Quantifying the Relationship Between Team Dysfunctions and Performance in Capstone Projects
- URL: http://arxiv.org/abs/2608.24634v1
- Date: Tue, 25 Aug 2026 14:48:11 GMT
- Title: Quantifying the Relationship Between Team Dysfunctions and Performance in Capstone Projects
- Abstract summary: This paper investigates the association between team dysfunctions and performance in the context of an engineering Capstone program.<n>Using survey data collected from student teams, we quantify team dynamics based on Lencioni's model of the five dysfunctions of a team.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper investigates the association between team dysfunctions and performance in the context of an engineering Capstone program. Capstone programs are considered fundamental in assessing the readiness of engineering and computer science students for professional practice, evaluating not only technical knowledge but also essential competencies such as teamwork, communication, design, and organization. Despite the recognized importance of team dynamics in such settings, their relationship with project performance remains insufficiently understood. Using survey data collected from student teams, we quantify team dynamics based on Lencioni's model of the five dysfunctions of a team, with all measures reverse-coded so that higher values indicate more positive team behaviors (i.e., lower dysfunction). The results indicate a weak-to-moderate relationship between team dynamics and performance. Each dysfunction score was computed as the sum of three corresponding survey items. All items were coded such that higher values indicate more positive team behaviors (i.e., lower dysfunction). Therefore, each score ranges from 3 to 9, where higher values reflect healthier team dynamics. This study analyzes empirical evidence on team effectiveness in project-based learning environments and offers insights for the design of Capstone programs. In particular, it highlights the importance of goal alignment and focus on deliverables while emphasizing that team dynamics should be considered alongside technical and contextual factors.
Related papers
- From Perception to Action: An Interactive Benchmark for Vision Reasoning [51.11355591375073]
Causal Hierarchy of Actions and Interactions (CHAIN) benchmark designed to evaluate whether models can understand, plan, and execute structured action sequences grounded in physical constraints.<n> CHAIN shifts evaluation from passive perception to active problem solving, spanning tasks such as interlocking mechanical puzzles and 3D stacking and packing.<n>Our results show that top-performing models still struggle to internalize physical structure and causal constraints, often failing to produce reliable long-horizon plans and cannot robustly translate perceived structure into effective actions.
arXiv Detail & Related papers (2026-02-24T15:33:02Z) - What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect [0.0]
We analyze a large-scale dataset from the real-time strategy game Age of Empires II.<n>We find that certain individuals consistently improve team outcomes beyond what their technical skills predict.<n>Our results demonstrate that social skills and familiarity interact in a complementary, rather than additive, way.
arXiv Detail & Related papers (2025-06-04T21:46:50Z) - Improving Question Embeddings with Cognitive Representation Optimization for Knowledge Tracing [77.14348157016518]
Research on KT modeling focuses on predicting future student performance based on existing, unupdated records of student learning interactions.<n>We propose a knowledge-tracking cognitive representation optimization (CRO-KT) model that uses dynamic programming algorithms to optimize the structure of cognitive representation.
arXiv Detail & Related papers (2025-04-05T09:32:03Z) - Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance [7.591509098751302]
The relationship between individuals' behavioral and physiological markers and their combined influence on overall team performance remains poorly understood.<n>Our findings reveal a strong connection between team performance and the predictability of a team member's future actions.<n> Contrary to conventional wisdom that high-performing teams are highly synchronized, our results suggest that physiological and behavioral synchronizations among team members have a limited correlation with team performance.
arXiv Detail & Related papers (2025-01-25T21:37:17Z) - Assessing Teamwork Dynamics in Software Development Projects [2.823770863747379]
This study investigates teamwork dynamics in student software development projects through a mixed-method approach.<n>We analyzed individual contributions across six project phases, comparing self-reported and actual contributions to measure discrepancies.<n>Findings reveal that teams with minimal contribution discrepancies achieved higher project grades and exam pass rates.
arXiv Detail & Related papers (2025-01-21T08:23:46Z) - ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and Predicting Conversational Turn-taking Patterns [25.049072387358244]
We develop a computational model of conversational turn-taking within self-organized teams.
By bridging the gap between individual personality traits and team communication patterns, our model has the potential to inform theories of team processes.
arXiv Detail & Related papers (2024-11-23T01:27:01Z) - Code Collaborate: Dissecting Team Dynamics in First-Semester Programming Students [3.0294711465150006]
The study highlights the collaboration trends that emerge as first-semester students develop a 2D game project.
Results indicate that students often slightly overestimate their contributions, with more engaged individuals more likely to acknowledge mistakes.
Team performance shows no significant variation based on nationality or gender composition, though teams that disbanded frequently consisted of lone wolves.
arXiv Detail & Related papers (2024-10-28T11:42:05Z) - Detecting and Optimising Team Interactions in Software Development [58.720142291102135]
This paper presents a data-driven approach to detect the functional interaction structure for software development teams.
Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model.
We show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios.
arXiv Detail & Related papers (2023-02-28T14:53:29Z) - Task Formulation Matters When Learning Continually: A Case Study in
Visual Question Answering [58.82325933356066]
Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge.
We present a detailed study of how different settings affect performance for Visual Question Answering.
arXiv Detail & Related papers (2022-09-30T19:12:58Z) - An Empirical Investigation of Commonsense Self-Supervision with
Knowledge Graphs [67.23285413610243]
Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models.
We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
arXiv Detail & Related papers (2022-05-21T19:49:04Z) - Knowledge-driven Data Construction for Zero-shot Evaluation in
Commonsense Question Answering [80.60605604261416]
We propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks.
We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks.
We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks.
arXiv Detail & Related papers (2020-11-07T22:52:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.