Mind Mapping in Orthodontic Education: A Two-Cohort Action-Research Study Exploring the Impact of Feedback on Student...

Mind mapping is a visual learning strategy that fosters critical thinking in health professions education, yet it remains underexplored in orthodontic curricula. With varying class sizes, increasing reliance on technology, and fragmented delivery of core orthodontic concepts, there is a need for pedagogical tools that support integrative and meaningful learning. This study evaluates how instructional support significantly impacts students' perceptions and learning gains when mind mapping is introduced in an undergraduate orthodontics course.

An opportunistic comparison action-research study was conducted with two student cohorts (n = 187). Cohort I received structured orientation, guided instruction, and individualised feedback on their mind maps, while Cohort II completed the activity independently due to limited faculty availability. A 14-item perception questionnaire was used to assess students' views of the mind mapping experience. Reliability testing (Cronbach's α), t-tests, ANOVA, and regression models examined the impact of instructional support and learning styles on student perceptions.

The perception scale demonstrated strong internal consistency (α = 0.81). Cohort I reported significantly higher scores in 11 out of 14 perception items. Regression analysis identified cohort membership as a significant predictor of students' ability to perceive "the big picture" and recognise inter-topic links, suggesting a positive effect of structured feedback.

The findings align with literature on feedback-enhanced learning and highlight the value of guided visual tools in complex clinical domains. The differences between cohorts underscore the influence of faculty scaffolding on student engagement and conceptual integration.

When embedded within structured instructional guidance, proper scaffolding, and formative feedback, learning tools can support meaningful integration of orthodontic concepts, an increasingly important safeguard against fragmented surface learning, especially in the AI era.