The Usability of SEEQ in Quality Evaluation of Arabic Secondary Education in Malaysia
Bibliographic record
Abstract
Evaluation of the quality of Arabic education in Malaysia needs to be conducted on a continuous basis to achieve the objectives of Arabic education. Student Evaluation of Education Quality (SEEQ) was proposed as one of the evaluation instruments of the quality of Arabic education. This study aimed to evaluate the usability of SEEQ in the context of Malaysian education, including its validity and reliability based on the collected local data. In this study, a total of 491 samples from Islamic National Secondary Schools (SMK(A)) throughout Malaysia were involved. Confirmatory Factor Analysis (CFA) and Cronbach’s alpha for internal consistency were performed using AMOS and SPSS.A new model of SEEQ (8 constructs and 25 of 31 items were retained after modifications) with good readings off it indices and alpha value was obtained. This study suggests using the new model of SEEQ as the indicator of the quality of Arabic education in Malaysia.
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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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".