Experience in Applying Active Learning Methods in Probability and Statistics at Gia Lai Medical College

Abstract : 

Background: In health-professions education, Probability and Statistics supports the interpretation of quantitative information and the development of evidence-informed reasoning. Students may nevertheless master computational routines without understanding when, why, or how to use them in professional contexts.

Objective: This article systematises pedagogical experience in applying active learning at Gia Lai Medical College and develops a coherent instructional design framework linking health-related contexts, cognitive tasks, learning evidence, and feedback.

Methods: A qualitative descriptive synthesis combined a focused review of eight foundational publications, analysis of topics and learning activities in the course, and reflective accounts of classroom practice. The material was organised across difficulties in statistical reasoning, task formats, learning products, assessment, and implementation constraints.

Results: Five topic-to-context alignments, six complementary active-learning approaches, an illustrative 90-minute lesson sequence, and a weighted assessment rubric were consolidated. Four practical tensions emerged: professional relevance versus cognitive overload, collaboration versus uneven individual participation, computational efficiency versus conceptual understanding, and discussion time versus instructional closure. The proposed Context-Task-Evidence-Feedback (CTEF) framework connects these elements in a recurring design cycle. Conclusion: Active learning in introductory statistics should be judged by the reasoning learners can demonstrate, not by the number of techniques used. The CTEF framework is an instructional proposal derived from published literature and practitioner reflection; its effects on student achievement have not yet been empirically evaluated