NDLI: Work in progress — Prediction of students' academic performance in an introductory engineering course

Content Provider	IEEE Xplore Digital Library
Author	Shaobo Huang Ning Fang
Copyright Year	2011
Description	Author affiliation: Department of Engineering and Technology Education, Utah State University (Shaobo Huang; Ning Fang)
Abstract	Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students will perform, so instructors can take proactive measures to improve student learning. This paper reports our recent ongoing efforts that focus on developing a predictive model to predict students' academic performance in an introductory engineering course titled Engineering Dynamics. A total of 2,151 data points were collected from 239 undergraduate students in three semesters. Four predictive models were developed using multivariate linear regression (MLR), multilayer perceptron (MLP) neural networks, radial basis function (RBF) neural networks, and support vector machines (SVMs), respectively. The results show that in many cases, the support vector machine model generates the overall best predictions: The average prediction accuracy is 89.0%-90.9% and good predictions are 62.3%–69.0%.
File Size	322054
File Format	PDF
ISBN	9781612844688
ISSN	01905848
e-ISBN	9781612844695
e-ISBN	9781612844671
DOI	10.1109/FIE.2011.6142729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Support vector machines Accuracy Neural networks Computational modeling Calculus Linear regression support vector machines Engineering dynamics multivariate linear regression neural networks
Content Type	Text
Resource Type	Article
Subject	Education Computer Science Applications Software

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