Effectiveness of student-led objective tutorials in pharmacology teaching to medical students
Bibliographic record
Abstract
Objectives: Current teaching in pharmacology is passive with less emphasis on clinical application. There is a need to incorporate newer instructional designs into pharmacology. Student-led objective tutorial (SLOT) is one of the novel designs to enhance interest among learners, provide opportunities for group learning, and facilitate self-directed learning. This study aims to assess the effectiveness of SLOTs over conventional tutorials (CTs) in pharmacology and to obtain feedback from the students regarding their perceptions about it. Subjects and Methods: The regular batch of MBBS 2nd professional in pharmacology was randomly divided into two groups. Five topics from central nervous system (CNS) were selected. One group received SLOT as the instructional strategy, whereas the other group went through CTs. At the end of the module, a written test was conducted to assess the effectiveness of both strategies. The students provided feedback regarding their experience using a prevalidated questionnaire. Statistical Analysis: The mean scores of both the groups were analyzed using Mann–Whitney U-test. Results: There was no significant difference in the mean scores of the end of the module test. However, the overall passing percentage was significantly higher in the intervention group (P = 0.043). A total of 45.71% students favored it as a future tutorial method and expressed that SLOT enhanced their ability to learn independently. Conclusion: SLOT is an effective teaching–learning method to teach pharmacology to medical undergraduates. It enhances interest among learners and increases the ability to learn independently. Key message: Student led objective tutorial (SLOT) serve as critical determinants for self-learning and improve analytical skills of students. It enhances interest among learners, provide opportunities for group learning and facilitate self-directed learning. SLOT can be introduced as an interactive teaching learning strategy in pharmacology.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".