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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".