Teamwork Cognitive Diagnostic Modeling

Teamwork relies on collaboration to achieve goals that exceed individual capabilities, with team cognition playing a key role by integrating individual expertise and shared understanding. Identifying the causes of inefficiencies or poor team performance is critical for implementing targeted interventions and fostering the development of team cognition. This study proposes a teamwork cognitive diagnostic modeling framework comprising 12 specific models-collectively referred to as Team-CDMs-which are designed to capture the interdependence among team members through emergent team cognitions by jointly modeling individual cognitive attributes and a team-level construct, termed <i>teamwork quality</i>, which reflects the social dimension of collaboration. The models can be used to identify strengths and weaknesses in team cognition and determine whether poor performance arises from cognitive deficiencies or social issues. Two simulation studies were conducted to assess the psychometric properties of the models under diverse conditions, followed by a teamwork reasoning task to demonstrate their application. The results showed that Team-CDMs achieve robust parameter estimation, effectively diagnose individual attributes, and assess teamwork quality while pinpointing the causes of poor performance. These findings underscore the utility of Team-CDMs in understanding, diagnosing, and improving team cognition, offering a foundation for future research and practical applications in teamwork-based assessments.

Authors

Institutions

Publication Details

Journal
Psychometrika
Published
2025-08-08
DOI
https://doi.org/10.1017/psy.2025.10036
Primary Topic
Educational and Psychological Assessments
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Teamwork Cognitive Diagnostic Modeling

Peida Zhan, Zhimou Wang, Gaohong Chu, Haixin Qiao
Psychometrika
Educational and Psychological Assessments
article

Teamwork Cognitive Diagnostic Modeling

Peida Zhan, Zhimou Wang, Gaohong Chu, Haixin Qiao
article en

Abstract

Teamwork relies on collaboration to achieve goals that exceed individual capabilities, with team cognition playing a key role by integrating individual expertise and shared understanding. Identifying the causes of inefficiencies or poor team performance is critical for implementing targeted interventions and fostering the development of team cognition. This study proposes a teamwork cognitive diagnostic modeling framework comprising 12 specific models-collectively referred to as Team-CDMs-which are designed to capture the interdependence among team members through emergent team cognitions by jointly modeling individual cognitive attributes and a team-level construct, termed <i>teamwork quality</i>, which reflects the social dimension of collaboration. The models can be used to identify strengths and weaknesses in team cognition and determine whether poor performance arises from cognitive deficiencies or social issues. Two simulation studies were conducted to assess the psychometric properties of the models under diverse conditions, followed by a teamwork reasoning task to demonstrate their application. The results showed that Team-CDMs achieve robust parameter estimation, effectively diagnose individual attributes, and assess teamwork quality while pinpointing the causes of poor performance. These findings underscore the utility of Team-CDMs in understanding, diagnosing, and improving team cognition, offering a foundation for future research and practical applications in teamwork-based assessments.

PsychometrikaVol. 90(4)
Zhejiang Normal University (CN)
Ministry of Education, India
Openalex Percentile: Top 79%
Educational and Psychological Assessments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.