Measuring Teachers’ Adherence to Ethical Principles in Educational Assessment
Bibliographic record
Abstract
Although educational assessment is one of the main responsibilities of the teachers, there has been little research designed to examine ethical principles in the daily classroom assessment practices of the teachers. Using a descriptive survey research design, the purposes of the current study were to develop a measure of the teachers’ adherence to the ethical principles in educational assessment and identify teachers’ characteristics associated with it. Participants were 3557 teachers teaching grades 5 to 12 in public schools across all educational governorates in Oman. Principal components analysis of the teachers’ responses to an 11-items survey revealed three dimensions of the ethical principles in educational assessment: confidentiality, test integrity, and transparency. Internal consistency reliability ranged from .64 to .78. The correlations among the dimensions ranged from .19 to .32. Construct validity was evidenced by the statistically significant positive low correlation of .10 between the dimensions and knowledge of educational assessment ethics. Multivariate analyses of variance revealed statistically significant differences in levels of adherence to the ethical principles in educational assessment among teachers with respect to gender, educational qualification, teaching subject, teaching experience, and training in educational assessment. It was concluded that the measure developed in this study has the potential to provide educators and researchers with valuable information to understand teachers’ adherence to the ethical principles in educational assessment.
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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 | 0.026 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".