Effects of Sensemaking Translucence on Distributed Collaborative Analysis

Goyal, Nitesh; Fussell, Susan R.

doi:10.1145/2818048.2820071

Cited by 52 publications

(19 citation statements)

References 47 publications

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“…Studies on display design have shown how variations in externalizations influence complex collaboration. For example, a translucent interface meant to assist sensemaking, fostered collaboration by supporting the sharing of insights and preventing narrowing of focus ( Goyal and Fussell, 2016 ). This led to collaborators identifying more problem solving clues as well as finding a target in a criminal investigation task.…”

Section: Cognition Technology and Teamsmentioning

confidence: 99%

Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes

2016

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In this paper we advance team theory by describing how cognition occurs across the distribution of members and the artifacts and technology that support their efforts. We draw from complementary theorizing coming out of cognitive engineering and cognitive science that views forms of cognition as external and extended and integrate this with theorizing on macrocognition in teams. Two frameworks are described that provide the groundwork for advancing theory and aid in the development of more precise measures for understanding team cognition via focus on artifacts and the technologies supporting their development and use. This includes distinctions between teamwork and taskwork and the notion of general and specific competencies from the organizational sciences along with the concepts of oﬄoading and scaffolding from the cognitive sciences. This paper contributes to the team cognition literature along multiple lines. First, it aids theory development by synthesizing a broad set of perspectives on the varied forms of cognition emerging in complex collaborative contexts. Second, it supports research by providing diagnostic guidelines to study how artifacts are related to team cognition. Finally, it supports information systems designers by more precisely describing how to conceptualize team-supporting technology and artifacts. As such, it provides a means to more richly understand process and performance as it occurs within sociotechnical systems. Our overarching objective is to show how team cognition can both be more clearly conceptualized and more precisely measured by integrating theory from cognitive engineering and the cognitive and organizational sciences.

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Section: Cognition Technology and Teamsmentioning

confidence: 99%

Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes

2016

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“…The data visualisation pages focused on different themes, which aimed to spark discussion and exploration about potential relationships between government health expenditure, cancer, income and health risks. They explored: When all six tasks were finished the participants were asked to rate the usability of the interface using a SUS questionnaire [7,9], rate the workload required to complete the tasks using the raw NASA TLX questionnaire [16] and a collaboration questionnaire created by Goyal et al [14,15]. We used a between subject approach in order to accurately compare NASA TLX, collaboration questionnaire, SUS, and TCT scores.…”

Section: Methodsmentioning

confidence: 99%

“…Interactive technology provides effective support for users reflecting upon data in the process of sensemaking. Goyal et al [14] showed how implicit sharing can help users in sensemaking over a distance using a desktop interface. RAMPARTS [42] illustrated how mobile devices allowed for more effective sensemaking than a tabletop interface and Thaddeus [43] showed that effective interaction with information visualisation on tablets was possible.…”

Section: Data Exploration On Lhrds and Tabletsmentioning

confidence: 99%

Large displays and tablets

Gorkovenko

Lischke

Woźniak

2018

Proceedings of the 10th Nordic Conference on Human-Computer Interaction

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Data is pivotal to open government initiatives, where citizens are often expected to be informed and actively participate. Yet, it can be difficult for people to understand the meaning of data. Presenting data to the public in an appropriate way may also increase citizen's willingness to participate in data collection. Here we present a study which explores how large screens can support socially relevant data exploration. In a between subject laboratory experiment, we analysed how pairs of participants explored data visualisations on a highresolution display (LHRD) and a tablet. Our results indicate that LHRDs are less cognitively demanding, while tablets offer more shared control of the interface. Data exploration had limited effect on increasing comfort with sharing personal data but helped increase perceptions of trustworthiness within the data collection process. We observed that appropriately visualised data on either platform has significant potential to increase the public's understanding of large data sets.

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“…This body of work has inspired later studies that looked at different aspects of sensemaking. In the context of EDA, recent work looked at how analysts manage uncertainty [7]; how they explore various types of alternatives [19]; and how groups deal with trade-offs [5] and build common ground [14]. The results of this body of work are often conceptual models, task taxonomies and design recommendations.…”

Section: Related Workmentioning

confidence: 99%

How Domain Experts Structure Their Exploratory Data Analysis: Towards a Machine-Learned Storyline

Barczewski¹,

Bezerianos

Boukhelifa

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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Exploratory data analysis is an open-ended iterative process, where the goal is to discover new insights. Much of the work to characterise this exploration stems from qualitative research resulting in rich findings, task taxonomies, and conceptual models. In this work, we propose a machinelearning approach where the structure of an exploratory analysis session is automatically learned. Our method, based on Hidden-Markov Models, automatically builds a storyline of past exploration from log data events, that shows key analysis scenarios and the transitions between analysts' hypotheses and research questions. Compared to a clustering method, this approach yields higher accuracy for detecting transitions between analysis scenarios. We argue for incorporating provenance views in exploratory data analysis systems that show, at minimum, the structure and intermediate results of past exploration. Besides helping the reproducibility of the different analyses and their results, this can encourage analysts to reflect upon and ultimately adapt their exploration strategies.

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Effects of Sensemaking Translucence on Distributed Collaborative Analysis

Cited by 52 publications

References 47 publications

Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes

Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes

Large displays and tablets

How Domain Experts Structure Their Exploratory Data Analysis: Towards a Machine-Learned Storyline

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