A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing
This work addresses the need for automated lecture assessment in higher education, but it appears incremental as it applies existing methods to a new dataset without claiming major breakthroughs.
The paper tackled the problem of automatically assessing academic lectures by detecting qualitative features from video, using machine learning and computer vision techniques, with results indicating potential usefulness.
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. We follow this research path, and in this work, we explore 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 collected for this purpose. 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.