Predicting Dropout in Programming MOOCs through Demographic Insights

Abstract

Massive Open Online Courses (MOOCs) have gained widespread popularity for their potential to offer education to an unlimited global audience. However, they also face a critical challenge in the form of high dropout rates. This paper addresses the need to identify students at risk of dropping out early in MOOCs, enabling course organizers to provide targeted support or adapt the course content to meet students’ expectations. In this context, zero-time dropout predictors, which utilize demographic data before the course commences, hold significant potential. Despite a lack of consensus in the existing literature regarding the efficacy of demographic data in dropout prediction, this study delves into this issue to contribute new insights to the ongoing discourse. Through an extensive review of prior research and a detailed analysis of data acquired from two programming MOOCs, we aim to shed light on the relationship between students’ demographic characteristics and their likelihood of early dropout from MOOCs, using logistic regression. This research extends the current understanding of the impact of demographic features on student retention. The results indicate that age, education level, student status, nationality, and disability can be used as predictors of dropout rate, though not in every course. The findings presented here are expected to affect the development of more effective strategies for reducing MOOC dropout rates, ultimately enhancing the educational experience for online learners.