Learning in the Laboratory: How Group Assignments Affect Motivation and Performance
Bibliographic record
Abstract
Team projects can optimize educational resources in a laboratory, but also create the potential for social loafing. Allowing students to choose their own groups could increase their motivation to learn and improve academic performance. To test this hypothesis, final grades and feedback from students were compared for the same course in two different years, one with and one without fixed group arrangements. Seniors of the United States Military Academy at West Point were divided into groups of three or four to complete chemical engineering lab projects during the fall semesters of 2014 and 2015. In the first year, 21 cadets remained in instructor-assigned teams for the duration of the course. The next year, 23 cadets were initially assigned groups, but then allowed to choose their own teammates for the second half of the semester. There was no significant difference in graded performance between the two years, although cadet feedback was interesting. When cadets had the option of choosing groups, 65% of survey respondents strongly agreed that their peers had contributed to their learning, versus 40% when groups were not allowed to change. When asked if their motivation to learn or their critical thinking ability had increased, fewer respondents in the second year strongly agreed with either statement. While these results are not conclusive, a wider implementation of team-focused learning currently underway at West Point will offer a robust dataset and insights on how to get group work to work well in science and engineering education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".