Courses for tutors in problem-based learning. Current challenges at four Swedish universities
Bibliographic record
Abstract
The key role of the tutor in problem-based learning (PBL) is to help students become selfregulated learners. Tutors need training to acquire the necessary facilitating skills for this task. The aim of this article is to describe and discuss how PBL tutor training is currently arranged at four universities in Sweden: Linköping University, Lund Medical Faculty, Uppsala Medical School and Örebro School of Medicine. Moreover, we seek to analyse how the content and format of the tutor training courses correspond to the desired skills and competencies for PBL tutors described in the literature. We draw especially on work coming out of three pioneering universities for PBL: McMaster University, Canada; Maastricht University, The Netherlands; and Linköping University, Sweden. One aim has been to construct a framework for analysis that uses categories specifying the knowledge base, capabilities and skills to support students’ learning processes which characterise the full-fledged PBL tutor. For this framework, we have used the following categories: Knowledge of PBL and pedagogical theories, Personal traits, Student-centeredness, Ability to handle group processes, and Subject knowledge. We collected descriptions of the course design and content from the four universities, and assessed to what extent these categories were represented within the courses. Our results show that all categories inform the course content at all four universities, though the design varies between courses. In summary, we show that the four PBL tutor training courses are all designed to enable participants to experience PBL first-hand both as members of a tutorial group and as tutors. They all also include a theoretical base and offer opportunities for discussion and reflection with peers; however, there are some differences in design between the courses. According to participants, all four courses provide good preparation for the tutor role. Yet, we see a need for the programmes to organise continuous educational support for tutors after they have started their work with groups of students.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".