Toward An Early Risk Alert In A Distance Learning Context

The high failure rate is a common issue among online institutions. Early Warning Systems (EWSs) are widely adopted as a solution to deal with this issue. However, these systems do not go beyond the early identification of failing learners. In this paper, we propose a new alert algorithm of an educational EWS for generating risk alerts at the earliest. This algorithm is based on a weekly prediction model that aims to generate early alerts. The regular tracking of prediction results enabled to propose measures for the right prediction earliness and the model’s temporal stability. These measures prepare the last step of the algorithm which is the alerts generation according to a predefined rule. The objective of this rule is to target at-risk learners to improve their learning. For this aim, we used data of k-12 learners enrolled in an online physics-chemistry module.