Learning-Style Profiles of 150 Veterinary Medical Students
Bibliographic record
Abstract
Awareness of student learning-style preferences is important for several reasons. Understanding differences in learning styles permits instructors to design course materials that allow all types of learners to absorb and process information. Students who know their own learning style are better able to help themselves in courses taught in a non-preferred method by developing study strategies in line with their preferred learning method. We used the Felder and Solomon Index of Learning Styles to assess the learning-style profiles of 150 veterinary students in three consecutive years. Students were predominantly active (56.7%), sensing (79.3%), visual (76.7%), and sequential (69.3%). Most were balanced on the active-reflective (59.3%) and global-sequential (50%) dimensions, and 61.3% and 54% were moderately to strongly sensing and visual, respectively. Small but significant numbers of students were moderately to strongly intuitive (8.7%), verbal (13%), and global (12%). The most common patterns were active-sensing-visual-sequential (26%), reflective-sensing-visual-sequential (19.3%), active-sensing-visual-global (8.7%), and active-sensing-verbal-sequential (8.7%). Although most students (65.3%) were balanced on one to two dimensions, 77.3% had one or more strong preferences. Our results show that although people have dominant learning-style preference and patterns, they have significant minor preferences and patterns across all dimensions with moderate to strong preferences on each scale. These results indicate that a balanced approach to teaching is essential to allow all students to learn optimally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".