Narrative Assessments in Higher Education: A Scoping Review to Identify Evidence-Based Quality Indicators
Bibliographic record
Abstract
PURPOSE: Narrative comments are increasingly used in assessment to document trainees' performance and to make important decisions about academic progress. However, little is known about how to document the quality of narrative comments, since traditional psychometric analysis cannot be applied. The authors aimed to generate a list of quality indicators for narrative comments, to identify recommendations for writing high-quality narrative comments, and to document factors that influence the quality of narrative comments used in assessments in higher education. METHOD: The authors conducted a scoping review according to Arksey & O'Malley's framework. The search strategy yielded 690 articles from 6 databases. Team members screened abstracts for inclusion and exclusion, then extracted numerical and qualitative data based on predetermined categories. Numerical data were used for descriptive analysis. The authors completed the thematic analysis of qualitative data with iterative discussions until they achieved consensus for the interpretation of the results. RESULTS: After the full-text review of 213 selected articles, 47 were included. Through the thematic analysis, the authors identified 7 quality indicators, 12 recommendations for writing quality narratives, and 3 factors that influence the quality of narrative comments used in assessment. The 7 quality indicators are (1) describes performance with a focus on particular elements (attitudes, knowledge, skills); (2) provides a balanced message between positive elements and elements needing improvement; (3) provides recommendations to learners on how to improve their performance; (4) compares the observed performance with an expected standard of performance; (5) provides justification for the mark/score given; (6) uses language that is clear and easily understood; and (7) uses a nonjudgmental style. CONCLUSIONS: Assessors can use these quality indicators and recommendations to write high-quality narrative comments, thus reinforcing the appropriate documentation of trainees' performance, facilitating solid decision making about trainees' progression, and enhancing the impact of narrative feedback for both learners and programs.
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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 | 0.127 | 0.374 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.054 | 0.039 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".