Relationships Between Students' Approaches to Learning, Perceptions of the Teaching–Learning Environment, and Study Success: A Case Study of Third-Year Veterinary Students
Bibliographic record
Abstract
The relationships among veterinary students' approaches to learning, perceptions of the teaching-learning environment, and study success were evaluated in a demanding, discipline-based curriculum. The aim was to elicit elements for improving student counseling. As part of a large multidisciplinary survey, 36 third-year students (74% response rate) answered a modified version of the Experiences of Teaching and Learning Questionnaire in 2006. In this study, the authors used students' responses to questions regarding examinations and the progress of studies. In addition, students were classified in the large survey into four clusters according to their approaches to studying. Study success was evaluated by exploring the number of study credits students had earned and their grade point averages. The differences in study success between the clusters were not statistically significant, but, in general, students applying a deep approach were most successful, whereas unorganized students applying a deep approach showed the largest variation in study progress. The most commonly mentioned factors for enhancing or impeding study progress were related to the curriculum and to the students' actions or experiences. Unorganized students applying a deep approach seemed to suffer the most from the workload and pressure of progressing in their studies according to a predetermined timetable. These students were also most unaware of the examinations' demands. The findings suggested that, in addition to curriculum development, there is a need to explicitly make students aware of their approaches to learning and to support the development of their study practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".