Clinical Anatomy · 2024 · 10 citations · 22 references
EducationAnatomyMedical EducationInstructional DesignMetacognitionPedagogyMetacognitive AwarenessMetacognitive KnowledgeMetacognitive RegulationPersonalized LearningPersonalized Learning SystemsStudent ReflectionMedical Anatomy EducationPedagogical ModelsPedagogical QuestionsPersonalized Learning TechnologyStudent Clinical CompetenceStudent Performance AnalysisStudent Performance AssessmentStudent Performance Criteria
Personalization of learning is an educational strategy rooted in metacognition and is significant in academic training. This is especially true in medical contexts. This study explored the relationship between the metacognitive profile of students of human anatomy, the classification of questions according to their difficulty, and the anatomical domain. It also covered the integration of educational technologies to create personalized learning environments. The identification of metacognitive profiles ("Active", "Pragmatic", "Theoretical", and "Reflective") has been highlighted as a critical influence on students' responses to different pedagogical approaches. Personalized adaptation based on these profiles has shown potential for improving grades and increasing student satisfaction and engagement with learning. The results revealed variations in student performance in relation to different pedagogical approaches, learning units, and evaluation modalities. The "Experience" evaluation modality, personalized according to metacognitive profiles, level of competence, and learning objectives, resulted in higher average scores. However, there was significant variability in the results. Those findings confirm the effectiveness of metacognitive adaptation in improving academic performance. Furthermore, they provide a solid basis for formulating personalized and effective pedagogical strategies in medical education. They recognize the influence of metacognitive profiles on student performance and contribute to advancing medical pedagogy.
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R: A Language and Environment for Statistical Computing
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