Study of artificial intelligence face recognition technology for real-time assessment of medical students' classroom concentration

doi:10.3760/cma.j.cn115259-20220516-00628

Chinese Journal of Medical Education ›› 2023, Vol. 43 ›› Issue (1): 31-34.DOI: 10.3760/cma.j.cn115259-20220516-00628

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Study of artificial intelligence face recognition technology for real-time assessment of medical students' classroom concentration

Yang Jingwen¹, Yang Zonghan², Li Jian¹, Liu Yunsong¹

¹Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China;
²Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received:2022-05-16 Online:2023-01-01 Published:2022-12-29
Contact: Liu Yunsong,Email:kqliuyunsong@163.com
Supported by:
Teaching Reform Funding Project of Peking University School and Hospital of Stomatology (2020-PT-10); 2022 “New Ideas in Teaching 2.0”in Peking University(YX22003); Peking University HSC Medical Education Research Funding Project (2022ZD05)

Abstract

Abstract: Objective To explore the feasibility of artificial intelligence face recognition technology (referred to as face recognition technology) in evaluating medical students' classroom concentration in real time. Methods In October 2021, 80 students of five-year and eight-year majors in stomatology enrolled in 2018 in Peking University School of Stomatology were selected as the research participants. Their classroom videos of 8 class hours in the dental anatomy course were collected, and face recognition technology was used. Perform face analysis on the pictures converted from video data (extract one frame per second) to evaluate the number of students who are “focused” in real time; a random function selects 1/5 of the entire picture data as the detection data, and judging whether the student is listening to the class attentively according to the naked eye observation of the student's facial state by the teachers, to carry out the manual identification of “focused” students, calculate the accuracy and recall rate of face recognition. Wilcoxon test was used to analyze the difference between face recognition technology and human recognition. The consistency of face recognition and evaluator recognition results was tested by intraclass correlation coefficient (ICC). Results Artificial intelligence recognized the face of “focused” students with 21 600 pictures, and 4 320 pictures were used as detection data sets for manual marking. The accuracy of face recognition was 90.4% (41 288/45 668), and the recall rate was 81.4% (41 228/50 648). There was no significant difference between face recognition (378 830 person times) and manual recognition (345 689 person times); They were moderately correlated (ICC=0.65, P=0.026). Conclusions Face recognition technology can effectively judge the concentration of students in the classroom and can provide an effective technical support for real-time evaluation of teaching effect.

Key words: Students, medical, Real-time evaluation, Concentration, Teaching evaluation, Face recognition, Artificial intelligence

CLC Number:

Yang Jingwen, Yang Zonghan, Li Jian, Liu Yunsong. Study of artificial intelligence face recognition technology for real-time assessment of medical students' classroom concentration[J]. Chinese Journal of Medical Education, 2023, 43(1): 31-34.

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Study of artificial intelligence face recognition technology for real-time assessment of medical students' classroom concentration

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