Learning theory and its application to the use of social media in medical education
Bibliographic record
Abstract
BACKGROUND: There is rapidly increasing pressure to employ social media in medical education, but a review of the literature demonstrates that its value and role are uncertain. OBJECTIVE: To determine if medical educators have a conceptual framework that informs their use of social media and whether this framework can be mapped to learning theory. METHODS: Thirty-six participants engaged in an iterative, consensus building process that identified their conceptual framework and determined if it aligned with one or more learning theories. RESULTS: The results show that the use of social media by the participants could be traced to two dominant theories-Connectivism and Constructivism. They also suggest that many medical educators may not be fully informed of these theories. CONCLUSIONS: Medical educators' use of social media can be traced to learning theories, but these theories may not be explicitly utilised in instructional design. It is recommended that formal education (faculty development) around learning theory would further enhance the use of social media in medical education.
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How this classification was reachedexpand
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 | 0.018 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".