Construction of Diagnostic Test in Mathematics on Addition and Subtraction Basic for Primary Students
Bibliographic record
Abstract
The objectives of this research were to construct and verify a mathematics diagnostic assessment on addition and subtraction basis for grade 4 students in Buriram Primary Educational Service Area Office 3. The research samples consisted of 440 Prathomsuksa 4 students selected by adapting the multi-stage random sampling method. The research instruments employed in this research comprised of two types of examinations constructed according to strand 1: number and algebra in Mathematics 1.1. The tests were to examine test and diagnostic test. The examining test was a subjective test, consisted of 80 items which divided into addition part for 40 items and subtraction part for 40 items. The diagnostic test was a multiple-choice with 4 choices tests, consisted of 48 items which divided into addition part for 24 items and subtraction part for 24 items. The diagnostic test. The results showed that the content validity, determined by the expert, had the IOC score at 0.60-1.00. The addition part in the 1st test had a difficulty score of 0.21-0.79, the discrimination score at 0.23-0.89, and the reliability score at 0.89. The subtraction part in the 2nd test had the difficulty score at 0.31-0.72, the discrimination score at 0.32-0.72 and the reliability scores at 0.32-0.81.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".