Predicting students' performance using artificial neural networks

Περίληψη

Artificial intelligence has enabled the development of more sophisticated and more efficient student models which represent and detect a broader range of student behavior than was previously possible. In this work, we describe the implementation of a user-friendly software tool for predicting the students' performance in the course of “Mathematics” which is based on a neural network classifier. This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or weak students who are likely to have low achievements.