“Oh, that’s where you are!” – Towards a Hybrid Asymmetric Collaborative Immersive Analytics System

Abstract

We present a hybrid Immersive Analytics system to support asymmetrical collaboration between a pair of users during synchronous data exploration. The system consists of an immersive Virtual Reality application, a non-immersive web application, and a real-time communication interface connecting both applications to provide features to facilitate the collaborators' mutual understanding and their ability to make (spatial) references. We conducted a real world case study with pairs of language students, encouraging them to use the developed system to investigate a large multivariate Twitter dataset from a sociolinguistic perspective within an explorative analysis scenario. Based on the results of usability scores, log file analyses, observations, and interviews, we were able to validate the approach in general, and gain insights into the users' collaboration with respect to awareness, deixis, and group dynamics. CCS Concepts: • Human-centered computing → Virtual reality; Collaborative interaction; Empirical studies in HCI ; Collaborative and social computing.

An Empirical Evaluation of Asymmetric Synchronous Collaboration Combining Immersive and Non-Immersive Interfaces Within the Context of Immersive Analytics

“…In addition to the insights and directions as presented in the current state-of-the-art, we are also motivated to further explore the matter of bridging interactive InfoVis and IA based on some of our initial investigations (Reski et al, 2020b). More specifically, we investigated the mixture of applying immersive and non-immersive interfaces within the scope of a real world case study in the context of the digital humanities, allowing pairs of language students to analyse language variability on social networks (Reski et al, 2020b).…”

“…In addition to the insights and directions as presented in the current state-of-the-art, we are also motivated to further explore the matter of bridging interactive InfoVis and IA based on some of our initial investigations (Reski et al, 2020b). More specifically, we investigated the mixture of applying immersive and non-immersive interfaces within the scope of a real world case study in the context of the digital humanities, allowing pairs of language students to analyse language variability on social networks (Reski et al, 2020b). Based on a sociolingustic context and an explorative data analysis scenario, i.e., undirected search without hypotheses (Aigner et al, 2011, Chapter 1.1), the immersed student analysed the social network data from a geospatial perspective, while the non-immersed student focused their efforts on aspects of textual analysis (Reski et al, 2020b).…”

“…More specifically, we investigated the mixture of applying immersive and non-immersive interfaces within the scope of a real world case study in the context of the digital humanities, allowing pairs of language students to analyse language variability on social networks (Reski et al, 2020b). Based on a sociolingustic context and an explorative data analysis scenario, i.e., undirected search without hypotheses (Aigner et al, 2011, Chapter 1.1), the immersed student analysed the social network data from a geospatial perspective, while the non-immersed student focused their efforts on aspects of textual analysis (Reski et al, 2020b). Both interfaces provided functionalities that allowed the students to send discrete signals to their peer, i.e., when they discovered something noteworthy in the data that they wanted to share, they could make a visual data annotation (Reski et al, 2020b).…”

“…Based on a sociolingustic context and an explorative data analysis scenario, i.e., undirected search without hypotheses (Aigner et al, 2011, Chapter 1.1), the immersed student analysed the social network data from a geospatial perspective, while the non-immersed student focused their efforts on aspects of textual analysis (Reski et al, 2020b). Both interfaces provided functionalities that allowed the students to send discrete signals to their peer, i.e., when they discovered something noteworthy in the data that they wanted to share, they could make a visual data annotation (Reski et al, 2020b). Based on the results of the user study, we were able to validate the usability of the presented interaction and collaborative between a pair of users where one was inside VR, while the other remained outside, each for their own dedicated data analysis purpose (Reski et al, 2020b).…”

“…Both interfaces provided functionalities that allowed the students to send discrete signals to their peer, i.e., when they discovered something noteworthy in the data that they wanted to share, they could make a visual data annotation (Reski et al, 2020b). Based on the results of the user study, we were able to validate the usability of the presented interaction and collaborative between a pair of users where one was inside VR, while the other remained outside, each for their own dedicated data analysis purpose (Reski et al, 2020b). Based on these prior insights and experiences as well as the various described literature throughout Section 2.1 and Section 2.2, our overall stance in regard to CIA has not changed and involves 1) envisioning a synergy between immersive and non-immersive analytics applications, 2) endorsing the mentality that different visualization and interaction approaches can satisfy different data exploration and analysis needs, and 3) encouraging collaboration between multiple users to support joint analytical reasoning and data understanding, independent of their role and background, i.e., experts and novices are considered similarly (Reski et al, 2020b).…”