TY - GEN
T1 - Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style
AU - Weng, Jinta
AU - Dong, Haoyu
AU - Deng, Yifan
AU - Hu, Yue
AU - Wu, Hao
AU - Huang, Heyan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.
AB - AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.
UR - http://www.scopus.com/inward/record.url?scp=85187252203&partnerID=8YFLogxK
U2 - 10.1109/SMC53992.2023.10394425
DO - 10.1109/SMC53992.2023.10394425
M3 - Conference contribution
AN - SCOPUS:85187252203
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2421
EP - 2426
BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Y2 - 1 October 2023 through 4 October 2023
ER -