Identifying the Attitudes and Views of Social Sciences Teachers toward Values Education in Turkey
Bibliographic record
Abstract
Values are the foundation of culture. The aim of values education is to help learners improve their social tendenciesby adopting social and universal values, support character development, raise good citizens, and enhance academicefforts and achievement. In Turkey, values education is offered in the teaching-learning process of all coursesthrough formal curricula in primary and secondary schools. However, it is “social sciences” course that directlyrelates to values education and intends to teach at least one value in every chapter. This study aimed at identifyingsocial sciences teachers' attitudes toward values education, analyse difference in attitudes based on diverse variablesand examine teacher views on values education practices in social sciences course. It is a mixed method study thatwas conducted using triangulation method. The study group comprised 135 social sciences teachers who participatedin the study on quantitative level and 45 social sciences teachers on qualitative level. Results of the study indicatedthat the teachers held positive and high attitudes toward values education, there was no significant variation inattitudes toward values education based on gender, but attitudes differed based on professional experience while theteachers held positive views concerning values education practices implemented in social sciences class, consideringthem important. They stated that “being responsible” is the least adopted value among students.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".