Data Mining Algorithms to Classify Students

TLDR

The study compares data mining methods for classifying students using Moodle usage data and final grades. The authors applied various data mining techniques to real Moodle data from seven courses, using discretization and rebalancing preprocessing to evaluate classifier performance. They developed a tool that simplifies configuration and execution of data mining techniques and argue that effective educational classifiers must be both accurate and interpretable for instructors.

Abstract

In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining techniques easier for instructors. We have used real data from seven Moodle courses with Cordoba University students. We have also applied discretization and rebalance preprocessing techniques on the original numerical data in order to verify if better classifier models are obtained. Finally, we claim that a classifier model appropriate for educational use has to be both accurate and comprehensible for instructors in order to be of use for decision making.