USE OF A NEURAL NETWORK MODEL AND NONCOGNITIVE MEASURES TO PREDICT STUDENT MATRICULATION IN ENGINEERING

摘要

Engineering students' affective self-beliefs prior to their first year have the potential to help researchers better understand various issues related to student retention and engagement. This paper examines whether a neural network model based on student noncognitive characteristics can be used to predict student persistence in engineering, and the influence of gender in the predictive model. Eight noncognitive measures (i.e., academic self-efficacy, academic motivation, leadership, metacognition, career, type of learner (e.g., deep vs. surface), teamwork, and expectancy-value) serve as independent parameters to an artificial neural network (NN) that is used to predict student persistence within engineering school at the end of first year. A feed-forward neural network model with back-propagation training was developed to predict third semester retention of a cohort of first-year engineering students (N=1,523) at a large Midwestern university. The model constituted of 159 primary nodes corresponding to 8 noncognitive factors described by a 159 item instrument. The resulting model was shown to have a predicative accuracy of 82% for retained students after their first year and 30% for non-retained students. Significantly decreasing the number of inputs (i.e., only using those items that appeared to have the strongest influence) had little impact on the predicative accuracy of the retained students. However, the reduction in inputs decreased the predictive accuracy of the non-retained students by approximately 10%. Results for the same cohort also indicate that the neural network prediction rate is independent of gender.