Interactive Approaches to Video Lecture Assessment

A growing number of universities and other educational institutions record videos of
regularly scheduled classes and lectures to provide students with additional
resources for their study. However, the video alone is not necessarily the same than
a carefully prepared educational video. The main issue is that they are typically
not post-processed in an editorial sense. That is, the videos often contain longer
periods of silence or inactivity, unnecessary repetitions, spontaneous interaction
with students, or even corrections of prior false statements or mistakes.
Furthermore, there is often no summary or table of contents of the video, unlike
with educational videos that supplement a certain curriculum and are well scripted
and edited. Thus, the plain recording of a lecture is a good start but far from a
good e-learning resource. This thesis describes a system that can close the gap
between a plain video recording and useful e-learning resource by producing
automatic summaries and providing an interactive lecture browser that can visualize
automatically extracted key phrases and their importance on an augmented time line.
The lecture browser depends on four tasks: automatic speech recognition, automatic
extraction and ranking of key phrases, extractive speech summarization, and the
visualization of the phrases and their salience. These tasks as well as the
contribution to the state of the art are described in detail and evaluated on a
newly acquired corpus of academic spoken English, the LMELectures. A first user
study shows that students using the lecture browser can solve a topic localization
task about 29 % faster than students that are provided with the video only.