June 24, 2007
June 24, 2007
June 27, 2007
Educational Research and Methods
12.1522.1 - 12.1522.11
Use of a Neural Network Model and Noncognitive Measures to Predict Student Matriculation in Engineering
Abstract 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.
Introduction Engineering programs typically attract the top graduates from high school in terms of grade point average (GPA) and standardized test scores, but attrition out of engineering continues to be a major issue; programs often see some of the most statistically qualified students leave engineering for other majors or drop out of college altogether. In 1975, attrition in the freshman year in engineering was about 12%, increasing to about 25% by 1990 (Beaufait, 1991). In a large study of over 25,000 students at over 300 universities, Astin (1993) found that only 47% of students who begin in engineering graduate with an engineering degree. The National Academies’ report “Rising Above The Gathering Storm: Energizing and Employing America for a Brighter Economic Future” reports that undergraduate programs in science and engineering have the lowest retention rate among all academic disciplines. The National Academies describes the importance of advances in engineering and technology as crucial to the social and economic conditions for the United States to compete, prosper, and be secure in the global community in the 21st century (Augustine, 2005).
Imbrie, P., & Lin, J. J., & Oladunni, T., & Reid, K. (2007, June), Use Of A Neural Network Model And Noncognitive Measures To Predict Student Matriculation In Engineering Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. https://peer.asee.org/3029
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