Modeling student retention in science and engineering disciplines using neural networks
Modeling student retention in science and engineering disciplines using neural networks
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DOI：
10.1109/educon.2011.5773209
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发表时间：
2011-04
期刊：
影响因子：
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通讯作者：
Ruba Alkhasawneh;Rosalyn S. Hobson
中科院分区：
文献类型：
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作者：
Ruba Alkhasawneh;Rosalyn S. Hobson
几十年来，吸引更多的学生进入科学和工程学科让许多研究人员感到担忧。文献使用传统的统计方法和定性技术来确定对学生保持能力影响最大的因素，并预测他们的坚持能力。本文利用前馈反向传播网络建立了两个预测理工科学生记忆的神经网络模型。第一个模型被用来预测新生的保留率，并确定相关的大学前因素。第二种模式是将新生群体分为三类：高危、中级和高级学生。总共使用了338个样本，70.1%的学生分类正确。
Attracting more students into science and engineering disciplines concerned many researchers for decades. Literature used traditional statistical methods and qualitative techniques to identify factors that affect student retention up most and predict their persistence. In this paper we developed two neural network models using a feed-forward backpropagation network to predict retention for students in science and engineering fields. The first model is used to predict incoming freshmen retention and identify correlated pre-college factors. The second model is to classify freshmen groups into three classes: at-risk, intermediate, and advanced students. With total of 338 samples used, 70.1% of students classified correctly.