Students’ perception of the learning environment in a distributed medical programme
Bibliographic record
Abstract
BACKGROUND: The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. PURPOSE: To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. METHOD: The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. RESULTS: The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. CONCLUSIONS: Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".