Acceptance and usage of e-assessment for UK awarding bodies – a research study
Bibliographic record
Abstract
This research provides an exploration of the UK e-Assessment market, in\nrelation to the UK Awarding Bodies, comparing findings with those of twelve\nmonths ago. It also elucidates on the key areas that have emerged since the\nfirst research was conducted.\nThis provides an insight into the remaining drivers and barriers to the adoption\nof e-Assessment, but also the widespread acceptance and adoption in the\nUK.\nWith 81% of all recognised Awarding Bodies being interviewed, this study is\nverging on an Awarding Body e-Assessment census based on sound\nresearch principles which will lead to continuing e-Assessment development.\nThe level of e-Assessment industry knowledge and uptake of programs within\nUK Awarding Bodies is at a much more advanced position compared to the\nprevious research findings. The pace of market change has clearly quickened.\nIt is possible to state that these findings will allow Awarding Bodies to revisit\ntheir thoughts on e-Assessment, altering the pace of market maturity in the\nshort to medium term.\nQuestions related to topics such as psychometrics, use of multiple choice\nquestions for higher levels of learning and e-Assessment location preference,\nhave provided responses which give a sign-post for the key emergent market\nneeds.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".