Self-directed learning during problem-based learning sessions
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Purpose: The study aimed to obtain student perceptions about the influence of problem-based learning on self-directed learning skills among undergraduate basic science medical students. Methods: A cross-sectional study was conducted among first to sixth-semester undergraduate medical students during the last week of July 2016. A previously used instrument was used after obtaining written permission from the developers. The data was analyzed using statistical package for social sciences version 20. The free text comments were tabulated. Results: Fifty-two of the 90 students (58%) participated. The majority of respondents were between 20 to 30 years of age, and of either American or Canadian nationality. The gender distribution was nearly equal. The mean self-management of learning, independent pursuit of learning, learner control of instruction and personal autonomy scores were 2.95, 2.94, 2.98 and 2.87 (maximum possible score being 4). There were no significant differences in the mean domain scores according to age group, gender, and nationality of respondents. Conclusion: Respondents in the present study showed good scores on most statements related to the four dimensions of self-directed learning. Due to the hybrid nature of the curriculum and due to lectures being the dominant teaching-learning strategy, students may have devoted less effort and time to the PBL topics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".