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Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models by Shaobo Huang; Ning Fang is a Social Sciences article available to read on EtoBox.

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Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamicsa high-enrollment, highimpact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student's cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students' scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematic

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Author

Shaobo Huang; Ning Fang

Publisher

Elsevier BV

Published

2013

Language

EN

Field

Social Sciences