The impact of ergonomics and biomechanics on optimizing learning environments in higher education management

  • Kang Liu Faculty of Education, Lomonosov Moscow State University, 119991 Moscow, Russia
  • Yiwen Zhou Zhejiang Ocean University, Zhoushan 316000, China
Keywords: biomechanical models; biomechanical principles; ergonomic design; biomechanical interventions; physical strain; learning environment; postural alignment; muscle activity
Ariticle ID: 396

Abstract

In higher education, the design of learning environments is serious in prompting student well-being, engagement, and academic performance. Traditional classrooms often lack ergonomic consideration, leading to discomfort, increased physical strain, and reduced concentration. As education evolves, there is a growing need to apply ergonomic and biomechanical principles to create spaces that accommodate students’ diverse physical and cognitive needs. Despite the theoretical support for these interventions, there is limited empirical evidence on their practical impact in educational settings. This study addresses this gap by examining the effects of ergonomic and biomechanical adjustments on student outcomes in higher education. Utilizing a mixed-methods approach, the research was conducted across four universities with a diverse sample of 126 students. The interventions included adjusting furniture, optimized spatial layouts, and environmental adjustments to assess their influence on postural alignment, muscle activity, and engagement. Key findings revealed significant improvements: postural alignment showed an increase in spinal angle from 118° to 133° and a reduction in neck angle from 37° to 29°. Muscle activity, particularly in the neck and lower back, decreased by 40% and 44%, respectively. Additionally, self-reported comfort improved from a mean of 2.8 to 4.3, while physical strain decreased from 3.7 to 2.2. Engagement levels also improved, with scores rising from 3.1 to 4.5. These results underscore the importance of ergonomic design in promoting student well-being and fostering a more conducive learning environment, providing evidence-based recommendations for optimizing learning spaces in higher education.

References

1. Ribeiro-Silva, E., Amorim, C., Aparicio-Herguedas, J. L., & Batista, P. (2022). Trends of active learning in higher education and students’ well-being: A literature review. Frontiers in Psychology, 13, 844236.

2. Ogunmokun, O. A., Unverdi‐Creig, G. I., Said, H., Avci, T., & Eluwole, K. K. (2021). Consumer well‐being through engagement and innovation in higher education: A conceptual model and research propositions. Journal of Public Affairs, 21(1), e2100.

3. Papaioannou, G., Volakaki, M. G., Kokolakis, S., & Vouyioukas, D. (2023). Learning spaces in higher education: a state-of-the-art review. Trends in Higher Education, 2(3), 526–545.

4. Akinbami, A. A. (2024). Integrating Natural Light for Wellbeing, Performance, and Quality Care Delivery in Healthcare Environments.

5. Wahid, S. J. (2023). Teacher Perceptions of Dynamic Seating in the Elementary Classroom. Regent University.

6. Owen, J. (2024). Topic: Biomechanical Analysis Model for Ergonomic Design.

7. Gumasing, M. J. J., & Castro, F. M. F. (2023). Determining ergonomic appraisal factors affecting the learning motivation and academic performance of students during online classes: Sustainability, 15(3), 1970.

8. Cheng, E. S. W., Lai, D. K. H., Mao, Y. J., Lee, T. T. Y., Lam, W. K., Cheung, J. C. W., & Wong, D. W. C. (2023). Computational Biomechanics of Sleep: A Systematic Mapping Review. Bioengineering, 10(8), 917.

9. Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391

10. Kristof-Brown, A., & Guay, R. P. (2011). Person–environment fit. In S. Zedeck (Ed.), APA handbook of industrial and organizational psychology, Vol. 3. Maintaining, expanding, and contracting the organization (pp. 3–50). American Psychological Association.

11. Harvey, Craig & Koubek, R. & Rothrock, Ling & Darisipudi, A. & Kim, Jong W & Munch, J.. (2005). Cognitive Ergonomics.

12. Daniel, R. M. (2024). Measure and Mis-Measure: Rethinking Anthropometry in Interior Design. Journal of Interior Design, 49(1), 17–34.

13. Abdul Latip, M. S., Abdul Latip, S. N. N., Tamrin, M., & Rahim, F. A. (2024). Modeling physical ergonomics and student performance in higher education: the mediating effect of student motivation. Journal of Applied Research in Higher Education.

14. Ighrakpata, F. C., Akpaokueze, T. N., Ukpene, C. P., & Molua, O. C. Biomechanics of Ergonomic Furniture Design: Integrating Physics, Biology and Home Science for Improved Posture and Well-being.

15. Yough, M. (2023). Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices (Doctoral dissertation, West Virginia University).

16. Kim, H., Park, C., & You, J. S. H. (2024). Sustainable effectiveness of kinetic chain stretching on active hip flexion movement and muscle activation for hamstring tightness: A preliminary investigation. Technology and Health Care, (Preprint), 1–13.

17. Wickett, D. (2023). Development, validation, and application of a biomechanical model of reclined sitting posture (Doctoral dissertation, Anglia Ruskin Research Online (ARRO)).

18. Baum, C. M., Bass, J. D., & Christiansen, C. H. (2024). Theory, models, frameworks, and classifications. In Occupational Therapy (pp. 23–46). Routledge.

19. Hochhauser, M., & Liberman, E. (2024). Health status and ergonomics education: A comparison between student nurses and first‐year nursing staff. Nursing Open, 11(7), e2239.

20. Pătroc, D. (2023). Architectural evolution in education: shaping 21st-century learning spaces. education and applied didactics, 7(2), 7–21.

21. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI:10.3390/s23094365.

22. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI:10.1109/ACCESS.2023.3236843.

23. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI:10.1007/s10772-022-09960-1.

24. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI:10.1016/j.seta.2021.101601.

25. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI:10.1016/j.avb.2021.101633.

26. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI:10.1007/s11042-021-10842-y.

27. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI:10.1007/s11277-021-08333-2.

28. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI:10.1007/s12652-020-02869-0.

29. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors and Microsystems, 83, 2021, 104030, DOI:10.1016/j.micpro.2021.104030 .

30. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.

31. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563–574, https://doi.org/10.53759/7669/jmc202404054.

32. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x

33. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI:10.1007/978-981-19-7455-7_57.

Published
2024-11-14
How to Cite
Liu, K., & Zhou, Y. (2024). The impact of ergonomics and biomechanics on optimizing learning environments in higher education management. Molecular & Cellular Biomechanics, 21(3), 396. https://doi.org/10.62617/mcb396
Section
Article