SELF- AND PEER-ASSESSMENTS OF TEAM-EFFECTIVENESS IN A FIRST YEAR ENGINEERING DESIGN COURSE
Bibliographic record
Abstract
Many first-year design courses in engineering take place in large classes (100-1000 students), where a significant portion of the student’s course grade is attributed to a team project. In these large classes most students receive limited, or no, personalized assessment or feedback to guide their ongoing learning of effectiveness in teams due to resource constraints (e.g. limited interaction time with instructors or teaching assistants). As a result, students are not provided a foundation upon which to continuously improve their effectiveness as they participate in different teams throughout their degree. A web-based tool is being designed to create a safe, virtual environment in which students can learn about their team-effectiveness competencies through the use of self- and peer-assessment in their project teams [1]. Specifically, this intervention provides students with a team-effectiveness framework to create a common language by which structured feedback can be provided based on observable behaviours and competencies.A pilot study to assess the utility of this framework in facilitating useful feedback was tested in the Winter 2012 term of a 250 student cornerstone design course, Praxis II, in first year Engineering Science. The objective of the study was to assess whether students can be guided to provide useful feedback on team-effectiveness to their teammates using our team-effectiveness framework.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".