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 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 | 0.000 | 0.000 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".