Intelligent System for Personalised Interventions and Early Drop-out Prediction in MOOCs

Abstract

In this paper, we propose an approach to early detect students at high risk of drop-out in MOOC (Massive Open Online Course); we design personalised interventions to mitigate that risk. We apply Machine Learning (ML) algorithms and data mining techniques to a dataset extracted from XuetangX MOOC learning platforms and sourced from the KDD cup 2015. Since this dataset contains only raw student log activity records, we perform a hybrid feature selection and dimensionality reduction techniques to extract relevant features, and reduce models complexity and computation time. Besides, we built two models based on: Genetic Algorithms (GA) and Deep Learning (DL) with supervised learning methods. The obtained results, according to the accuracy and the AUC (Area Under Curve)-ROC (Reciever Operator Characteristic) metrics, prove the pertinence of the extracted features and encourage the use of the hybrid features selection. They also proved that GA and DL are outperforming the baseline algorithms used in related works. To assess the generalisation of the approach used in this work, The same process is performed to a second benchmark dataset extracted from the university MOOC. Then, a single web application hosted on the university server, produces an individual weekly drop-out probability, using time series data. It also proposes an approach to personalise and prioritise interventions for at-risk students according to the drop-out patterns.