Prediction of students' assessment readiness in online learning environments

Abstract

Online learning environments are now pervasive in higher education. While not exclusively the case, in these environments, there is often modest teacher presence, and students are provided with access to a range of learning, assessment, and support materials. This places pressure on their study skills, including self-regulation. In this context, students may access assessment material without being fully prepared. This may result in limited success and, in turn, raise a significant risk of disengagement. Therefore, if the prediction of students' assessment readiness was possible, it could be used to assist educators or online learning environments to postpone assessment tasks until students were deemed ready. In this study, we employed a range of machine learning techniques with aggregated and sequential representations of students' behaviour in a Massive Open Online Course (MOOC), to predict their readiness for assessment tasks. Based on our results, it was possible to successfully predict students' readiness for assessment tasks, particularly if the sequential aspects of behaviour were represented in the model. Additionally, we used sequential pattern mining to investigate which sequences of behaviour differed between high or low level of performance in assessments. We found that a high level of performance had the most sequences related to viewing and reviewing the lecture materials, whereas a low level of performance had the most sequences related to successive failed submissions for an assessment. Based on the findings, implications for supporting specific behaviours to improve learning in online environments are discussed.