Instructor perceptions and reported practices around informal peer collaboration on homework among engineering students
Bibliographic record
Abstract
While team learning within engineering classrooms has been studied, minimal work has been done examining out-of-classroom collaboration to complete individual deliverables. However, such informal peer collaboration (IPC) is common among engineering undergraduates, and some evidence exists that low levels of IPC are associated with poorer learning outcomes. We aimed to explore beliefs, perceptions and actions toward IPC among engineering science instructors. We used a descriptive phenomenological approach to explore the instructors’ experiences of IPC. Data from semi-structured interviews was analysed using inductive thematic analysis. Instructors identified positive (e.g. gaining confidence) and negative (e.g. overconfidence) outcomes of IPC. They believed that students used IPC for a range of needs and identified factors (e.g. language spoken) influencing IPC groupings. Most instructors only defined academic misconduct in syllabi, despite using implicit strategies (e.g. withholding solutions) to promote/inhibit IPC. Specific IPC policies were rarely connected to instructors’ understanding of student motivations. Instructors view IPC as unavoidable and recognise that IPC may meet student needs while increasing or bypassing learning. Despite both using IPC as students and observing students now, instructors showed limited understanding of who participates. IPC may be an opportunity to increase learning among students; further research on barriers and pedagogy is warranted.
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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 | 0.005 | 0.024 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".