A New Concept of Unsupervised Learning: Directed Self-Guided Learning in the Health Professions
Bibliographic record
Abstract
BACKGROUND: Among the advantages in using educational technologies in health professions education is the opportunity for trainees to learn on their own time. This flexibility in learning opportunities, however, comes with possible dangers associated with unsupervised learning, such as the potential for developing bad habits and misunderstandings, and for overestimating one's preparedness for practice. METHOD: This nonsystematic review reflects on the literatures that speak to the advantages of self-guided learning, explores the metacognition literature to understand what trainees do spontaneously when self-guiding their learning, and reexamines the advantages of supervised learning. RESULTS: Those literatures are combined in an effort to reorient our questions when considering the concept of self-guided learning. CONCLUSIONS: The authors propose that future research should ask questions that focus on our understanding of trainees' natural propensities while learning in the unsupervised context and on exploring conditions that will maximize the educational benefit of self-guided learning.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".