  Ranking and recommendation of multimedia content such as videos is usually
realized with respect to the relevance to a user query. However, for lecture
videos and MOOCs (Massive Open Online Courses) it is not only required to
retrieve relevant videos, but particularly to find lecture videos of high
quality that facilitate learning, for instance, independent of the video's or
speaker's popularity. Thus, metadata about a lecture video's quality are
crucial features for learning contexts, e.g., lecture video recommendation in
search as learning scenarios. In this paper, we investigate whether
automatically extracted features are correlated to quality aspects of a video.
A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed
regarding audio, linguistic, and visual features. Furthermore, a set of
cross-modal features is proposed which are derived by combining transcripts,
audio, video, and slide content. A user study is conducted to investigate the
correlations between the automatically collected features and human ratings of
quality aspects of a lecture video. Finally, the impact of our features on the
knowledge gain of the participants is discussed.
