Application of “Earl's Assessment as, Assessment for, and Assessment of Learning Model” with Orthopaedic Assessment Clinical Competence
Bibliographic record
Abstract
Context In order to study the efficacy of assessment methods, a theoretical framework of Earl's model of assessment was introduced. Objective (1) Introduce the predictive learning assessment model (PLAM) as an application of Earl's model of learning; (2) test Earl's model of learning through the use of the Standardized Orthopedic Assessment Tool (SOAT); and (3) establish construct validity of the SOAT. Design Quasi-experimental. Setting Three Canadian universities Patients or Other Participants A convenience sample of 57 third-year undergraduate athletic therapy students from three universities were randomly assigned into three experimental groups. Intervention(s) Treatment group 1 gave the instructor access to the SOAT, but the instructor could not explicitly share it. Treatment group 2 gave both the instructor and students access to the SOAT throughout the semester to use formatively. Group three was the comparison. Main Outcome Measure(s) All students were tested using the SOAT at the end of the semester using expert raters. An analysis of variance (ANOVA) (P < .05) was used to determine whether there was a difference between groups in their final examination grades. Results The ANOVA demonstrated a significant difference between groups (F2,56 = 28.6, P < .01). The effect size, calculated using η2, was 0.51. Post hoc analysis revealed a significant difference between treatment group 2 and the other treatment group and comparison group. Conclusions Small sample size and the quasi-experimental design prevent definitive conclusions, but the SOAT was able to discriminate between various groups, supporting our construct validity objective. The SOAT was introduced as a predictive tool that may assist orthopaedic assessment skill development. The treatment group exposed to the SOAT demonstrated that formative assessment of students using the SOAT was an effective means of teaching relative to no exposure or where only the instructor was exposed to the SOAT.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".