Becoming a tutor: exploring the learning experiences and needs of novice tutors in a PBL programme
Bibliographic record
Abstract
The tutor plays an important role in facilitating learning in a problem-based learning (PBL) curriculum. This paper explored the ways that novice tutors were educated in a PBL programme at McMaster University. Thirteen novice tutors were interviewed in this qualitative, ethnographic study to identify their learning needs and culture at the entry phase of 'becoming a tutor'. Ten tutor guides were also interviewed to provide additional information and perspectives regarding the data generated by the novice tutors. Categories that emerged were: (1) benefiting from the experience, (2) managing the challenges, (3) transitioning to a new role, (4) uncovering learning opportunities, (5) maintaining vigilance, and (6) explicating the implicit. The overarching framework that wove the categories together was that of the theme of storytelling in the teaching-learning process. Implications for practice for tutor training are addressed considering the oral tradition.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".