Detection and Evaluation of Emotions in Massive Open Online Courses

Derick Leony (Universidad Carlos III de Madrid, Spain)

Pedro J. Muñoz-Merino (Universidad Carlos III de Madrid, Spain)

José A. Ruipérez-Valiente (Universidad Carlos III de Madrid, Spain)

Abelardo Pardo (University of Sydney, Australia)

Carlos Delgado Kloos (Universidad Carlos III de Madrid, Spain)

Abstract: Massive Open Online Courses (MOOCs) have grown up to the point of becoming a new learning scenario for the support of large amounts of students. Among current research efforts related to MOOCs, some are studying the application of well-known characteristics and technologies. An example of these characteristics is adaptation, in order to personalize the MOOC experience to the learner’s skills, objectives and profile. Several educational adaptive systems have emphasized the advantages of including affective information in the learner profile. Our hypothesis, based on theoretical models for the appraisal of emotions, is that we can infer the learner’s emotions by analysing their actions with tools in the MOOC platform. We propose four models, each to detect an emotion known to correlate with learning gains and they have been implemented in the Khan Academy Platform. This article presents the four models proposed, the pedagogical theories supporting them, their implementation and the result of a first user study.

Keywords: MOOC, affective computing, emotion detection, learning analytics, user modelling

Categories: J.4, L.2.2, L.3.6