An evaluation on the outcomes of the Sekolah Rakan Alam Sekitar(SERASI) programme in Sabah, Malaysia
Bibliographic record
Abstract
This study evaluated the implementation of SERASI Programme in Sabah in terms of attitude change and outcomes. Evaluation of attitude change focused on three attitude components namely cognitive, affective and behavioural. Intended and unintended outcomes of the programme were also evaluated. The evaluation showed that the implementation of SERASI in the 39 schools had enhanced and improved environmental attitude amongst teachers and students. Collective change of attitude among the teachers and students may have resulted in behaviours that in turn produced positive environmental outcomes. Based on the teachers’ years of service, it was found that there was a significant difference in environmental attitude after SERASI implementation. It was also found that there was no significant difference in environmental attitude between teachers who attended environmental education courses and those who did not. The results showed there was no significant difference in environmental attitude between teachers who teach environment-related subjects and non-environment related subjects, between graduate and non-graduate teachers, and between primary and secondary school teachers. For students, it was found that there was a significant difference in environmental attitude between leaders and non-leaders, and between primary and secondary school students. The results showed that there was a positive correlation between teachers’ understanding on SERASI and their environmental attitude. Positive correlations were found among the cognitive, affective and behavioural components of teachers’ and students’ environmental attitudes. Based on the results, 88.5% of teachers and 90.3% of students responded that their schools’ surroundings were more pleasant and cleaner after SERASI was implemented. Therefore, this particular outcome was the most obvious outcome of SERASI in the 39 schools. These findings were concluded by both qualitative and quantitative data analyses. There were other intended and unintended outcomes found in the research. For future research pertaining to the evaluation of SERASI Programme, studies should include more districts and schools, and other aspects of SERASI. The research findings are important to the organisers of SERASI Programme and to other relevant organisations working closely in the field of environmental education in Sabah.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".