The difference between estimated and perceived item difficulty: An empirical study
Bibliographic record
Abstract
Test development is a complicated process that demands examining various factors, one of them being writing items of varying difficulty. It is important to use items of a different range of difficulty to ensure that the test results accurately indicate the test-taker's abilities. Therefore, the factors affecting item difficulty should be defined, and item difficulties should be estimated before testing. This study aims to investigate the factors that affect estimated and perceived item difficulty in the High School Entrance Examination in Türkiye and to improve estimation accuracy by giving feedback to the experts. The study started with estimating item difficulty for 40 items belonging to reading comprehension, grammar, and reasoning based on data. Then, the experts' predictions were compared with the estimated item difficulty and feedback was provided to improve the accuracy of their predictions. The study found that some item features (e.g., length and readability) did not affect the estimated difficulty but affected the experts' item difficulty perceptions. Based on these results, the study concludes that providing feedback to experts can improve the factors affecting their item difficulty estimates. So, it can help improve the quality of future tests and provide feedback to experts to improve their ability to estimate item difficulty accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".