Modeling student retention in science and engineering disciplines using neural networks
- Resource Type
- Conference
- Authors
- Alkhasawneh, Ruba; Hobson, Rosalyn
- Source
- 2011 IEEE Global Engineering Education Conference (EDUCON) Global Engineering Education Conference (EDUCON), 2011 IEEE. :660-663 Apr, 2011
- Subject
- General Topics for Engineers
Engineering Profession
Educational institutions
Artificial neural networks
Biological system modeling
Predictive models
Mathematical model
Accuracy
Data mining
retention
S&E
neural networks
modeling
classification
- Language
- ISSN
- 2165-9559
2165-9567
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.