Assessment of Teamwork in Laboratory Courses: What, When and How
Bibliographic record
Abstract
Teamwork might be considered as one of the most desired attributes of engineering graduates. From anassessment perspective, it might be one of the most difficult attributes to assess. Evaluation of teamworkrefers to the assessment of individuals on their ability to work in a team. However, in many cases theperformance of the team is taken as the only measure of teamwork. A plethora of literature has beendedicated to the understanding of the attribute; nevertheless, many laboratory instructional team facesthe dilemma on whether the designed assessment meet its requirements. In this article, the concept ofteamwork will be explored in the context of engineering laboratories. Following an exploratoryunderstanding of the attribute, mechanism to assess teamwork will be proposed. The attribute will bedecomposed into component skills and each skill element will be explored to determine assessmentrequirement by answering to a series of what, when and how questions. The outcome if the work will bevaluable for redesigning engineering laboratories which is the long-term objective of the work.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".