Neural networks refined: Using a genetic algorithm to identify predictors of is student success


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.


Information Technology and Cybersecurity

Document Type



Artificial Intelligence, Genetic Algorithm, Myers-Briggs, Neural Networks

Publication Date


Journal Title

Journal of Computer Information Systems