Improving Students Performance in Small-Scale Online Courses - A Machine Learning-Based Intervention

Abstract

The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in-class teaching is becoming less popular with the young generation – the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses are higher than that of more traditional ones, and the reduced in-person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML)-based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML-based techniques requires a large amount of data seems to be a bottleneck when dealing with small-scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students’ overall performance but also that it could be used to propose timely intervention strategies to boost the students’ performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students’ progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies.