Recommendation System for Student Academic Progress

The purpose of this work is to study the possible approaches to build a recommendation system that could help students in organizing their work and improving their results. More specifically, we intend to predict grades of a student for future exams, based on his/her previous results and the past grades received by all students from the same series/group. We have tried several machine learning methods for predicting future student grades, and finally we obtained good results, namely a mean absolute prediction error smaller than 1. The best variant proved to be the one based on neural networks that leads to a mean absolute prediction error smaller than 0.5. These results show the practical applicability of our proposed methodology, and consequently, we built, based on these, a practical recommendation system available to students as a web application.