Assessing prior knowledge types as predictors of academic achievement in the introductory phase of biology and physics study programmes using logistic regression

Increasingly, high dropout rates in science courses at colleges and universities have led to discussions of causes and potential support measures of students. Students’ prior knowledge is repeatedly mentioned as the best predictor of academic achievement. Theory describes four hierarchically ordered types of prior knowledge, from declarative knowledge of facts to procedural application of knowledge. This study explores the relevance of these four prior knowledge types to academic achievement in the introductory phase of the two science subjects, biology and physics.

We assessed the knowledge types at the beginning and student achievement (measured by course completion) at the end of the first study year. We applied logistic regression models to evaluate the relationship between the knowledge types and academic achievement. First, we controlled for a well-established predictor of academic achievement (high school grade point average). Second, we added the knowledge types as predictors. For biology, we found that only knowledge about principles and concepts was a significant predictor in the first year. For physics, knowledge about concepts and principles as well as the ability to apply knowledge to problems was related to academic achievement.

Our results concerning the knowledge types, which are of special relevance in biology and physics studies, could lead to effective measures, e.g. for identifying at-risk students and course guidance. Furthermore, the results provide a profound starting point for controlled intervention studies that systematically foster the identified relevant knowledge types in each subject and aim at a theory- and empirical-based optimization of pre- and introductory courses.


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