Transforming assessment and feedback case study: Embedding electronic assessment management
Bibliographic record
Abstract
In 2011 with our funding, the e-AFFECT project run by the Centre for Educational Development at Queen’s University Belfast initiated an institution-wide programme of assessment and feedback enhancement extending into 2014 and beyond. \n \nTaking an appreciative inquiry approach to change management, the centre encourages academic schools to identify what they do well in assessment and feedback using a set of educational principles as benchmarks. This process not only surfaces existing good practice but also enables academic teams to identify for themselves what needs to change. Centre staff then provide ongoing support as participating schools implement the technology-supported solutions that best meet their needs. \n \nOne of the first to take part was the School of English, a humanities discipline which piloted then adopted computer-assisted assessment (CAA) in a phonetics module plus electronic submission, marking and feedback (EMA) as standard practice in all modules. \n \nThis is one of a series of case studies developed by the Jisc Assessment and Feedback programme. \n
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".