Challenges of the Fennema-Sherman Test in the International Comparisons
Bibliographic record
Abstract
The shortened version of Fennema-Sherman test is used to measure attitudes toward mathematics in several international testing settings like TIMSS and PISA. On the basis of Classical item analysis and Confirmatory Factor Analysis in the different achievement levels and sets of countries, it is suggested that there are two items on the Fennama-Sherman test which should be discarded due to cultural and achievement considerations. Items “Mathematics is more difficult for me than for many of my classmates” and “Mathematics is not one of my strengths” are too complicated for test takers who belong to the lowest quartile of achievement. These items also seem to carry culturally sensitive elements especially in East Asian countries where fairly good students answer illogically due to such negative wordings. Alternative possibilities for test items are recommended.
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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 | 0.074 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".