New directions in e-learning research in health professions education: Report of two symposia
Bibliographic record
Abstract
BACKGROUND: The use of Computer Assisted Instruction (CAI) is rising across health professions education. Research to date is of limited use in guiding the implementation and selection of CAI innovations. AIMS: In the context of two symposia, systemic reviews were discussed that evaluate literature in Internet-based learning, Virtual Patients, and animations. Each session included a debate with the goal of reaching consensus on best current practices and future research. METHODS: Thematic analysis of the discussions was performed to arrange the questions by theme, eliminate redundancy, and craft them into a cohesive narrative. RESULTS: The question analysis revealed that there are clear advantages to the use of CAI, and that established educational theories should certainly inform the future development and selection of CAI tools. Schools adopting CAI need to carefully consider the benefits, cost, available resources, and capacity for teachers and learners to accept change in their practice of education. Potential areas for future research should focus on the effectiveness of CAI instructional features, integration of e-learning into existing curricula and with other modalities like simulation, and the use of CAI in assessment of higher-level outcomes. CONCLUSIONS: There are numerous opportunities for future research and it will be important to achieve consensus on important themes.
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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 | 0.062 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".