Psychometric Properties of Social Perception of Mathematics: Rasch Model Analysis
Bibliographic record
Abstract
Social perception is an evaluation process, which uses any information available in order to form impressions, understanding, and judgments about others. It is also considered as an essential element of social skills. This study aims to examine the psychometric analysis of students’ social perceptions of mathematics using Rasch model analysis. This study uses a quantitative survey approach. The sample comprised 40 first year students at King Faisal University. The Rasch model is used because it is considered an effective tool for assessing constructs’ validity and reliability of the instrument. It also generalizes results and inferential studies. The developed questionnaire consists of six dimensions. Every dimension consists of six items. They are verifying the validity based on the Rasch model using item polarity, item fit, and dimensionality. In addition, the reliability was verified using person and item reliability, and item and person separation. The results of the Rasch model analysis show that the items of social perception of mathematics SPoM fit the model appropriately.
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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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".