Investigation of Pre-Service Science Teachers’ Attitudes towards Sustainable Environmental Education
Bibliographic record
Abstract
The purpose of the current study is to investigate pre-service science teachers’ sustainable environmental education attitudes and the factors affecting them in terms of some variables (gender and grade level). The study group of the current research is comprised of 154 pre-service teachers attending the Department of Science Education in the Faculty of Education of Aksaray University. The study employed the descriptive survey method, one of the qualitative research methods. As the data collection tool, “The Sustainable Environmental Education Attitude Scale” developed by Afacan and Demirci Güler (2011) was used in the study. The Cronbach alpha reliability of the scale was calculated to be α=.93. In the statistical analysis of the data, SPSS was used. In the analysis of the data, Independent Samples t-Test and One Way ANOVA were run. The analysis results revealed that sustainable environmental education attitudes frequency of the pre-service teachers is at the medium level. It was also found that the sustainable environmental education attitudes of the pre-service teachers do not vary significantly by gender; yet, they were found to be varying significantly depending on the grade level variable. It can be suggested that further research can attempt to determine the attitudes of pre-service teachers from different branches and to analyze different factors affecting sustainable environmental education attitude.
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.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".