Using artificial neural nets to predict academic performance
Artificial neural networks (ANN) are used to predict 1) degree program completion, 2) earned hours, and 3) GPA for college students. The feed forward neural net architecture is used with the back propagation learning function and the logistic activation function. The database used for training and validation consisted of 17,476 student transcripts from Fall 1983 through Fall 1994. It is shown that age, gender, race, ACT scores, and reading level are significant in predicting the degree program completion, earned hours, and GPA. Of the three, earned hours proved the most difficult to predict.