On-line formative assessment item banking and learning support
Bibliographic record
Abstract
Access to the Internet now makes it possible to deliver new services to centres around\nthe world including on-line formative assessments for use in the classroom. The results\nof these assessments provide information that can feed back into the learning and\nteaching process to highlight where improvements can be made. This can be a\nproductive tool in improving student learning and has significant potential to provide a\nricher educational experience. For this potential to be developed, large item banks\ncontaining questions with known operating characteristics are required so that valid and\nreliable assessments can be built. Questions can be stored as assessing particular\nlearning outcomes, levels of attainment, skills or other features, thus allowing specific\nfeedback to students and to their teachers indicating curriculum areas or skills in which\nstudents were relatively strong or weak.\nThe limitations on the types of questions that can be asked on-line and marked\nobjectively by computer limits the use that can be made of the results of on-line\nassessment. As a greater variation in the types of questions becomes available, so the\nuse that may be made of the results increases.\nAs item banks are used to build assessments for known cohorts of students and results\nare collated over a period of time it becomes possible to supply more meaningful\nfeedback to the users of assessments. The use of calibrated banks, and careful data\nmanagement will extend this use.\nThe future for the Cambridge on-line assessments will be determined by the opinions of\nthe teachers as to which forms of feedback are the most useful for themselves and their\nstudents.
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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 | 0.021 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.066 |
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".