Making curriculum review accountable – an impact study
Bibliographic record
Abstract
Purpose This paper reports on an impact study that aims to evaluate the impact of a curriculum review conducted previously on two courses in an undergraduate programme in a Hong Kong university. These courses are part of a mandatory academic English programme. Students were to take the first course (C1), followed by the second course (C2) to develop their academic English skills. A curriculum review was conducted on these courses earlier on, and interventions were introduced subsequently. This study is a tracer/impact study of the interventions introduced as the quality assurance process within the quality assurance process. Design/methodology/approach Using a mixed-methods approach, this impact study compares the performance of two cohorts of students in C2 who took C1 before (n = 780) and after the interventions (n = 586). To triangulate quantitative data, comments from students who took C2 and teachers (n = 5) who taught both cohorts are used to explore evidence of quality improvement in the C1-C2 alignment. Findings The findings suggest that quality reviews focusing on the alignment and progression of sequential courses have broader implications for ensuring programme coherence than reviews of individual courses. This study highlights the potential value of conducting impact evaluations of such reviews, which can contribute to the development of evidence-based policies in higher education. Research limitations/implications Educational practitioners and researchers are encouraged to undertake impact studies of quality reviews to ensure the accountability and effectiveness of the review processes themselves. Originality/value This study contributes to the existing literature on impact studies regarding quality enhancement in higher education.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".