FOSTERING TEAMWORK SKILLS USING COLLABORATION SOFTWARE IN ENGINEERING DESIGN EDUCATION
Bibliographic record
Abstract
The ability to work in a professional team is an essential social competence of engineers, who must collaborate on common tasks, with shared goals. Social competence has many aspects that are difficult to define and evaluate. Based on a theoretical framework of social competence, we identify several specific attributes and indicators that can be used to develop and evaluate social competencies related to teamwork in professional engineering. The specific attributes are professional project management, team interaction and professional documentation. These attributes are fostered in several ways. Team communication and coordination is fostered through explicit team roles (e.g. moderator, secretary, project manager, etc.), explicit requirements for project planning and scheduling, requiring professional documentation, and using computer tools to support these collaborative activities. These strategies develop competencies in communication, teamwork, presenting and understanding information, and using collaboration tool. At a higher level, these competencies contribute to each team member understanding the roles and contributions of the other team members, developing a shared understanding of the team’s position, and negotiating within the team and with external parties to reach sound decisions and conclusions. This paper describes and compares experiences using collaboration software tools to support teamwork activities in undergraduate design projects at Karlsruhe Institute of Technology (KIT) and Western University. Collaboration tools include Microsoft Project, Microsoft SharePoint, Sakai, and wikis. The paper discusses the selection of appropriate tools, the formalization of their use, and methods used to evaluate student competence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".