  Informal learning on the Web using search engines as well as more structured
learning on MOOC platforms have become very popular in recent years. As a
result of the vast amount of available learning resources, intelligent
retrieval and recommendation methods are indispensable -- this is true also for
MOOC videos. However, the automatic assessment of this content with regard to
predicting (potential) knowledge gain has not been addressed by previous work
yet. In this paper, we investigate whether we can predict learning success
after MOOC video consumption using 1) multimodal features covering slide and
speech content, and 2) a wide range of text-based features describing the
content of the video. In a comprehensive experimental setting, we test four
different classifiers and various feature subset combinations. We conduct a
detailed feature importance analysis to gain insights in which modality
benefits knowledge gain prediction the most.
