Learning Style Differences between Nursing and Teaching Students in Sweden: A Comparative Study
Bibliographic record
Abstract
The teaching profession has been continually challenged to provide evidence of the effectiveness of teaching and learning methods. Teacher education, as well as nursing education, is currently undergoing reforms in Sweden. At the university where the research was conducted, teaching and nursing programs are two priority educational programs and maybe knowledge of learning styles can improve the quality of these programs. The purpose of this research was to examine the learning style preferences for two student groups, teachers and nurses, to analyze their differences in light of international research on learning styles. The study involved 78 teaching students and 78 nursing students. Twenty subscales of the Productivity Environmental Preference Survey (PEPS) (Dunn, Dunn, & Price, 1984; 1991; 2000) were used to identify the participants’ learning style preferences. The results showed statistically significant differences between the two student groups. In comparison to teaching students, more nursing students were highly motivated, kinesthetic, and preferred authorities. More teaching students were highly persistent. The findings suggest the need for widely diverse teaching approaches and conscious didactic action skills in higher education, as well as implementation of learning strategies for students.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".