TY - GEN
T1 - A multimodal framework for automated teaching quality assessment of one-to-many online instruction videos
AU - Pan, Yueran
AU - Wu, Jiaxin
AU - Ju, Ran
AU - Zhou, Ziang
AU - Gu, Jiayue
AU - Zeng, Songtian
AU - Yuan, Lynn
AU - Li, Ming
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.
AB - In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.
KW - Emotion Recognition
KW - Interptretable Feature Extraction
KW - Multi-modal Behavior Coding
KW - Speaker Diarization
KW - Teaching Quality Assessment
UR - http://www.scopus.com/inward/record.url?scp=85143639597&partnerID=8YFLogxK
U2 - 10.1109/ICPR56361.2022.9956185
DO - 10.1109/ICPR56361.2022.9956185
M3 - Conference contribution
AN - SCOPUS:85143639597
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1777
EP - 1783
BT - 2022 26th International Conference on Pattern Recognition, ICPR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Pattern Recognition, ICPR 2022
Y2 - 21 August 2022 through 25 August 2022
ER -