The validity of the assessment for learning measurement instrument for Ethiopian middle school mathematics teachers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.348 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it