Development of an Item Bank of Order and Graph by Applying Multidimensional Item Response Theory
Bibliographic record
Abstract
This study aimed to develop an item bank of Order and Graph of Mattayomsuksa 1 level (grade 7). The samples were 4,800 lower secondary students from 34 schools in northeastern area of Thailand, academic year 2011 chosen through multi-stage random sampling. The research tool used in the study was a multiple choicetest of an Order and Graph lesson by applying multidimensional item response theory. Parameter were analyzed by confi rmatory factor analysis by applying multidimensional normalogive model with guessing of the program normalogive harmonic analysis robust method (NOHARM). Discrimination power and Easiness intercept were equated through non–orthogonal procrustes method. The study results indicated that there were 59 items out of 140 passed the test standard. Key words: Item bank; Cognitive process; Multidimensional item response theory (MIRT)
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".