Supporting Student Learning and Experiences in the Lab: (How) Should We Design Their Groups?
Bibliographic record
Abstract
Abstract Undergraduate science students spend a substantial amount of time working in their laboratory groups, and instructors want to make evidence-based decisions on how to best set up these groups. Despite several studies on group composition, the evidence appears to be quite context-specific, and very little has been published about lab groups. Further, many studies focus solely on conceptual learning; however, the lab is an important venue for also supporting non-content outcomes such as confidence, process skills, team skills, and attitudes. Thus, in our introductory course on molecules, cells, and physiology we were interested in the impact of group composition, on a spread of student outcomes. Students were either placed into groups by the instructor, or self-selected into groups. To assess the impact of group composition on student outcome, we collected pre/post data from >500 students over 2 semesters. Our measures assess conceptual knowledge, confidence in lab skills, attitudes toward group learning, lab grades, gender, year of study, and (via open-ended questions) student perspectives. Using a multiple regression approach, we established models that predict student outcomes based on their individual attributes and on their lab group attributes. Surprisingly, the hetero/homogeneity of the initial group, and whether the groups were student- or instructor-selected, did not affect student outcomes in these models. Further MANCOVA analysis demonstrated that student interaction outside of the lab time was the strongest predictor of positive student attitudes toward group learning. Student perspectives on group formation are mixed, and suggest that a simple and flexible choice approach may best support our students. Overall, these findings have clear implications for our course design and instructional choices: we should focus our efforts to promote positive student interactions, rather than worrying about initial composition.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".