Deep Learning for Automatic Detection of Qualitative Features of Lecturing

Anna Wróblewska, Jozef Jasek, Bogdan Jastrzebski, Stanislaw Pawlak, Anna Grzywacz, Siew Ann Cheong, Seng Chee Tan, Tomasz Trzcinski, Janusz A. Holyst

Abstract: Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students’ works or even providing directions to the teachers on how to enhance the lectures. This research explores how an academic lecture can be assessed automatically by quantitative features. First, we prepare a set of qualitative features based on teaching practices and then annotate the dataset of academic lecture videos. We then show how these features could be detected automatically using machine learning and computer vision techniques. Our results show the potential usefulness of our work.