Datensatz:

Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis - Replication data

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Datum der Erstveröffentlichung

2024

Autor:innen

Piselli, Tommaso
Di Giacomo, Emilio
Montecchiani, Fabrizio

Andere Beitragende

Repositorium der Erstveröffentlichung

Universitätsbibliothek Stuttgart

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DOI (Link zu den Daten)
https://doi.org/10.18419/darus-4231
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Deutsche Forschungsgemeinschaft (DFG): 251654672

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Published

Zusammenfassung

This dataset contains the supplementary materials to our publication "Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis", where we report on a study we conducted. Please refer to publication for more details, also the abstract can be found at the end of this description. The dataset contains:

  1. The collection of graphs with layout used in the study

  2. The final, randomized experiment files used in the study

  3. The source code of the study prototype

  4. The collected, anonymized data in tabular form

  5. The code for the statistical analysis

  6. The Supplemental Materials PDF

Paper abstract: Problem solving is a composite cognitive process, invoking a number of systems and subsystems, such as perception and memory. Individuals may form collectives to solve a given problem together, in collaboration, especially when complexity is thought to be high. To determine if and when collaborative problem solving is desired, we must quantify collaboration first. For this, we investigate the practical virtue of collaborative problem solving. Using visual graph analysis, we perform a study with 72 participants in two countries and three languages. We compare ad hoc pairs to individuals and nominal pairs, solving two different tasks on graphs in visuospatial mixed reality. The average collaborating pair does not outdo its nominal counterpart, but it does have a significant trade-off against the individual: an ad hoc pair uses 1.46 more time to achieve 4.6 higher accuracy. We also use the concept of task instance complexity to quantify differences in complexity. As task instance complexity increases, these differences largely scale, though with two notable exceptions. With this study we show the importance of using nominal groups as benchmark in collaborative virtual environments research. We conclude that a mixed reality environment does not automatically imply superior collaboration. (2024)

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Computer and Information Science, Collaboration, Group Problem Solving, Network Visualization, Mixed Reality, Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing

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ISO 690GARKOV, Dimitar, Tommaso PISELLI, Emilio DI GIACOMO, Karsten KLEIN, Giuseppe LIOTTA, Fabrizio MONTECCHIANI, Falk SCHREIBER, 2024. Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis - Replication data
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RDF
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1. The collection of graphs with layout used in the study

2. The final, randomized experiment files used in the study

3. The source code of the study prototype

4. The collected, anonymized data in tabular form

5. The code for the statistical analysis

6. The Supplemental Materials PDF


Paper abstract: 
Problem solving is a composite cognitive process, invoking a number of systems and subsystems, such as perception and memory. Individuals may form collectives to solve a given problem together, in collaboration, especially when complexity is thought to be high. To determine if and when collaborative problem solving is desired, we must quantify collaboration first. For this, we investigate the practical virtue of collaborative problem solving. Using visual graph analysis, we perform a study with 72 participants in two countries and three languages. We compare ad hoc pairs to individuals and nominal pairs, solving two different tasks on graphs in visuospatial mixed reality. The average collaborating pair does not outdo its nominal counterpart, but it does have a significant trade-off against the individual: an ad hoc pair uses 1.46 more time to achieve 4.6 higher accuracy. We also use the concept of task instance complexity to quantify differences in complexity. As task instance complexity increases, these differences largely scale, though with two notable exceptions. With this study we show the importance of using nominal groups as benchmark in collaborative virtual environments research. We conclude that a mixed reality environment does not automatically imply superior collaboration. (2024) </dcterms:abstract>
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    <dc:contributor>Liotta, Giuseppe</dc:contributor>
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