"Is It My Turn?" Assessing Teamwork and Taskwork in Collaborative Immersive Analytics

TL;DR

This paper presents an augmented reality system for collaborative immersive analytics in machine learning, analyzing how interdisciplinary teams interact and collaborate during ML tasks, with insights into design for sustained teamwork.

Contribution

Introduces a novel AR-based system for collaborative ML modeling and provides empirical insights into teamwork dynamics in interdisciplinary settings.

Findings

System elicits sustained collaboration across six dimensions

Interdisciplinary backgrounds influence collaboration patterns

Design recommendations for immersive ML collaboration tools

Abstract

Immersive analytics has the potential to promote collaboration in machine learning (ML). This is desired due to the specific characteristics of ML modeling in practice, namely the complexity of ML, the interdisciplinary approach in industry, and the need for ML interpretability. In this work, we introduce an augmented reality-based system for collaborative immersive analytics that is designed to support ML modeling in interdisciplinary teams. We conduct a user study to examine how collaboration unfolds when users with different professional backgrounds and levels of ML knowledge interact in solving different ML tasks. Specifically, we use the pair analytics methodology and performance assessments to assess collaboration and explore their interactions with each other and the system. Based on this, we provide qualitative and quantitative results on both teamwork and taskwork during collaboration. Our results show how our system elicits sustained collaboration as measured along six distinct dimensions. We finally make recommendations how immersive systems should be designed to elicit sustained collaboration in ML modeling.