Perception of problem based learning versus conventional teaching methods by clinical medical students in Nigeria
Bibliographic record
Abstract
INTRODUCTION: Problem-based learning (PBL) method which was introduced about 50 years ago in Canada is beginning to gain acceptance over conventional teaching method (CTM) worldwide in medical education but still remains unpopular in Nigeria. This study aims to determine the perception of clinical medical students to the use of both learning methods in pathology courses. METHODS: A cross-sectional quantitative survey was conducted in four Nigerian universities drawn from four regions of the country. Data were collected using pretested semi-structured self-administered questionnaires. RESULTS: The study included 310 respondents, 182(58.7%) males and 128(41.3%) females. Of all the participants, 257(82.9%) had heard of PBL prior to the study and 260(83.9%) thought it suitable for teaching and learning Pathology. Majority of participants, 221(71.3%) preferred a combination of both PBL and CTM while 238(76.8%) thought PBL suitable for all medical students. Some identified factors capable of enhancing adaptation of PBL into medical curriculum include conducive quiet spaces for learning and availability of computers with internet facilities for students' use. CONCLUSION: Participants demonstrated high level of awareness of PBL and thought it suitable for all medical students. Availability of computers and up-to-date libraries with internet and audio-visual facilities could enhance adaptation of PBL into medical curriculum in Nigeria.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".