Are we talking about the same thing? – The use of “groups” and “teams” in Canadian Engineering Education Research
Bibliographic record
Abstract
Engineering graduates must be able to work well in a team. As such, significant effort has been made to include teamwork elements in engineering curriculum (e.g., cornerstone and capstone design courses) which has resulted in several interesting research publications related to teamwork in the engineering classroom. However, the discourse that we use to describe these research outcomes has not always been clear, using words like ‘teams’ and ‘groups’ to describe similar sets of people in similar activities. In this paper, we review the proceedings from the 2018-2022 CEEA/ACEG annual conference to determine the use of these words in Canadian Engineering Education research. The quantitative and qualitative findings of this review reveal diversity in language describing collaborative efforts in the classroom. We observe that some articles use the two terms interchangeably. We conclude with some discussion about the implications for limited distinction between the words
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".