Adaptive Visualizations for Enhanced Data Understanding and Interpretation

Amyrotos, Christos

doi:10.1145/3450613.3459657

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…To tackle the challenges formulated in the research question above, our work follows the Design-Based Research (DBR) approach [14]. This approach had originally been devised for education research [2], but has since been applied in the areas of human-centred computing as well [1]. Returning to previous phases when this is meaningful due to the knowledge gained in later phases (cf.…”

Section: Research Approach Methods and Rationalementioning

confidence: 99%

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Scrutability of Intelligent Personal Assistants

Jeromela

2022

Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

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Intelligent personal assistants (IPAs) have become widely available, yet they remain primarily used for discrete, straightforward tasks. By contrast, both user studies and literature reviews indicate that IPAs of the future are to be personalised, proactive, and capable of performing elaborate undertakings. Such systems would have to be based on complex and dynamic user and context models. We believe that scrutability -i.e. the ability of the user to actively study and modify the models towards tuning personalisation -could emerge as an essential element of such a human-assistant interaction paradigm. Yet, to the best of our knowledge, no work so far has investigated how the principles of scrutability, as presented in [21], relate to the context and novel challenges raised by the proactive IPAs and how scrutability could facilitate effort-efficient control of the assistants. This paper introduces our vision of the confluence of the research fields of IPAs and scrutability, presents a diagram of the proposed interaction structure, and reanalyses data from user studies originally presented in [11,39] to better understand user expectations regarding scrutability and proactivity of IPAs.CCS Concepts: • Human-centered computing → Personal digital assistants; User models; Natural language interfaces; User centered design.

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Section: Research Approach Methods and Rationalementioning

confidence: 99%

“…Returning to previous phases when this is meaningful due to the knowledge gained in later phases (cf. [14]) is integral to the DBR, which is very advantageous given the partially unpredictable nature of interventions in human-centred research [1].…”

Section: Research Approach Methods and Rationalementioning

confidence: 99%

Scrutability of Intelligent Personal Assistants

Jeromela

2022

Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

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“…Second, research on BIV evaluation mostly focuses on usability. For example, Amyrotos noticed the importance of UX in BIV and proposed a user-centered framework, but his research focuses on usability such as effectiveness and accuracy [111]. However, good usability is not enough to create a good UX, because usability is only one part of the user experience.…”

Section: Motivationmentioning

confidence: 99%

UXBIV: An Evaluation Framework for Business Intelligence Visualization

Liu,

Zhang,

Yang

et al. 2023

IEEE Access

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Nowadays, visualization is becoming an essential part of data analysis. Business Intelligence Visualization (BIV) is a powerful tool that helps modern business flows faster and smoother than ever before. However, research on BIV evaluation is severely lacking; most evaluation studies for BIV focus on usability, which have limited aspects covered for customers' needs. The purpose of this research is to develop a UX framework that evaluates BIV (UXBIV), including decision-making experience and interactivity. First, we did a literature survey for good understanding of research progress in related fields, and established a conceptual framework. Second, we performed a case study that implemented this framework to demonstrate how our framework can be used in real business. Our analysis shows that this UXBIV framework is quite reasonable, and it can capture the differences among various BIV designs from the users' standpoints. This UXBIV framework can help design BIV and promote better decision-making on business affairs.

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“…Expertise may train users to search for more information in particular formats; consequently, such users may have the tendency to prefer those formats rather than the formats that cognitive fit theory might otherwise infer [25], [28]. Therefore, expertise has become an important user characteristic to consider in the design of adaptive visualizations [29]. Here, we consider the expertise of having a particular educational background: accounting or data science.…”

Section: Related Workmentioning

confidence: 99%

The Role of Expertise on Insight Generation from Visualization Sequences

Rosenthal

Chung

2022

2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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Data analysts often tediously create visualization sequences to derive insights about what they see. While recent AIdriven approaches generate sequences to optimize visualization appeal and individual user preferences, extended cognitive fit theory suggests that expertise and insight type will affect the visualizations that analysts prefer. To investigate the role of expertise on insight generation from visualization sequences, we asked data scientists and accountants to report their insights as they investigated two business datasets. We found that both groups frequently followed the visualization sequences in order. However, expertise played a role in predicting the types of visualizations that each group chose to visit when they had finished the sequence but had time remaining. We also found significant interaction effects of visualization type, insight type, and expertise when assessing the numbers of insights generated per participant. Based on these results, we recommend that AIdriven data visualization tools should incorporate expertise as a feature for predicting new visualizations to produce.

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Adaptive Visualizations for Enhanced Data Understanding and Interpretation

Cited by 4 publications

References 22 publications

Scrutability of Intelligent Personal Assistants

Scrutability of Intelligent Personal Assistants

UXBIV: An Evaluation Framework for Business Intelligence Visualization

The Role of Expertise on Insight Generation from Visualization Sequences

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