Learning Styles in Problem-based Learning Environments Impacts on Student Achievement and Professional Preparation in University Level Physical Therapy Courses
Bibliographic record
Abstract
It is unknown if matching students’ preferred learning style with course delivery style improves academic success and further study is warranted in this area (Feely & Biggerstaff, 2017; Newton & Miah, 2017). Recently, there has been a call to better identify and understand the preferred learning styles of physical therapy education students and the effect this has on education (Brudvig, Mattson, & Guarino, 2016; Lowdermilk, 2016). While the benefit of matching teaching and learning styles has been investigated in other academic disciplines, it has not been investigated in physical therapy education. The purpose of this study was to determine if student preferred learning style is related to success in a learner centered problem-based learning formatted class and program, specifically for physical therapy students. Results provide insights into preferred learning styles and student achievement in a problem-based learning centered Doctoral level Physical Therapy Program.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".