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Predicting first-semester grade point average using self-regulated learning variables
Abstract
Much effort and expense is put into the creation and validation of academic and cognitive measures, yet the research continues to suggest serious limitations in using these tests in educational decision-making. Current research indicates that standardized college admissions tests (e.g., the Scholastic Aptitude Test (SAT), the American College Test (ACT)) predict about 10% to 30% of the variance in first year grade point average (Linn, 1990). Self-regulated learning variables such as motivation and cognitive strategy use (Pintrich, 1989) may enhance our assessment of learning potential. The purpose of this study was to assess the model fit of self-regulated learning variables and ACT in predicting first semester grade point average using structural equation modeling. Although chi-square and goodness of fit indices suggest a poor model fit, the self-regulated learning variables were able to contribute to the explained variance in grade point average above and beyond that of ACT. Future research needs to explore the relationships between self-regulated learning variables and their unique contribution to the prediction of academic performance in college. As more is discovered about self-regulated learning variables, educators can use this information to provide better instructional services to students.
Subject Area
Cognitive therapy|Higher education|Educational evaluation|Educational psychology
Recommended Citation
Naumann, Wendy Carole, "Predicting first-semester grade point average using self-regulated learning variables" (1998). ETD collection for University of Nebraska-Lincoln. AAI9902970.
https://digitalcommons.unl.edu/dissertations/AAI9902970