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

Anna Wroblewska; Jozef Jasek; Bogdan Jastrzebski; Stanislaw Pawlak; Anna Grzywacz; Cheong Siew Ann; Tan Seng Chee; Tomasz Trzcinski; Janusz Holyst

doi:10.1007/978-3-031-11644-5_70

arXiv · 2205.14919

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

Anna Wroblewska·Jozef Jasek·Bogdan Jastrzebski·Stanislaw Pawlak·Anna Grzywacz·Cheong Siew Ann·Tan Seng Chee·Tomasz Trzcinski·Janusz Holyst

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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.

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Anna Wroblewska, Jozef Jasek, Bogdan Jastrzebski, Stanislaw Pawlak, Anna Grzywacz, Cheong Siew Ann, Tan Seng Chee, Tomasz Trzcinski, Janusz Holyst. 2022-05-30. A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing. https://doi.org/10.1007/978-3-031-11644-5_70

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