Use of mind maps in dental education: An activity performed in a preclinical endodontic course
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: (1) to assess the ability of dental students to use mind maps to express the relationships of endodontic theory and technique; (2) to explore features illustrated from the highest- and lowest-graded mind maps; and (3) to evaluate improvements in successive mind maps from the same student. METHODS: A total of 31 second-year students were asked to configure a mind map on root canal cleaning-shaping and then 18 weeks later develop a second mind map on root canal obturation. Faculty visually analyzed the mind maps using a qualitative approach: a multilayered process of thematic analysis. Codes and themes were generated to investigate if students were able to express appropriate and evidence-based ideas on the topics (accuracy of relationships and depth of information presented). Two of the highest- and 2 of the lowest-graded mind maps for each activity were directly compared. Improvement by the same student from the first to second mind map was also evaluated based on trend/style and creativity. RESULTS: The majority of the students accurately expressed the scientific basis for root canal cleaning-shaping and obturation. The highest-graded mind maps displayed the biomedical and humanistic conceptions of critical thinking. In comparing the second mind map to the first, nearly 50% of the students incorporated more detail and artistic expression in the second map. CONCLUSIONS: using mind maps in dental education can benefit students with different learning styles and help the instructor to identify the level of conceptualization that the student had developed about a topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".