CATME or ITP Metrics? Which One Should I Use for Design Team Development and Assessment?
Bibliographic record
Abstract
Abstract Abstract The characteristics of and the correlation of high performing teams with individual satisfaction and excellent task results have been studied extensively. Consequently, these characteristics have become selection criteria for new graduates, and development and performance benchmarks for employees and entrepreneurs alike, that define aspects of corporate culture. The University of Alberta Faculty of Engineering design, and engineering and safety risk management instructors have been using the Comprehensive Assessment of Team Member Effectiveness (CATME) from Purdue University for peer evaluation and team selection for the past several years. Different departments use CATME to varying degrees and for various purposes. The Individual and Team Performance Metrics Lab (ITP Metrics), at the University of Calgary Psychology Department in cooperation with the Schulich School of Engineering also at the University of Calgary have developed a set of research-backed team and leadership tools including peer evaluation tools and team formation tools for student and industry use. These on-line assessment tools differ in methodology, scope, emphasis user interface, feedback format, and cost. The selection of a peer evaluation and feedback system, for student use and evaluation, should consider the development of skills, as well as the reliability of the method to assess differential grades for students on teams when required. The method of data collection, the type of feedback and the contextual validity of the feedback may impact students’ development of useful team behaviours and personal strategies for working in team environments. In this contribution, a comparative analysis of CATME and ITP Metrics is provided. Instructor experiences with these assessment tools are then reported and discussed based on student pre and post skill self-evaluation in a design course where CATME and then ITP Metrics evaluations were used for sequential student cohorts.
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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 | 0.035 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".