Solving Mathematical Exercises: Prediction of Students' Success

In educational settings, recommender systems can help to choose the right exercises a student should be given for training. To make good decisions, the system should be able to estimate how successful a student would answer a recommended exercise. In this work, we study the performance of convolutional neural networks and collaborative filtering for estimating students’ success. We show that we can distinguish between correctly and wrong processed exercises with a precision of up to 64% while training on a small corpus of 712 user interactions.