Measuring Moral Judgment of Education Profession Ethics Among Pre-Service Teachers of the School of Educational Sciences at the University of Jordan
Bibliographic record
Abstract
This study aimed at identifying moral judgment for education profession ethics among pre-service teachers programs of the School of Educational Sciences at the University of Jordan. Accordingly, the researchers developed a Moral Judgment Scale based on Kohlberg (1984), and on the Adult Moral Judgment Scale localized by Abdul-Fattah (2001). The scale consisted of five dilemmas related to ethics in education profession. Validity and reliability of the scale were confirmed. Study population involved (420) students from (5) programs in the academic year 2018/2019. The results of the study showed that the prevailing moral judgement is the second level (conventional morality) of Kohlberg with (308) students of (89.3%), and the fourth stage (Upholding/enforcing norms and laws) with (245) students of (71%) of total students. No students were in the first level, stage 1, stage 2, nor in stage 6. However, significant differences were observed in the level of moral judgment attributable to the academic achievement variable, while no differences were attributable to the program specialization. Moreover, significant differences were found in the moral judgment stages attributable to the academic achievement and students’ program. These findings argue for investment in pre-service teachers’ education to increase their moral judgment during education profession.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".