Prospective comparison of student-generated learning issues and resources accessed in a problem-based learning course
Bibliographic record
Abstract
BACKGROUND: Multiple factors can contribute to variability in content coverage and student study activities between problem-based learning (PBL) groups. AIMS: The purpose of this study was to analyse the student learning issues to answer three questions: 1. How do the student-generated learning issues compare to faculty-developed 'key feature' objectives for each case? 2. Is there stability in choice of student learning issues over a four-year period? 3. What resources do the students access and has this changed over a four-year period? METHODS: Student-generated learning issues were collected during a course that follows a PBL design using standardized patient cases. Between 2002 and 2005, 407 students in 74 groups completed the course. The student-generated learning issues were compared with faculty-developed learning objectives to identify content covered. Students also recorded resources accessed and time spent researching the learning issues. RESULTS: Learning issues regarding medical content had moderate correspondence to faculty objectives. However, 'key feature' objectives that included other content such as communication challenges, ethics issues, psychosocial stressors, etc. were identified less frequently in student learning issues. Student study time was constant across cases, groups and years. A trend toward increased use of electronic resources over time was identified, and student choice of resource material did not necessarily match the references listed in the case materials. CONCLUSION: Despite similarity in student study time between groups, significant variability in content of learning issues and resources accessed was apparent.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".