Bias Geographic Location of Math National Examination in Junior High School: Analysis of Differential Item Functioning (DIF)
Bibliographic record
Abstract
Diversity is a hot issue discussed in the world of education.With its diversity, Indonesia has great potential to study such things as the geographical diversity equalization factors that affect the quality of education.Implementation of national examinations (NE) as a benchmark and standards from primary to secondary education have a different condition for each location, such as Daerah Istimewa Yogyakarta (DIY) representing the West and Nusa Tenggara Timur (NTT) representing the western region of Indonesia.The focus of this research is to find out how much difference the ability of junior high school students in Indonesia in terms of geographical location.This study uses five DIF detection methods for mathematics NE 2013/2014 school year to analyze students' different abilities.The analysis results show that, in general, the NE questions for the 2013/2014 academic year benefit the focal group / NTT on Algebra, Geometry, and Statistics/Probability material, although with lower ability compared to group reference /DIY.With the analysis carried out, policymakers can take corrective steps to focus more on fixing problems, facilities, and resources teacher power so that problem inequality from aspect geographical there is no future again in Indonesia.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".