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

TL;DR

This study investigates the effectiveness of collaborative problem solving in mixed reality environments for visual graph analysis, highlighting the importance of benchmarks and the limitations of 3D representations.

Contribution

It introduces a comparative analysis of ad hoc and nominal pairs in mixed reality, emphasizing the role of task complexity and benchmarking in collaborative visual analysis.

Findings

Nominal groups serve as effective benchmarks for evaluation.

3D graph representation alone does not improve collaboration outcomes.

Task complexity influences the effectiveness of collaborative problem solving.

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.