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Integrating active learning and digital tools in pathophysiology education: enhancing conceptual understanding and clinical application

Sharipova Postumia Anvarovna
Associate professor, Department of Physiology and Pathology, Tashkent State Dental Institute, Tashkent, Uzbekistan
Mirzaakhmedova Nilufar Askarovna
Assistent, Department of Physiology and Pathology, Tashkent State Dental Institute, Tashkent, Uzbekistan
Shagulyamova Kamola Lutfullayevna
Assistent, Department of Physiology and Pathology, Tashkent State Dental Institute, Tashkent, Uzbekistan
Turdualiyev Komiljon Makhsudaliyevich
Assistent, Department of Physiology and Pathology, Tashkent State Dental Institute, Tashkent, Uzbekistan
DOI Crossref DOI 10.37547/tajiir/Volume07Issue04-04
tajiir 2025
VOL. 7 / NO. 04 APRIL
FULL TEXT PP. 24-29
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VOLUME 7
ISSUE 04
YEAR 2025
PAGES 24-29
RESEARCH OVERVIEW

Abstract

Pathophysiology is a cornerstone of medical and health sciences education, bridging basic science and clinical practice. However, students often struggle with its complexity and abstract concepts. This article explores innovative teaching strategies, including case-based learning, virtual simulations, and adaptive learning platforms, to improve pathophysiology education. We assess the effectiveness of these methods in fostering deeper comprehension, retention, and clinical reasoning skills. Additionally, we discuss challenges in implementation and propose best practices for educators.

RESEARCH TOPICS

Keywords

Pathophysiology education medical education health sciences teaching

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References

  1. Michael, J., & Modell, H. (2021). Active learning in secondary and college science classrooms: A working model for helping the learner to learn. Routledge. https://doi.org/10.4324/9781410609212
  2. Woods, N. N., Mylopoulos, M., & Brydges, R. (2019). Informal self-regulated learning on a surgical rotation: Hidden curriculum in action. Advances in Health Sciences Education, 24(1), 103-118. https://doi.org/10.1007/s10459-018-9856-8
  3. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
  4. Chen, F., Lui, A. M., & Martinelli, S. M. (2018). A systematic review of the effectiveness of flipped classrooms in medical education. Medical Education, 51(6), 585-597. https://doi.org/10.1111/medu.12872
  5. Albanese, M. A., & Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issues. Academic Medicine, 68(1), 52-81. https://doi.org/10.1097/00001888-199301000-00012
  6. Srinivasan, M., Wilkes, M., Stevenson, F., Nguyen, T., & Slavin, S. (2007). Comparing problem-based learning with case-based learning: Effects of a major curricular shift at two institutions. Academic Medicine, 82(1), 74-82. https://doi.org/10.1097/01.ACM.0000249963.93776.aa
  7. Cook, D. A., Erwin, P. J., & Triola, M. M. (2010). Computerized virtual patients in health professions education: A systematic review and meta-analysis. Academic Medicine, 85(10), 1589-1602. https://doi.org/10.1097/ACM.0b013e3181edfe13
  8. Kononowicz, A. A., Woodham, L. A., Edelbring, S., Stathakarou, N., Davies, D., Saxena, N., ... & Zary, N. (2019). Virtual patient simulations in health professions education: Systematic review and meta-analysis by the digital health education collaboration. Journal of Medical Internet Research, 21(7), e14676. https://doi.org/10.2196/14676
  9. Ruiz, J. G., Mintzer, M. J., & Leipzig, R. M. (2006). The impact of E-learning in medical education. Academic Medicine, 81(3), 207-212. https://doi.org/10.1097/00001888-200603000-00002
  10. Masters, K. (2020). Artificial intelligence in medical education. Medical Teacher, 42(9), 976-980. https://doi.org/10.1080/0142159X.2020.1788714
  11. Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37-76. https://doi.org/10.1016/B978-0-12-387691-1.00002-8
  12. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.
  13. Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966-968. https://doi.org/10.1126/science.1152408
  14. Schmidt, H. G., & Moust, J. H. (2000). Factors affecting small-group tutorial learning: A review of research. *Problem-Based Learning: A Research Perspective on Learning Interactions, 1*, 19-52.
  15. Mylopoulos, M., & Woods, N. (2014). Preparing medical students for future learning using basic science instruction. Medical Education, 48(7), 667-673. https://doi.org/10.1111/medu.12426
  16. Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781108894333