e.g., using their notes or asking a friend for help--perform better on tests. This finding also suggests that the type of learning strategy may be just as important as the number of strategies students possess. Students who use strategies that require social interaction may receive more guidance and modeling to accomplish an academic task than if they did it completely on their own. Although younger learners may need more social assistance to learn a skill, older students who are able to practice their skills more independently still need assistance from teachers to help refine their skills (Schunk, 2001).

Contrasted with the college students in Kitsantas' study, high-achieving high school students in this study reported fewer learning strategies overall. These results are not surprising because students use more self-regulatory strategies in areas where they are given opportunities to self-regulate (Schunk & Ertmer, 2000). Learning to self-regulate is not considered an innate ability, but develops within the context of modeling and instruction from adults and peers (Zimmerman, 2002). Students who have limited chances to self-evaluate, monitor, or plan their academic goals in the classroom will be limited in their initiative to direct their own learning. For this particular chemistry class, the students are given the opportunity to choose from several different resources with which to prepare for an exam, and thus demonstrate the highest amount of self-regulatory behavior in this area. A similar argument can be made for information and help seeking. Ideally, high school students have extensive contact with their peers, teachers, and parents, which increases the opportunity to ask for help or to obtain information.

Findings from the regression analysis indicated that 25 percent of the variance in subsequent test scores could be explained by self-regulatory skill, self-efficacy beliefs, and task value. This result parallels very closely Kitsantas' finding of 24 percent, except that self-regulatory skill was a better predictor of college students' test performance than self-efficacy beliefs. This suggests that college students may have more experience using self-regulatory skills than high-school students and thus, self-efficacy beliefs begins to play less of a role in predicting academic performance as the student moves through higher grade levels.

Limitations

A limitation of the study involves self-reporting issues. At the beginning of the study, we had some concerns regarding whether students' self-reports would significantly alter the results, because one of the authors was conducting the interviews and giving students their grades. However, since the results were consistent with Kitsantas' findings and the author did not discover any inconsistencies between students' reports in the pre- and post-exam interviews as well as the observations of student study behavior in class, self-reporting does not appear to have been a major issue. The validity and reliability of the findings must still be considered, however, within the context of the limitations found in self-reporting--specifically, interviewer effects and memory distortions.

References

Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman.

Black, P. (1998). Formative assessment: Raising standards inside the classroom. School Science Review, 80(291), 39-46.

Cleary, T., & Zimmerman, B. J. (2004). Self-regulation empowerment program: A school-based program to enhance self-regulated and self-motivated cycles of learning. Psychology in the Schools, 41(5), 537-550.

Eshel, Y., & Kohavi, R. (2003). Perceived classroom control, self-regulated learning strategies, and academic achievement. Educational Psychology, 23(3), 249-261.

Kitsantas, A. (2002). Test preparation and performance: A self-regulatory analysis. The Journal of Experimental Education, 70(2), 101-113.

Kobayashi, K. (2006). Combined effects of note-taking/-reviewing on learning and the enhancement through interventions: A meta-analytic review. Educational Psychology, 26(3), 459-477.

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57, 701-717.

Newman, R. S. (2000). Social influences on the development of children's adaptive help seeking: The role of parents, teachers, and peers. Developmental Review, 20, 350-404.

Schunk, D. H. (2001). Social cognitive theory and self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 125-151). Mahwah, N J: Lawrence Erlbaum.

Schunk, D. H., & Ertmer, P. A. (2000). Self-regulation and academic learning: Selfefficacy enhancing interventions. In M. Boekaerts, P. R. Printrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego, CA: Academic.

Schunk, D. H., & Pajares, F. (2002). The development of academic self-efficacy. In A. Wigfield & J. S. Eccles (Eds.), Development of achievcment motivation (pp. 16-31). San Diego, CA: Academic.

Weinstein, Husman, J., & Dierking, D. R. (2000). Self-regulation interventions with a focus on learning strategies. In M. Boekaerts, P. R. Printrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 727-747). San Diego, CA: Academic.

Wood, E., Motz, M., & Willoughby, T. (1998). Examining students' retrospective memories of strategy development. Journal of Educational Psychology, 90, 698-704.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41 (2), p. 64-71.

Zimmerman, B. J., & Pons, M. M. (1986). Development of a structured interview assessing student use of self-regulated learning strategies. American Educational Research Journal, 23(4), 614-628.

Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Mahwah, N J: Lawrence Erlbaum.

Jeffrey S. Judd, University of Hawai'i at Manoa, HI

Frederick T. Bail, University of Hawai'i at Manoa, HI

Judd is a secondary science teacher and a doctoral candidate in Educational Psychology, and Bail, Ph.D., is a Professor in the Department of Educational Psychology.