Automatic item generation for online measurement and evaluation: Turkish literature items
Bibliographic record
Abstract
Developments in the field of education have significantly affected test development processes, and computer-based test applications have been started in many institutions. In our country, research on the application of measurement and evaluation tools in the computer environment for use with distance education is gaining momentum. A large pool of items is required for computer-based testing applications that provide significant advantages to practitioners and test takers. Preparing a large pool of items also requires more effort in terms of time, effort, and cost. To overcome this problem, automatic item generation has been widely used by bringing together item development subject matter experts and computer technology. In the present research, the steps for implementing automatic item generation are explained through an example. In the research, which was based on the fundamental research method, first a total of 2560 items were generated using computer technology and SMEs in field of Turkish literature. In the second stage, 60 randomly selected items were examined. As a result of the research, it was determined that a large item pool could be created to be used in online measurement and evaluation applications using automatic item generation.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".