Climate studies: can students’ perceptions of the ideal educational environment be of use for institutional planning and resource utilization?
Bibliographic record
Abstract
As educational climate strongly affects student achievement, satisfaction and success, it is important to get regular feedback from students on how they experience the educational environment. The Dundee Ready Education Environment Measure (DREEM) Inventory was administered on the same day to all first-, second- and third-year students at the Canadian Memorial Chiropractic College (CMCC) and the students were requested to complete the questionnaire as they were actually experiencing the educational environment at CMCC, and then to say what they would have wanted, or preferred it to be like. Valid returns were received from 146 (95%) first-, 123 (82%) second- and 73 (48%) third-year students (n = 342). The results indicated that the DREEM Inventory used in the Ideal mode, together with the responses in the Actual mode, could be used effectively to determine the dissonance between what they had and what they would have liked to have. It was found that there was a strong similarity in the areas of the educational environment that the different groups of students indicated as falling short of their ideal. The results of this study provided a useful basis for strategic planning and resource utilization.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".