pisco_log
banner

Application and Effect Analysis of "Task-driven + Case Analysis" Blended Teaching in Children's Rehabilitation Course

Shuijing He, Yue Liu, Zhenlei Lyu, Haijiao Bao, Xianghua Li

Abstract


Objective: To improve teaching quality and learning efficiency and interest of vocational undergraduate students, this study explored the application of a "task-driven+case analysis" blended teaching method in children's rehabilitation courses. Methods: A total of 150
rehabilitation students were divided into an experimental group (76 students, blended teaching) and a control group (74 students, traditional
teaching), with learning outcomes and satisfaction compared. Results: The experimental group achieved significantly higher theoretical
scores and better classroom performance (P<0.05), with higher learning interest and teaching satisfaction. Conclusion: This blended teaching method effectively improves students' clinical analysis and teamwork abilities, and is worthy of promotion in children's rehabilitation and
other rehabilitation-related courses.

Keywords


Task-driven; Case analysis; Blended teaching; Pediatric rehabilitation; Teaching reform

Full Text:

PDF

Included Database


References


[1] Meng L, Wei Q, Tsang R C C, et al. Pediatric Rehabilitation Therapy and Physiotherapy Education in China[J]. Physical & Occupational

Therapy in Pediatrics, 2023, 43(1): 93-108.

[2] Xiong Y, Wu N, Zhang Z, et al. Blended learning in practice-intensive medical education: A qualitative exploration of undergraduate

medical imaging technology students' experiences[J]. Radiography, 2025, 31(5): 103115.

[3] Li M, Hong Y, Wu A, et al. The effectiveness of blended learning in nursing and medical education: An umbrella review[J]. Nurse Education in Practice, 2025: 104421.

[4] Horrocks J. Student-led peer marking criteria[J]. 100 Ideas for Active Learning, 2022.

[5] Onyia O P, Allen S. Peer Assessments of GPW: Infusing fairness into students' assessment of peer contributions[J]. Research in Higher

Education, 2012, 17(September).

[6] Jin Y, Li X, Cao F, et al. Multidimensional Rubric-oriented Reward Model Learning via Geometric Projection Reference Constraints[J].

arXiv preprint arXiv:2511.16139, 2025.

[7] Komolafe O O, Mustofa J, Daley M J, et al. Current applications and outcomes of AI-driven adaptive learning systems in physical rehabilitation science education: A scoping review protocol[J]. PLoS One, 2025, 20(6): e0325649.




DOI: http://dx.doi.org/10.70711/neet.v4i2.8692

Refbacks

  • There are currently no refbacks.