Social and Emotional Learning as Experienced by Students in Undergraduate General Chemistry Labs
Bibliographic record
Abstract
Students can meticulously perform laboratory techniques and be proficient in writing lab reports yet report negative affective experiences within the chemistry teaching labs. This study addresses this contradiction by studying students’ experiences using a Social and Emotional Learning (SEL) framework to qualitatively and deductively analyze semi-structured student interviews (N=15). The interviews followed the students’ completion of introductory chemistry and focused on their experiences with the laboratories. What emerged from this analysis were the SEL competencies intertwining with all aspects of the teaching labs as students worked with their lab partners, peers, and teaching assistants. The labs provide a unique environment for students to apply their SEL skills, including effective relationship and self-management skills. Despite students using and relying on their SEL skills, they are not always supported in lab design, and this presents a barrier to student success and a positive learning experience associated with the teaching labs. Given this, there is a need to change chemistry laboratory teaching practices and curricula to emphasize support and learning outcomes related to SEL. These findings were used to implement changes to the first-year labs to support SEL competencies, such as designing and implementing a TA manual, providing students with tips for dividing lab procedures, and offering more resources including rubrics.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".