Do Motivation and Race/Ethnicity Impact Success in an Anatomy Course for Doctor of Physical Therapy Students?
Bibliographic record
Abstract
Introduction Assessing motivation behaviors of Doctor of Physical Therapy (DPT) students during their first foundational science course, Clinical Anatomy, may improve our understanding of academic success. Students’ ability to succeed academically is determined by components of self‐regulated learning strategies (SRL) and motivation behaviors. While DPT program admissions criteria are intended to be markers indicative of academic success that highlight SRL, motivation behaviors are not typically assessed. Student failure rates have been persistent and there appears to be a mismatch between the markers designed to predict success and actual success in DPT education. Objective: The purposes of this study were to determine the direct effects of motivational behaviors and race/ethnicity on academic success and to determine the moderating effect of race/ethnicity on motivational behaviors. Materials and Methods Thirty‐three first‐year DPT students participated during their first foundational course, clinical anatomy. The motivation subscales from the Motivated Strategies for Learning Questionnaire (MSLQ) were used to assess how student motivation behaviors impacted academic success expressed as course grade. Results The motivation sub scale of self‐efficacy for learning and performance (SEL) was significantly correlated with course grade (r(31) = .44, p < .05). Independent t‐test indicated that course grade differed at a statistically significant level by race/ethnicity (t(31) = 2.93, p < .01). Within the full multivariate model, race/ethnicity (B = .05, SE = .01, β = .42, p < .008) and SEL (B = .02, SE = .01, β = .39, p < .01), remained significantly related to course grade. Conclusion The results of this study indicated that SEL and race/ethnicity are factors that can determine academic success. Significance/Implications Understanding students’ levels of SEL can guide the development of programmatic and teaching strategies to support students identified with poor SEL. Implementing strategies aimed to improve students’ SEL, such as providing timely feedback, delineating clear expectations, collaborative and team‐based learning, and incremental goal setting, may enhance academic success. The current study provides a rationale for implementing the strategies that bolster SEL and indicated that those strategies may be more closely applied to and benefit under‐represented students.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".