EVALUATION OF TUTOR PERFORMANCE IN PROBLEM BASED LEARNING: RATING THE SKILL ON STUDENTS PERSPECTIVE
Bibliographic record
Abstract
Background: The Problem based learning (PBL) was developed at McMaster University School of Medicine in Canada in the 1960s. It has become today’s most accepted method of teaching and learning activities in the field of medicine. A skilled and well-trained tutor plays major role in PBL. Present study is aimed to evaluate tutor performance on student’s perspective based on questionnaire. Methods: This questionnaire-based study was conducted with MBBS I (n=100) and II (n=100) year students of Nobel Medical College and Teaching Hospital. Tutors performance evaluation form was prepared provided with nine question items and the responses were limited to likert scale (1=strongly disagree, 2=disagree, 3=uncertain, 4=agree and 5=strongly agree). Students were instructed to give their opinion and total percentage score along with mean score of every question items were obtained. Then, mean score of each questions were compared between both MBBS batches. Results: Performance of tutors in problem-based learning sessions were analyzed which were obtained as Likert scale score; the percentage score 4 (agree, MBBS I= 52.11 %, MBBS II=53.55 %) followed by 5 (strongly agree, MBBS I=20.77 %, MBBS II= 32.22 %). Mean score obtained for each question items were compared between MBBS I and II year which significantly vary though the majority of scores were 4 (agree) and 5(strongly agree). Conclusions: Satisfactory tutor performance was procured on evaluating the tutor for their skill in PBL as facilitator based on student’s opinion.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".