Work in progress: Early prediction of students' academic performance in an introductory engineering course through different mathematical modeling techniques

Shaobo Huang, N. Fang
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引用次数: 15

Abstract

This paper presents our ongoing efforts in developing mathematical models to make early predictions, even before the semester starts, of what score a student will earn in the final comprehensive exam of the engineering dynamics course. A total of 1,938 data records were collected from 323 undergraduates in four semesters. Employed were four different mathematical modeling techniques: multivariate linear regression, multilayer perceptron neural networks, radial basis function neural networks, and support vector machines. The results show that within the five predictor variables investigated in this study, there is no significant difference in the prediction accuracy of these four mathematical models.
正在进行的工作:通过不同的数学建模技术对工程入门课程中学生的学习成绩进行早期预测
本文介绍了我们在开发数学模型方面所做的持续努力,甚至在学期开始之前,就可以对学生在工程动力学课程的期末综合考试中获得的分数进行早期预测。在四个学期中,从323名本科生中收集了1938条数据记录。采用了四种不同的数学建模技术:多元线性回归、多层感知器神经网络、径向基函数神经网络和支持向量机。结果表明,在本研究研究的5个预测变量内,这4种数学模型的预测精度没有显著差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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