Assessing Teamwork Skills: Can a Computer Algorithm Match Human Experts?
Bibliographic record
Abstract
Teamwork skills are commonly evaluated by human assessors, which can be logistically challenging and resource intensive. Technological advancements provide an opportunity for a new assessment method – virtual behavioural simulations with self-scoring algorithms. This study explores whether a rule-based algorithm can match human assessors at evaluating teamwork skills. 206 undergraduate students completed a virtual simulation assessment, where they interacted with “teammates” (represented by chatbots) using natural language. In this study, students’ teamwork skills were assessed independently by a computer algorithm and two human experts based on the transcripts of their conversations with “teammates” (chatbots). The relative accuracy of these assessments was evaluated against peer- and self-evaluations of teamwork. The assessment scores generated by the algorithm and human experts were highly correlated with each other and were comparable in their ability to predict teamwork. The scores generated by the algorithm were slightly more correlated with peer-evaluations than those generated by human experts ( r = .25 and r = .17, respectively; p = .21). The results indicate that AI-based techniques offer a promising method of skill assessment to support learning and acquisition teamwork skills.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".