Education in Green Chemistry: Incorporating Green Chemistry into Chemistry Teaching Methods Courses at the Universiti Sains Malaysia
Bibliographic record
Abstract
Green chemistry aims at preventing pollution and sustaining the earth; it is commonly practised in the production of industrial applications. While predominantly being applied in industrial applications it can be also adapted in education through laboratory-based experiments and classroom activities. It is also imperative to educate the future teachers (pre-service teachers) on green chemistry because these teachers have the power to access many generations of students near future. As a response to the UN declaration of a Decade of Education for Sustainable Development (UNDESD), at the School of Educational Studies, Universiti Sains Malaysia, green chemistry has been integrated into chemistry teaching methods and courses. In this chapter we first provide an argument why learning chemistry becomes relevant and we discuss (1) how green chemistry can be adapted in chemistry teaching methods course, (2) the feasibility of integrating green chemistry experiments, and (3) the effectiveness of green chemistry in enhancing environmental awareness and concern as well as bringing attitudinal, motivation and value change in solving environmental issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".