The Impact of Electronic Data to Capture Qualitative Comments in a Competency-Based Assessment System
Bibliographic record
Abstract
<p>Introduction Digitalizing workplace-based assessments (WBA) holds the potential for facilitating feedback and performance review, wherein we can easily record, store, and analyze data in real time. When digitizing assessment systems, however, it is unclear what is gained and lost in the message as a result of the change in medium. This study evaluates the quality of comments generated in paper vs. electronic media and the influence of an assessor's seniority. Methods Using a realist evaluation framework, a retrospective database review was conducted with paper-based and electronic medium comments. A sample of assessments was examined to determine any influence of the medium on the word count and the Quality of Assessment for Learning (QuAL) score. A correlation analysis evaluated the relationship between word count and QuAL score. Separate univariate analyses of variance (ANOVAs) were used to examine the influence of the assessor's seniority and medium on word count, QuAL score, and WBA scores. Results The analysis included a total of 1,825 records. The average word count for the electronic comments (M=16) was significantly higher than the paper version (M=12; p=0.01). Longer comments positively correlated with QuAL score (r=0.2). Paper-based comments received lower QuAL scores (0.41) compared to electronic (0.51; p<0.01). Years in practice was negatively correlated with QuAL score (r=-0.08; p<0.001) as was word count (r=-0.2; p<0.001). Conclusion Digitization of WBAs increased the length of comments and did not appear to jeopardize the quality of WBAs; these results indicate higher-quality assessment data. True digital transformation may be possible by harnessing trainee data repositories and repurposing them to analyze for faculty-relevant metrics.</p>
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".