Dynamic monitoring and optimization of teaching quality based on biomechanical models: A case study of private universities, with Shanghai Lida University as an example
Abstract
The quality of teaching in private higher education institutions has become a significant concern in recent years. Traditional evaluation methods, such as student surveys and academic performance, are often insufficient in capturing the full complexity of teaching effectiveness, particularly in terms of teacher-student interaction. This study proposes a novel approach for assessing and optimizing teaching quality at Shanghai Lida University, a private institution in China, by integrating biomechanical models to analyze non-verbal communication between teachers and students. A mixed-methods approach was adopted, combining survey data from 150 students and 20 teachers with biomechanical modeling techniques to evaluate the impact of teacher behaviors—such as gestures, eye contact, posture, and body movements—on student engagement. The findings reveal that teacher non-verbal communication, especially consistent eye contact and frequent use of hand gestures, significantly enhances student attentiveness and participation. Additionally, classroom environmental factors, such as lighting and temperature. They are found to influence student engagement levels. A multiple linear regression model identified teacher non-verbal behaviors and student engagement as the strongest predictors of teaching effectiveness. The study highlights the potential of biomechanical models to offer real-time insights into teacher-student interactions and presents actionable strategies for improving teaching practices. This research offers valuable contributions to the understanding and optimization of teaching quality in private universities.
References
1. Choi YS. Descriptive Characteristics of Systematic Functional Gestures Used by Pre-Service Earth Science Teachers in Classroom Learning Environments. Webpage: https://koreascience.kr/article/JAKO202428957832545.page
2. Shaghaghi S, Aliasghari P, Tripp B. Utilization of Non-verbal Behaviour and Social Gaze in Classroom Human-Robot Interaction Communications. Webpage: https://arxiv.org/abs/2312.06825
3. Hincapié M, Díaz CA, Valencia-Arias A. Using RGBD cameras for classifying learning and teacher interaction through postural attitude. Webpage: https://link.springer.com/article/10.1007/s12008-023-01262-3
4. Li S, Timmers R. Teaching and learning of piano timbre through teacher–student interactions in lessons. Webpage: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.576056/full
5. Kell C, Sweet J. Widening possibilities of interpretation when observing learning and teaching through the use of a dynamic visual notation. Webpage: https://www.tandfonline.com/doi/abs/10.1080/14703297.2016.1273789
6. Capel S, Whitehead M. Learning to teach physical education in the secondary school: A companion to school experience. Webpage: https://api.taylorfrancis.com/content/books/mono/download?identifierName=doi&identifierValue=10.4324/9781315767482&type=googlepdf
7. Saerbeck M, Schut T, Bartneck C. Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor. Webpage: https://dl.acm.org/doi/abs/10.1145/1753326.1753567
8. Vetter RE, Reusser JK. Learning to be an Effective Teacher: Strengthening Observation Skills. Webpage: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=10586288&AN=47480501
9. Kochman K, Moelants D, Leman M. Gesture as a communicative tool in vocal pedagogy. Webpage: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=27bf3e7b0bf37f3753ce4bd24acda1513307ab8e
10. Castañer M, Camerino O, Anguera MT, Jonsson GK. Kinesics and proxemics communication of expert and novice PE teachers. Webpage: https://link.springer.com/article/10.1007/s11135-011-9628-5
11. D’Mello S, Graesser A. AutoTutor and affective computing. Webpage: https://journals.sagepub.com/doi/10.3102/0034654314558493
12. Wang Y, Li X. Non-verbal communication and learning engagement. Webpage: https://www.sciencedirect.com/science/article/pii/S1871187118300857
13. Johnson S, Zhang M. The role of body language in teaching effectiveness. Webpage: https://www.sciencedirect.com/science/article/pii/S0959475219301322
14. Smith R, Brown T. How eye contact shapes student-teacher engagement. Webpage: https://onlinelibrary.wiley.com/doi/full/10.1111/bjep.12257
15. Harrison K, Patel S. The impact of teacher posture on student attention. Webpage: https://journals.sagepub.com/doi/10.1177/1474904118818538
16. Davis P, Keller J. Biomechanics and movement analysis in teaching. Webpage: https://journals.sagepub.com/doi/10.1177/0018726718802912
17. Wu Z, Lin H. Real-time monitoring of student attention in classrooms. Webpage: https://www.sciencedirect.com/science/article/pii/S0360131520301104
18. Garcia P, Moore L. Affective computing in education. Webpage: https://dl.acm.org/doi/abs/10.1145/3320486
19. Zhou L, Chen X. Machine learning for analyzing teacher gestures. Webpage: https://journals.sagepub.com/doi/10.1177/0165551519861883
20. Liang J, Wang S. Facial expressions and teacher-student interaction. Webpage: https://ieeexplore.ieee.org/document/8849466
21. Gomez J, Torres R. Multimodal learning and its effects on student cognition. Webpage: https://onlinelibrary.wiley.com/doi/full/10.1002/acp.3772
22. Nelson K, White M. Posture analysis and its role in teaching. Webpage: https://journals.sagepub.com/doi/10.1177/0956797619882024
23. Zhang L, Xu H. Analyzing the use of gesture and movement in classroom teaching. Webpage: https://www.tandfonline.com/doi/full/10.1080/01443410.2023.2168889
24. Wu D, Wang S, Liu Q, Abualigah L. An improved teaching-learning-based optimization algorithm with reinforcement learning strategy for solving optimization problems. Computational Intelligence and Neuroscience. 2022; 1535957.
25. Liu T, Gao Z, Guan H. Educational information system optimization for artificial intelligence teaching strategies. Mathematical Problems in Engineering. 2021; 5588650.
26. Ma Y, Zhang X, Song J, Chen L. A modified teaching–learning-based optimization algorithm for solving optimization problems. The Journal of Computational Science. 2021; 47: 101134.
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
