The Virtual Anatomy Lab: an eDemonstrator pedagogical agent can simulate student-faculty interaction and promote student engagement
Bibliographic record
Abstract
As medical curricula evolve, many universities have adopted a clinical case-centered medical curriculum with a strong focus on small group learning and reduction of traditional lectures such that anatomy has become a self-taught subject supported by e-learning modules. One caveat of this approach is decreased student-faculty interaction and reduced student engagement. Thus use of e-learning must be balanced with the need for continued student-faculty interaction to promote healthy student engagement. To both support self-directed learning of anatomy and to simulate student-faculty interaction, we created the Virtual Anatomy Lab (VAL) that features a human pedagogical agent, called the eDemonstrator, who guides student navigation through the available learning resources. The VAL was evaluated using a mixed methods approach (usage statistics and focus groups) by two medical student populations at the University of Ottawa: first year medical students in a revised curriculum where anatomy lectures were abolished and laboratory sessions were self-taught, and second year medical students in the former curriculum in which anatomy lectures were given in advance of each laboratory session. We conclude that online modules such as the VAL, well designed with a human pedagogical agent, can be used within the curriculum without negatively impacting student engagement. Ethical Approval for this study was obtained from the Ottawa Hospital Research Ethics Board (protocol number #2009055-01H).
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".