Analysis of ODL Logs for Predicting Recession

Abstract

This article presents preliminary results from a study to establish practicability of learning analytics in Open and Distance Learning (ODL) environment. Undesirably, a significant number of ODL students take longer to complete studies, exhibiting what can be referred to as learning recession. Meanwhile, learning institutions lack timely means to predict if a student’s learning is receding. In the study, attributes for potential use in developing prediction models are being engineered. In a preliminary stage, results of learning analytics have confirmed some facts and depicted interesting patterns. These preliminary results are expected to enable further studies on development of relevant predictive models.