Developing an Interactive Computer Program for Integrated Dental Education
Bibliographic record
Abstract
OBJECTIVES: The knowledge of anatomy is an integral part of dental and medical education that builds the foundations of pathology, physiology, and other related disciplines. Traditional three-dimensional (3D) models used to teach anatomy cannot represent dynamic physiological processes and lack structural detail in the oral regions relevant for dental education. We developed an interactive computer program to teach oral anatomy, pathology, and microbiology in an integrated manner to improve students' learning experiences. METHODS: The computer program, Jawnatomy, was developed as a 3D human head. Cognitive load theory guided the design of the tool, with the goal of reducing the heavy cognitive load of learning anatomy and integrating this knowledge with pathology and microbiology. Keller's attention, relevance, confidence, and satisfaction (ARCS) model of motivational design was used while creating the tool to improve learners' motivation and engagement. Blender was used to create the graphics, and Unity 3D was used to incorporate interactivity in the program. The 3D renderings of oral anatomy and progression of tooth decay were created with the input of content experts. RESULTS: Jawnatomy will be launched in our institution's dentistry and dental hygiene program to support project- and team-based learning. This program will also be introduced to students as a self-directed learning tool to help them practice and strengthen their anatomical knowledge at their own pace. CONCLUSIONS: Surveys and focus groups will be conducted to evaluate and further improve the computer program. We believe that Jawnatomy will become an invaluable teaching tool for dental education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".