Implementation and evaluation of a team-based electrochemistry module in a large undergraduate class
Bibliographic record
Abstract
Introductory chemistry courses at the undergraduate level offer students a foundation in chemistry principles and an opportunity to develop problem-solving skills. These principles and skill sets are widely applicable across disciplines, and thus first year chemistry courses are a gateway for many programs in science, technology, engineering, and mathematics (STEM). While first-year chemistry courses are essential for STEM students, the resulting large-enrollment classes in universities can lead to challenges in implementing active learning and in helping students to reach the course learning outcomes. The evolving field of chemistry education research (CER) offers data and insight for improving teaching and learning strategies at the undergraduate level. We propose and evaluate an electrochemistry team-based problem-solving module as an active learning component of a large, first-year, blended chemistry course. In this paper, we explore the process for developing the module and evaluate this learning approach through focus groups, a large class survey, and student experience interviews. Through a preliminary case study approach, our findings suggest the interactive module is useful for enhancing conceptual understanding and problem-solving in chemistry, and improving academic confidence in electrochemistry learning outcomes. Moreover, students valued their engagement with the team-based problem-solving modules as an opportunity to build community, learn collaboratively, and successfully approach relevant problems.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".