Using neural networks to predict student´s performance

This paper presents a first step towards an intelligent problem selection agent for the SQL-Tutor Intelligent Tutoring system. Currently SQL-Tutor uses an overly simple problem selection strategy, which selects a problem based on a single construct the student has most problems with. This strategy very often results in problems that are too easy/difficult for the student. Here we propose an intelligent problem-selection agent, which identifies the appropriate problem for a student in two stages. It firstly predicts the number of errors the student will make on a set of problems, and then in the second stage decides on a suitable problem for the student. In order to develop such an agent, we trained a feed-forward, backpropagation neural network to predict the number of errors a student will make. The achieved prediction accuracy is high, showing that a neural network is capable of making such predictions. However, the developed network cannot be used on-line, as it requires values that are not readily available. We present the plan for developing a modified network and for completing the problem selection agent.