STUDENT PERCEPTIONS AND USE OF AN INVENTORY TO FACILITATE LEARNING OF INDIVIDUAL TEAM-EFFECTIVENESS
Bibliographic record
Abstract
Team-based projects have become a common method of modeling real-world experience and meeting required graduate attributes in engineering. In these projects, much of a student’s grade is attributed to work produced by an entire team, creating a need for instruction on how to work effectively as team members in addition to course-content instruction. A web-based tool is in development to create a virtual environment in which students can learn about and improve their individual team-effectiveness competencies through self- and peer-assessments. Framed as a guided reflection, these assessments are facilitated using an inventory which identifies 18 competencies along three aspects of team-effectiveness: Organisational, Relational and Communication competencies [1]. The inventory assesses observable behaviours that translate to specific levels of competency so as to provide a foundation for normalized self- and peer-assessments, as well as provide examples of how to improve. A study to assess student perceptions and use of the inventory was conducted in the Fall 2012 term in two upper year courses. The first course was a third-year course on energy systems that is required of all students in the Energy Option of Engineering Science and the second a fourth-year engineering leadership course which any engineering student can select as an elective. The objective of this study was to determine if students in a required engineering course perceived and used the inventory differently than those who self-selected into an engineering leadership course.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".