Employing automatic content recognition for teaching methodology analysis in classroom videos

Muhammad Aasim Rafique; Faheem Khaskheli; Malik Tahir Hassan; Sheraz Naseer; Moongu Jeon

doi:10.1371/journal.pone.0263448

Employing automatic content recognition for teaching methodology analysis in classroom videos

PLoS One. 2022 Feb 17;17(2):e0263448. doi: 10.1371/journal.pone.0263448. eCollection 2022.

Authors

Muhammad Aasim Rafique¹, Faheem Khaskheli², Malik Tahir Hassan³, Sheraz Naseer², Moongu Jeon¹

Affiliations

¹ School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
² Department of Computer Sciences, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.
³ Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.

Abstract

A teacher plays a pivotal role in grooming a society and paves way for its social and economic developments. Teaching is a dynamic role and demands continuous adaptation. A teacher adopts teaching techniques suitable for a certain discipline and a situation. A thorough, detailed, and impartial observation of a teacher is a desideratum for adaptation of an effective teaching methodology and it is a laborious exercise. An automatic strategy for analyzing a teacher's teaching methodology in a classroom environment is suggested in this work. The proposed strategy recognizes a teacher's actions in videos while he is delivering lectures. In this study, 3D CNN and Conv2DLSTM with time-distributed layers are used for experimentation. A range of actions are recognized for a complete classroom session during experimentation and the reported results are considered effective for analysis of a teacher's teaching technique.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Deep Learning*
Employment*
Humans
Interpersonal Relations
Learning*
Neural Networks, Computer*
Pattern Recognition, Visual
Students / psychology*
Teaching / statistics & numerical data*
Videotape Recording / methods*

Grants and funding

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2014-3-00077, AI National Strategy Project).