EXPERIENCES WITH INTER-YEAR CAPSTONE DESIGN TEAMS
Bibliographic record
Abstract
Design teams in industry are composed of individuals with diverse backgrounds at various stages of their careers. A unique set of group dynamics will be created with one member, likely someone with sufficient experience, assuming the responsibility of being the team leader. Design teams formed in engineering classes within the university setting typically consist of individuals at the same stage of their academic training, thus students do not experience the same group dynamics as they will find in industry. In an attempt to give undergraduate engineering students this experience, inter-year design teams were formed from engineering students registered in courses representing different stages of completion of the engineering degree. Students registered in the final-year design course were expected to assume the roles of team leaders or coleaders. This paper will discuss a number of issues that were observed with inter-year capstone design teams. It has been concluded that the disadvantages of inter-year design teams outweigh the advantages.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".