COMPARITIVE STUDY OF THE ORGANIZATIONAL STRUCTURE OF ENGINEERING STUDENT TEAMS AND TEAM EFFECTIVENESS
Bibliographic record
Abstract
Abstract – Design-and-build competitions are integral to effective higher engineering education. Yet, there is not much research investigating if the organizational structures of engineering student teams and team effectiveness follow any trends. This paper delves into the possibility of this correlation by measuring parameters that contribute to effective teams. This research provides data that is used to judge best practices for engineering student teams. The findings from this paper can then be used as a basis for action when the students find a need for organization development in the future. Additionally, this analysis provides insight into teamwork in engineering. This could benefit 4th year design (a.k.a capstone) projects as well as innovative companies with similar settings. The core contributors to a team's effectiveness are leadership, direction, planning, knowledge transfer, and meetings for engineering student teams. Although parameters like communication and team culture are important, student teams generally have no problems in these areas. By comparing three organizational structures, it is concluded that in general engineering student teams are best when they follow a holocratic or flatter organizational structure as opposed to a strictly flat organizational structure.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".