A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing

TL;DR

This paper presents a machine learning and computer vision-based method for automatically detecting qualitative features of academic lectures to improve teaching assessment and enhancement.

Contribution

It introduces a novel set of qualitative features for lectures and demonstrates automatic detection using AI techniques, advancing lecture analysis methods.

Findings

Successful annotation of lecture videos with qualitative features

Effective automatic detection of features using machine learning

Potential for improving lecture quality assessment

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. 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.