Predicting Knowledge Gain for MOOC Video Consumption

Christian Otto, Markos Stamatakis, Anett Hoppe, Ralph Ewerth

Abstract: Informal learning on the Web using search engines as well as more structured learning on Massive Open Online Course (MOOC) platforms have become very popular. However, the automatic assessment of this content with regard to the challenging task of predicting (potential) knowledge gain has not been addressed by previous work yet. In this paper, we investigate whether we can predict learning success after watching a specific type of MOOC video using 1) multimodal features, and 2) a wide range of text-based features describing the structure and content of the video. In a comprehensive experimental setting, we test four different classifiers and various feature subset combinations. We conduct a feature importance analysis to gain insights in which modality benefits knowledge gain prediction the most.

External IDs:doi:10.1007/978-3-031-11647-6_92