Title
Neural networks refined: Using a genetic algorithm to identify predictors of is student success
Abstract
In this study, a genetic algorithm trained neural network was used to identify relevant characteristics of successful IS students. Those variables identified as predictors of student success included a student's age, gender, and the Myers-Briggs' attributes of extraversion/introversion (EI) and sensing/intuition (SN). During the past decade, neural networks have gained popularity, as they work exceedingly well for mapping unknown functions from historical data. With a neutral network, a researcher can simply include a wide variety of inputs in the model and the neural network will learn to discriminate between the relevant inputs and those that are irrelevant. Since the majority of neural network research uses gradient search techniques, usually some form of backpropagation, there is no way to identify the inputs that actually contribute to the prediction. By using the genetic algorithm as an alternative search technique, these contributing relevant variables can be identified.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
Keywords
Artificial Intelligence, Genetic Algorithm, Myers-Briggs, Neural Networks
Publication Date
3-1-2001
Recommended Citation
Sexton, Randall S., Michael A. Hignite, Tom Margavio, and John Satzinger. "Neural networks refined: using a genetic algorithm to identify predictors of IS student success." Journal of Computer Information Systems 41, no. 3 (2001): 42-47.
Journal Title
Journal of Computer Information Systems
