Exploiting Time in Adaptive Learning from Educational Data

Abstract

Virtual Learning Environments (VLEs) are web platforms where educational content is delivered, along with tools to support individual study. Logs that record how students interact with the platform are collected daily, so automated methods can be used to extract useful knowledge from these data. All stakeholders involved in the learning activities of the VLEs, especially students and teachers, can benefit from the insights derived from the educational data and valuable information can be extracted using machine learning algorithms. Usually, educational data are examined as stationary data using conventional batch methods. However, these data are non-stationary by nature and could be better treated as data streams. This paper reports the results of a classification study in which Random Forests, applied in both batch and adaptive mode, are used to build a model for predicting student exam failure/success. In addition, an analysis of the most important features is performed to detect the most discriminating attributes related to the student’s result. Experiments conducted on a subset of the Open University Learning Analytics (OULAD) dataset demonstrate the reliability of the adaptive version of Random Forest in accurately classifying the evolving educational data.