Evidence for the Validity of Grouped Self-Assessments in Measuring the Outcomes of Educational Programs
Bibliographic record
Abstract
There is compelling empirical evidence in support of the use of grouped self-assessment data to measure program outcomes. However, other credible research has clearly shown that self-assessments are poor predictors of individual achievement such that the validity of self-assessments has been called into question. Based on the reanalysis of two previously published studies and an analysis of two original studies, we show that grouped self-assessments may be good predictors of and hence valid measures of performance at the group level, an outcome commonly used in program evaluation studies. We found statistically significant correlation coefficients (between 0.56 and 0.87), when comparing across performance items using the group means of self-assessments with the group means of individual achievement on criterion tests. We call for further research into the conditions and circumstances in which grouped self-assessments are used, so that they can be employed more effectively and confidently by program evaluators, decision makers, and researchers.
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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 | 0.281 | 0.519 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".