Clustering moodle data as a tool for profiling students

This paper describes the first step of a research project with the aim of predicting students' performance during an online curriculum on a LMS and keeping them from falling behind. Our research project aims to use data mining, machine learning and artificial intelligence methods for monitoring students in e-learning trainings. This project takes the shape of a partnership between computer science / artificial intelligence researchers and an IT firm specialized in e-learning software. We wish to create a system that will gather and process all data related to a particular e-learning course. To make monitoring easier, we will provide reliable statistics, behaviour groups and predicted results as a basis for an intelligent virtual tutor using the mentioned methods. This system will be described in this article. In this step of the project, we are clustering students by mining Moodle log data. A first objective is to define relevant clustering features. We will describe and evaluate our proposal. A second objective is to determine if our students show different learning behaviours. We will experiment whether there is an overall ideal number of clusters and whether the clusters show mostly qualitative or quantitative differences. Experiments in clustering were carried out using real data obtained from various courses dispensed by a partner institute using a Moodle platform. We have compared several classic clustering algorithms on several group of students using our defined features and analysed the meaning of the clusters they produced.