Online learning in dentistry: an overview of the future direction for dental education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper provides an overview of the diversity of tools available for online learning and identifies the drivers of online learning and directives for future research relating to online learning in dentistry. After an introduction and definitions of online learning, this paper considers the democracy of knowledge and tools and systems that have democratized knowledge. It identifies assessment systems and the challenges of online learning. This paper also identifies the drivers for online learning, including those for instructors, administrators and leaders, technology innovators, information and communications technology personnel, global dental associations and government. A consideration of the attitudes of the stakeholders and how they might work together follows, using the example of the unique achievement of the successful collaboration between the Universities of Adelaide, Australia and Sharjah, United Arab Emirates. The importance of the interaction of educational principles and research on online learning is discussed. The paper ends with final reflections and conclusions, advocating readers to move forward in adopting online learning as a solution to the increasing worldwide shortage of clinical academics to teach dental clinicians of the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it