Tooth ARcademy: A mobile app for teaching and learning of oral histology
Bibliographic record
Abstract
m-Learning is gaining popularity in health professional education; however, reports on mobile apps targeting didactic teaching and learning are scarce, particularly in the context of health professional courses such as histology. Histology is an essential foundational component of dental and medical education. At the Mike Petryk School of Dentistry, University of Alberta, instructors utilize photomicrographs from textbooks to teach students on the microanatomy of teeth, the development of tooth and facial regions, and developmental anomalies. Limited availability of high-quality tissue sections and time constraints present challenges for both students and instructors. To provide students with an accessible collection of diverse histological sections and to facilitate in-class interactive didactic teaching, we developed an Augmented Reality (AR)-based mobile app called Tooth ARcademy. The development of Tooth ARcademy comprises the following steps: selecting histology glass slides, digitizing the glass slides, curating and annotating the digital slides, preparing multiple-choice questions, and integrating the resources into the mobile app. Tooth ARcademy is available worldwide at no cost. The app has three modes. Instructors can use the AR-based Learn mode to create in-class activities and supplemental questions tailored to students with specific learning outcomes. The Practice mode enables students to study oral histology outside of class time. With the Quiz mode of Tooth ARcademy, students can self-assess their knowledge of oral histology by participating in quizzes. The knowledge of oral histology is essential for dental education. Tooth ARcademy is designed to create interactive and engaging learning environments both inside and outside the classroom. Besides some limitations of the current phase, Tooth ARcademy can be a valuable m-learning tool that benefits students and educators in dental, medical, and other professional schools.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".