The validity of the assessment for learning measurement instrument for Ethiopian middle school mathematics teachers
Bibliographic record
Abstract
This article reports on a study that examined the validity of the Assessment for Learning Measurement Instrument (AfLMi). Data were gathered from 176 middle school mathematics teachers in Ethiopia. Confirmatory factor analysis and graded response model (GRM) IRT analysis were performed using Mplus 8.6. The original correlated four-factor model was compared to four competing models: one-factor, correlated three-factor, second-order, and bifactor models. The results showed that the bifactor model outperformed the other models, indicating that the AfLMi predominantly measures one general practice, i.e. assessment for learning, in the Ethiopian context, with little evidence that the four specific AfL strategies can be used independently as subscales. Furthermore, the IRT analysis revealed that the AfLMi, as a unidimensional measure of AfL practice, is composed of items with acceptable item discrimination indices and difficulty parameters. The AfL measurement instrument has excellent construct validity and reliability for assessing the AfL practices of mathematics teachers.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".