Using motivational factors and learning strategies to predict academic success.

Abstract

The purpose of the present study was to investigate the ability of a model of student learning incorporating achievement motivation, cognitive and metacognitive learning strategies to predict academic performance in college students. For this study, the Motivated Strategies for Learning Questionnaire was administered to 420 students at both the beginning and end of the term. The subjects were drawn from 9 college classes at three postsecondary institutions located in the state of Michigan and included the disciplines of Biology, English, and Social Science. The results of this study indicated that better performing students entered their courses with more awareness of cognitive learning strategies than the remainder of their classmates. Findings at the end of the term suggested that these higher achieving students were also better able to select cognitive strategies that were the most effective for academic success. The results supported the positive role of metacognitive strategies in the classroom as the most successful learners reported the greatest use of these techniques. An examination of the individual variables found that, at the start of the semester, expectancy for success and effort management were the strongest predictors of academic success followed by the cognitive learning strategy of organization and then by metacognition. The analysis performed on data at the end of the semester indicated that the cognitive learning strategy of rehearsal was the best predictor followed by effort management and course utility. A series of analyses was conducted to examine the relationship between the cognitive and metacognitive strategies. While significant effects between students reporting high and low strategy use were obtained, no significant interaction effects between cognitive strategies and metacognitive skills were observed. The examination of the ability of cognitive and metacognitive learning strategies to predict academic performance found that these techniques were able to account for approximately 13% of the explained variance at the start of the semester and 18% of the explained variance at the end of the course. A similar series of analyses performed for each of the individual classes on strategies reported at the end of the semester was able to account for larger amounts of explained variance. The range of this explained variance was from a high of 55% to a low of 13%. These results are in keeping with the findings of studies using other variables to predict academic performance. When data at the end of the semester were examined by discipline, the findings suggested that scales such as those used in this study appeared to be best matched for science classes or other courses with fixed areas for knowledge acquisition.