Opportunities-to-Learn and Motivation in Science: Examining Person and Context Factors That Predict Science Achievement

• In Event: Division C Outstanding Graduate Student Research Posters

Abstract

Drawing from opportunity-propensity frameworks of student achievement by using the TIMSS 2015 dataset, this study explored a motivation (i.e., science learning propensity) factor structure (CFA) that is aligned to key constructs in motivation theories. We further examined relationships among opportunity factors, student motivation , and antecedent variables using SEM to understand person and context-factors that predict science achievement. Results show that student self-concept in science is the strongest predictor of science achievement, followed by utility value, relatedness, and intrinsic value after controlling for student characteristics. Surprisingly, among the opportunity factors, student rating instructional quality is found to be a negative predictor of student science achievement, as well as internet access to course materials and to find information in science.

Authors

• Fa Zhang, Virginia Commonwealth University

• Christine Lee Bae, Virginia Commonwealth University