Guide Me in Analysis: A Framework for Guidance Designers

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues

Representation Effects and Loss Aversion in Analytical Behaviour: An Experimental Study into Decision Making Facilitated by Visual Analytics

This paper presents the results of an experiment into the relationship between the representation of data and decision-making. Three hundred participants online, were asked to choose between a series of financial investment opportunities using data presented in line charts. A single dependent variable of investment choice was examined over four levels of varying display conditions and randomised data. Three variations to line chart visualisations provided a controlled factor between subjects divided into three groups; -˜standard’ line charts, -˜tall’ line charts, and one dual-series line chart. The final results revealed a consistent main effect and two other interactions between certain display conditions and decision-making. The findings of this paper are significant to the study visualisation and to the field of visual analytics. This experiment was devised as part of a study into Analytical Behaviour, defined as decision-making facilitated by visual analytics - a new topic that encompasses existing research and real-world applications

Guidance in the human–machine analytics process

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making

Instruments for visualization of self, co, and socially shared regulation of learning using multimodal analytics:a systematic review

Abstract. This thesis presents a systematic literature review in the intersection of multimodal learning analytics, regulation theories of learning, and visual analytics literature of the last decade (2011- 2021). This review is to collect existing research-based instruments designed to visualize Self-Regulation of Learning (SRL), Co-Regulation of learning (CoRL), and Socially Shared Regulation of learning (SSRL) using dashboards and multimodal data. The inclusion and exclusion criteria used in this review addressed two main aims. First, to distil settings, instruments, constructs, and audiences. Second, to identify visualization used for targets (i.e., cognition, motivation, and emotion), phases (i.e., forethought, performance, and reflection), and types of regulation (i.e., SRL, CoRL, and SSRL). By following the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines, this thesis included 23 peer-reviewed articles out of 383 articles retrieved from 5 different databases searched in April 2021. The main findings from this literature review are (a) the included articles used theoretical grounding of SRL in all articles while CoRL is used only in 3 articles and SSRL only in 2 articles; (b) most articles used both teachers and students as the audience for visual feedback and operated in online learning settings; (c) selected articles focused mainly on visualizing cognition and motivation (17 articles each) as targets of regulation, while emotion as the target was applied only in 6 articles; (d) The performance phase was common to most of the articles and used various visualizations followed by reflection and forethought phases respectively. Simple visualizations, i.e., progress bar chart, line chart, color coding, are used more frequently than bubble chart, stacked column chart, funnel chart, heat maps, and Sankey diagram. Most of the dashboard instruments identified in the review are still improving their designs. Therefore, the results of this review should be put into the context of future studies to be utilized by researchers and teachers in recognizing the missing targets and phases of SRL, CoRL, and SSRL in visualized feedback. Addressing these could also assist them in giving timely feedback on students’ learning strategies to improve their regulatory skills

COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets

Visualisation of Origins, Destinations and Flows with OD Maps

We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two‐level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 105‐106 simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics