Towards forming learning communities - Understanding the role of collaborative work in an undergraduate engineering program
Bibliographic record
Abstract
Learning communities may form in the engineering undergraduate programmes through meaningful teamwork activities and positive experiences working with peers. This research study was conducted as part of a programme evaluation to understand how students conceptualised the purpose of a learning community. Through a thematic analysis of six focus groups with students, we have a better understanding of how students find value in maintaining relationships with their peers that they may have worked within team-based work. Each focus group consisted of students studying at the same level of the undergraduate programme – ranging from first-year students to recently graduated students. We synthesized data from these groups to guide inferences about why and how students formed communities with their peers, the motivations to maintain those communities, and any curricular interventions that fostered the sense of community. The findings of this study allow us to understand how a learning community pedagogy can be integrated into the broader engineering curriculum to provide undergraduate engineering students with meaningful and coherent learning experiences.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".