On the Need for Fine-Grained Analysis of Gender Versus Commenting Behaviour in MOOCs

Abstract

Stereotyping is the first type of adaptation ever proposed. However, the early systems have never dealt with the numbers of learners that current Massive Open Online Courses (MOOCs) provide. Thus, the umbrella question that this work tackles is if learner characteristics can predict their overall, but also fine-grain behaviour. Earlier results point at differences related to gender or to age. Here, we analyse gender versus commenting behaviour. Our fine-grained analysis shows that the result may further depend on the course topic, or even week. Surprisingly, for instance, women chat less in a Psychology-related course, but more (or similar) on a Computer Science course. These results are analysed in this paper in details, including two different methods of averaging comments, leading to remarkably different results. The outcomes can help in informing future runs, in terms of potential personalised feedback for teachers and students.