The integration of a community service learning water project in a post-secondary chemistry lab
Bibliographic record
Abstract
Community Service Learning (CSL) is an experiential learning approach that integrates community service into student projects and provides diverse learning opportunities to reduce interdisciplinary barriers. A semester-long chemistry curriculum with an integrated CSL intervention was implemented in a Canadian university to analyze the potential for engagement and positive attitudes toward chemistry as a meaningful undertaking for 14 post-secondary students in the laboratory as well as for their 400 K-12 student partners in the community. Traditionally, introductory science experiments typically involve repeating a cookbook recipe from a lab book, but this CSL project allowed the post-secondary and K-12 students to work collaboratively to determine the physical and chemical properties and total dissolved solids in the water fountains from the K-12 students' schools. Post-instructional surveys were completed by all learners and were analyzed using a mixed methodological approach with both quantitative and qualitative methods. The expected audience that may be interested in this study are those involved in teaching chemistry in higher education and at the K-12 level as well as those interested in service learning, community and civic engagement, experiential learning, and development of transferable skills in chemistry. The results demonstrate that both groups of students report favorable engagement and attitudes towards learning chemistry and higher self-confidence levels on performing lab skills after the activity. Furthermore, both groups of students expressed interest in exploring future projects, which is indicative of the positive impact of CSL and the mutual benefits of the partnership.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".