Students’ Perception of Cognitive Load in an Accelerated DPT Program with a Blended Curriculum
Bibliographic record
Abstract
Administrators and educators in higher education are interested in how academic tutoring services and gender impact perceptions of cognitive load and, therefore, students’ academic success. However, a lack of evidence existed in the literature regarding physical therapy students’ perception of cognitive load in an accelerated Doctor of Physical Therapy program with blended learning. Participants in this quantitative, non-experimental study completed the adapted Cognitive Load Scale to indicate their perception of cognitive load, participation in academic tutoring services, gender, and age. The DPT students perceived high cognitive overload, but a t value of 0.37 and a p value of 0.71 indicated that their perception was not significantly related to gender. Further, a t value of -3.09 and a p value of 0.005 indicated that academic tutoring services played a vital role in minimizing the perception of cognitive overload. However, the p value of 0.11 of the parametric multiple linear regression analysis and the p value of 0.59 of the interaction term indicated no moderating relationship between academic tutoring services and gender. This evidence may assist physical therapy administrators and educators of DPT students in re-structuring blended learning programs and accelerated curricula to reduce student perceptions of cognitive overload.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".