Exploring assessor cognition as a source of score variability in a performance assessment of practice-based competencies
Bibliographic record
Abstract
BACKGROUND: A common feature of performance assessments is the use of human assessors to render judgements on student performance. From a measurement perspective, variability among assessors when assessing students may be viewed as a concern because it negatively impacts score reliability and validity. However, from a contextual perspective, variability among assessors is considered both meaningful and expected. A qualitative examination of assessor cognition when assessing student performance can assist in exploring what components are amenable to improvement through enhanced rater training, and the extent of variability when viewing assessors as contributing their individual expertise. Therefore, the purpose of this study was to explore assessor cognition as a source of score variability in a performance assessment of practice-based competencies. METHOD: A mixed-method sequential explanatory study design was used where findings from the qualitative strand assisted in the interpretation of results from the quantitative strand. Scores from one objective structured clinical examination (OSCE) were obtained for 95 occupational therapy students. Two Generalizability studies were conducted to examine the relative contribution of assessors as a source of score variability and to estimate the reliability of domain and holistic scores. Think-aloud interviews were conducted with eight participants assessing a subset of student performances from the OSCE in which they participated. Findings from the analysis of think-aloud data and consideration of assessors' background characteristics were used to assist in the interpretation of variance component estimates involving assessors, and score reliability. RESULTS: Results from two generalizability analyses indicated the highest-order interaction-error term involving assessors accounted for the second-highest proportion of variance, after student variation. Score reliability was higher in the holistic vs. analytic scoring framework. Verbal analysis of assessors' think-aloud interviews provided evidential support for the quantitative results. CONCLUSIONS: This study provides insight into the nature and extent of assessor variability during a performance assessment of practice-based competencies. Study findings are interpretable from the measurement and contextual perspectives on assessor cognition. An integrated understanding is important to elucidate the meaning underlying the numerical score because the defensibility of inferences made about students' proficiencies rely on score quality, which in turn relies on expert judgements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".