Open AccessEmploying automatic content recognition for teaching methodology analysis in classroom videos
Author(s) -
Muhammad Aasim Rafique,
Faheem Khaskheli,
Malik Tahir Hassan,
Sheraz Naseer,
Moongu Jeon
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0263448
Subject(s) - adaptation (eye) , session (web analytics) , computer science , teaching method , work (physics) , multimedia , mathematics education , human–computer interaction , psychology , world wide web , mechanical engineering , neuroscience , engineering
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.