Students, coursework, and learning: a method for examining the interaction of student characteristics and classroom environment on general learned abilities

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1990
Authors
Oehler, David
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Abstract

The intent of this research was to identify variables with causal effect on undergraduate student cognitive learning. A Linear Structural Relationships (LISREL) causal path model was proposed based on prior research literature. This model combined research variables from studies using the institution as the unit of analysis with studies using the student as the unit of analysis. The methodology for defining the course as the unit of study was developed by the Differential Coursework Pattern (DCP) project at Iowa State University in 1988, initially funded by the Research Division, U.S. Department of Education Office of Educational Research and Improvement. The DCP project produced a model for clustering undergraduate coursework according to gains in student learning, as defined in terms of Graduate Record Examination (GRE) item-types. Coursework data taken from student transcripts were combined with GRE item-type residual gain scores to produce groupings of courses; they represented differences in student learning patterns;LISREL analysis of the causal model was unsuccessful; stepwise multiple linear regression was employed as an alternative data analysis technique. Regression equations were computed for each GRE item-type residual, identifying independent variables which were significant in predicting student cognitive outcomes;This study demonstrated the feasibility of using stepwise multiple linear regression to identify variables which contributed to student learning. Replication is needed to see if the same variables are consistently identified as contributing to student cognitive learning;One of the primary benefits of this study was to test a method for reducing the number of variables needed for study of the effects of college attendance on undergraduate student cognitive learning. With this method in place, non-significant variables can be eliminated so that more attention may be devoted to those which are understood or are yet to be discovered.

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Professional studies, Education, Higher education
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