The declining rate of student graduation in today's higher institutions of learning has become a major source of concern to educational authorities, school administration and parents. While school administrators are trying to increase the... more
The declining rate of student graduation in today's higher institutions of learning has become a major source of concern to educational authorities, school administration and parents. While school administrators are trying to increase the rate of graduation, students are dropping out at an alarming rate. The ability to correctly predict student's graduation time after admission into graduate program is critical for educational institutions because it allows for developing strategic programs that will aid or improve students' performances towards graduating on time (GOT). This paper explores predictive nature of artificial neural networks (ANN) to design a model based on cognitive and non-cognitive measures of students, together with background information, in order to predict students' graduation time. Synthetic data was used to test and verify the effectiveness of the proposed model. The results shows that artificial neural network is a promising tool for prediction.
