Publikation: Collaborative Problem Solving in Mixed Reality : A Study on Visual Graph Analysis
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Datum
2026
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Deutsche Forschungsgemeinschaft (DFG): 251654672
Deutsche Forschungsgemeinschaft (DFG): EXC 2117 - 422037984
Deutsche Forschungsgemeinschaft (DFG): EXC 2117 - 422037984
Projekt
Quantitative Methods for Visual Computing
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
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Published
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IEEE Transactions on Visualization and Computer Graphics. IEEE. ISSN 1077-2626. eISSN 1941-0506. Verfügbar unter: doi: 10.1109/TVCG.2026.3671472
Zusammenfassung
Problem solving is a composite cognitive process, invoking a number of cognitive mechanisms, such as perception and memory. Individuals may form collectives to solve a given problem together in collaboration, especially when complexity is perceived to be high. To determine if and when collaborative problem solving is desired in the context of visual graph analysis, we compare ad hoc pairs to individuals and nominal pairs, when solving different tasks in mixed reality. We discuss the results of an experiment with 72 participants performed in two countries and three languages. We apply the concept of task instance complexity to quantify the visual demand of tasks used in the experiment. Our results show the importance of using nominal groups as a benchmark for evaluating collaborative virtual environments. We conclude that 3D graph representation is not sufficient to induce better collaborative results compared to the benchmark.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Collaboration, Controlled experiment, Groups, Graph analysis, Immersive environments, Problem solving
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ISO 690
GARKOV, Dimitar, Tommaso PISELLI, Emilio DI GIACOMO, Karsten KLEIN, Giuseppe LIOTTA, Fabrizio MONTECCHIANI, Falk SCHREIBER, 2026. Collaborative Problem Solving in Mixed Reality : A Study on Visual Graph Analysis. In: IEEE Transactions on Visualization and Computer Graphics. IEEE. ISSN 1077-2626. eISSN 1941-0506. Verfügbar unter: doi: 10.1109/TVCG.2026.3671472BibTex
@article{Garkov2026Colla-76598,
title={Collaborative Problem Solving in Mixed Reality : A Study on Visual Graph Analysis},
year={2026},
doi={10.1109/TVCG.2026.3671472},
issn={1077-2626},
journal={IEEE Transactions on Visualization and Computer Graphics},
author={Garkov, Dimitar and Piselli, Tommaso and Di Giacomo, Emilio and Klein, Karsten and Liotta, Giuseppe and Montecchiani, Fabrizio and Schreiber, Falk},
note={Additional Funding Information: <br /> 1) MUR PRIN: EXPAND: scalable algorithms for EXPloratory Analyses of heterogeneous and dynamic Networked Data: 2022TS4Y3N; <br /> 2) MUR PRIN: NextGRAAL: Next-generation algorithms for constrained GRAph visuALization: 2022ME9Z7; <br /> 3) Universita degli Studi di Perugia: MiRA: Mixed Reality and AI Methodologies for Immersive Robotics.}
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<dcterms:abstract>Problem solving is a composite cognitive process, invoking a number of cognitive mechanisms, such as perception and memory. Individuals may form collectives to solve a given problem together in collaboration, especially when complexity is perceived to be high. To determine if and when collaborative problem solving is desired in the context of visual graph analysis, we compare ad hoc pairs to individuals and nominal pairs, when solving different tasks in mixed reality. We discuss the results of an experiment with 72 participants performed in two countries and three languages. We apply the concept of task instance complexity to quantify the visual demand of tasks used in the experiment. Our results show the importance of using nominal groups as a benchmark for evaluating collaborative virtual environments. We conclude that 3D graph representation is not sufficient to induce better collaborative results compared to the benchmark.</dcterms:abstract>
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Kommentar zur Publikation
Additional Funding Information:
1) MUR PRIN: EXPAND: scalable algorithms for EXPloratory Analyses of heterogeneous and dynamic Networked Data: 2022TS4Y3N;
2) MUR PRIN: NextGRAAL: Next-generation algorithms for constrained GRAph visuALization: 2022ME9Z7;
3) Universita degli Studi di Perugia: MiRA: Mixed Reality and AI Methodologies for Immersive Robotics.
1) MUR PRIN: EXPAND: scalable algorithms for EXPloratory Analyses of heterogeneous and dynamic Networked Data: 2022TS4Y3N;
2) MUR PRIN: NextGRAAL: Next-generation algorithms for constrained GRAph visuALization: 2022ME9Z7;
3) Universita degli Studi di Perugia: MiRA: Mixed Reality and AI Methodologies for Immersive Robotics.
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
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Ja
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