Student and teaching characteristics related to ratings of instruction in medical sciences graduate programs
Bibliographic record
Abstract
BACKGROUND: Although the validity of students' ratings of instruction has been documented, several student and course characteristics may be related to the ratings students give their instructors. AIMS: The purpose of this study was to examine student ratings obtained from the Universal Student Ratings of Instruction (USRI) instrument. These responses were compared to various student characteristics. Also, teaching characteristics that were most closely associated with the ratings were determined. METHOD: A total of 1738 USRI forms were completed by graduate students enrolled in medical science courses from 1999 to 2006 in the Faculty of Medicine at a Canadian university. RESULTS: Between group comparisons showed that negative student perceptions about the course (i.e., did not have the freedom to select), perceiving the course workload as high, and low grade expectations held were related to negative student ratings of overall quality of instruction. In terms of the student and teaching characteristics, organization of course material and perceptions of whether students felt they learned a lot in the course were most closely related to global ratings of instructional quality. CONCLUSION: Implications for teaching focus on improving the organization and delivery of course content that meets the learning objectives of graduate students in medical sciences.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".