Cooperative learning in organic chemistry increases student assessment of learning gains in key transferable skills
Bibliographic record
Abstract
Science and engineering educators and employers agree that students should graduate from college with expertise in their major subject area as well as the skills and competencies necessary for productive participation in diverse work environments. These competencies include problem-solving, communication, leadership, and collaboration, among others. Using a pseudo-experimental design, and employing a variety of data from exam scores, course evaluations, and student assessment of learning gains (SALG) surveys of key competencies, we compared the development of both chemistry content knowledge and transferable or generic skills among students enrolled in two types of large classes: a lecture-based format versus an interactive, constructive, cooperative learning (flipped classroom) format. Controlling for instructor, as well as laboratory and recitation content, students enrolled in the cooperative learning format reported higher learning gains than the control group in essential transferable skills and competency areas at the end of the term, and more growth in these areas over the course of the term. As a result of their work in the class, the two groups of students reported the most significant differences in their gains in the following areas: “interacting productively to solve problems with a diverse group of classmates,” “behaving as an effective leader,” “behaving as an effective teammate,” and “comfort level working with complex ideas.” Our findings clearly show that cooperative learning course designs allow students to practice and develop the transferable skills valued by employers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".