Item Analysis of the Rasch Model Items in the Final Semester Exam Indonesian Language Lesson
Bibliographic record
Abstract
This study aims to analyze and describe the characteristics of the items for the Final Semester Examination for Indonesian Language courses at the PKN STAN official high school. The item analysis in this study was carried out with a modern approach (item response theory) using the Rasch model with Winstep software. This research is a quantitative research with descriptive method. The data was obtained through the documentation method, namely 25 multiple choice questions with 314 respondents. The results of the analysis show that the unidimensional requirements are not met. There are several items in the test that do not meet the local independence requirements, while the monotonicity requirements have been met. The ability of the subject on this test is greater than the level of difficulty of the questions indicated. The reliability of the test is generally satisfactory with the quality of the items and the consistency of the subject's answers are quite good. Persons or subjects who do not fit are 28 people, while items that are not fit are 5 items. There are 11 items indicated bias.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".