How to create interactive digital resources that result in real learning outcomes
Bibliographic record
Abstract
Over recent years, technological advances and digital tools in anatomical education have been rapidly developing. Indeed, the way our students learn and access educational resources has changed significantly over the years. This is also reflected in the surge to the market of digital educational material, and not always validated by professional anatomists. Through an International Anatomy Education Hub, we have combined our skillsets in the field of digital anatomy from anatomy and surgical staff, from four leading universities across the globe. We aim to introduce a variety of tools and software that we as educators, and student users, could use in generating educationally validated, curriculum specific, digital anatomical training materials. Based on human‐computer interaction research and theories, evidence‐based guides to building effective educational content in the digital environment will be discussed. It will also highlight strategies for involving the students as co‐creators of educational materials, designing assessment tools, and will present feedback from the end user. Simple and effective ways to build local, curriculum specific content, in an open source format, will be presented, as well as the benefits of international collaborations. This will show how local digital focused activities can benefit the global anatomical education community.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.018 |
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".