SCÉES The Impact of Problem-Based Learning in an Interdisciplinary First-Year Program on Student Learning Behaviour
Bibliographic record
Abstract
Canadian universities are struggling to address seemingly contra-dictory challenges pertaining to undergraduate education: high demand and underfunding. A number of instruments, including the National Survey of Student Engagement (National Survey of Student Engage-ment, n.d.), have led to greater priority being placed on the undergradu-ate experience. Yet, strategies to ensure student satisfaction with their education, through initiatives such as small classes and personal contact with faculty, seem at odds with the large classes necessitated by fi scal imperatives. We carried out a systematic investigation of the impact of one problem-based learning course on fi rst year students ’ experiences. We also investigated the persistence of skills and attitudes learned in this single exposure to problem-based learning. The results of our inves-tigation show that this course had very positive effects on the immediate and persistent behaviours of students. Our research provides empirical evidence of the effectiveness of problem-based learning and leads us to suggest how a problem-based approach might help universities enhance the quality of education and the undergraduate experience. RÉSUMÉ Les universités canadiennes sont aux prises avec deux défi s apparemment contradictoires en matière d’enseignement au premier cycle: une demande élevée et un fi nancement insuffi sant. Plusieurs instruments, dont un sondage sur l’engagement étudiant (National Survey of Student Engagement), ont accru l’importance accordée à l’expérience étudiante au premier cycle. Néanmoins, les stratégies
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".