The role of green chemistry activities in fostering secondary school students' understanding of acid–base concepts and argumentation skills
Bibliographic record
Abstract
In a world where environmental degradation is taking on alarming levels, understanding, and acting to minimize, the individual environmental impact is an important goal for many science educators. In this study, a green chemistry curriculum—combining chemistry experiments with everyday, environmentally friendly substances with a student-centered approach that includes student–student discussion—was tested for its potential to increase the understanding of acid–base concepts and argumentative skills. A quasi-experimental design was chosen intended to take into account teacher/school nested effects. The study involved three classes of 150 16 year old Form Four students (1 experimental, N = 50; 2 control, N = 100) from two Schools A and B serving students from the same sociocultural and economic backgrounds taught by two teachers (Teacher A in School A taught 1 experimental and 1 control; Teacher B in School B taught 1 control). An ANCOVA with a pre-test as a covariate showed a statistically significant treatment effect as measured by an acid–base concept understanding test. Additionally, qualitative analysis of an Argumentation Skill Test (AST) shows that the experimental students used higher levels of argumentation skills following treatment than their peers in the two control classes. Implications are discussed for integrating green chemistry into the secondary school chemistry curriculum to teach the content on acid–base and green chemistry as a tool to assist the construction of arguments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".