Enhancing learning approaches: Practical tips for students and teachers
Bibliographic record
Abstract
BACKGROUND: In an integrated curriculum such as problem-based learning (PBL), students need to develop a number of learning skills and competencies. These cannot be achieved through memorization of factual knowledge but rather through the development of a wide range of cognitive and noncognitive skills that enhance deep learning. AIM: The aim of this article is to provide students and teachers with learning approaches and learning strategies that enhance deep learning. METHODS: We reviewed current literature in this area, explored current theories of learning, and used our experience with medical students in a number of universities to develop these tips. RESULTS: Incorporating the methods described, we have developed 12 tips and organized them under three themes. These tips are (1) learn how to ask good questions, (2) use analogy, (3) construct mechanisms and concept maps, (4) join a peer-tutoring group, (5) develop critical thinking skills, (6) use self-reflection, (7) use appropriate range of learning resources, (8) ask for feedback, (9) apply knowledge learnt to new problems, (10) practice learning by using simulation, (11) learn by doing and service learning, and (12) learn from patients. CONCLUSIONS: Practicing each of these approaches by students and teachers and applying them in day-to-day learning/teaching activities are recommended for optimum performance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".