Prediction of individual learning curves across information visualizations

Confident usage of information visualizations is thought to be influenced by cognitive aspects as well as amount of exposure and training. To support the development of individual competency in visualization processing, it is important to ascertain if we can track users’ progress or difficulties they might have while working with a given visualization. In this paper, we extend previous work on predicting in real time a user’s learning curve—a mathematical model that can represent a user’s skill acquisition ability—when working with a visualization. First, we investigate whether results we previously obtained in predicting users’ learning curves during visualization processing generalize to a different visualization. Second, we study to what extent we can make predictions on a user’s learning curve without information on the visualization being used. Our models leverage various data sources, including a user’s gaze behavior, pupil dilation, and cognitive abilities. We show that these models outperform a baseline that leverages knowledge on user task performance so far. Our best performing model achieves good accuracies in predicting users’ learning curves even after observing users’ performance on a few tasks only. These results represent an important step toward understanding how to support users in learning a new visualization.     

How information visualization novices construct visualizations

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.     

Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user's specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.     

Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising

Clickstreams are visitors' paths through a Web site. Analysis of clickstreams shows how a Web site is navigated and used by its visitors. Clickstream data of online stores contains information useful for understanding the effectiveness of marketing and merchandising efforts, such as how customers find the store, what products they see, and what products they purchase. In this paper, we present an interactive visualization system that provides users with greater abilities to interpret and explore clickstream data of online stores. This system visualizes the effectiveness of Web merchandising from two different points of view by using two different visualization techniques: visualization of sessions by using parallel coordinates and visualization of product performance by using starfield graphs. Furthermore, this system provides facilities for zooming, filtering, color-coding, dynamic querying and data sampling. It also provides summary information along with visualizations, and by maintaining a connection between visualizations and the source database, it dynamically updates the summary information. To demonstrate how the presented visualization system provides capabilities for examining online store clickstreams, we present a series of parallel coordinates and starfield visualizations that display clickstream data from an operating online retail store. A framework for understanding Web merchandising is briefly explained. A set of metrics referred to as micro-conversion rates, which are defined for Web merchandising analysis in our previous work (Lee et al., Electronic Markets, 2000), is also explained and used for the visualizations of online store effectiveness.     

Evaluating 2D Flow Visualization Using Eye Tracking

Flow visualization is recognized as an essential tool for many scientific research fields and different visualization approaches are proposed. Several studies are also conducted to evaluate their effectiveness but these studies rarely examine the performance from the perspective of visual perception. In this paper, we aim at exploring how users’ visual perception is influenced by different 2D flow visualization methods. An eye tracker is used to analyze users’ visual behaviors when they perform the free viewing, advection prediction, flow feature detection, and flow feature identification tasks on the flow field images generated by different visualizations methods. We evaluate the illustration capability of five representative visualization algorithms. Our results show that the eye‐tracking‐based evaluation provides more insights to quantitatively analyze the effectiveness of these visualization methods.     

Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles

Providing tools that make visualization authoring accessible to visualization non‐experts is a major research challenge. Currently the most common approach to generating a visualization is to use software that quickly and automatically produces visualizations based on templates. However, it has recently been suggested that constructing a visualization with tangible tiles may be a more accessible method, especially for people without visualization expertise. There is still much to be learned about the differences between these two visualization authoring practices. To better understand how people author visualizations in these two conditions, we ran a qualitative study comparing the use of software to the use of tangible tiles, for the creation of bar charts. Close observation of authoring activities showed how each of the following varied according to the tool used: 1) sequences of action; 2) distribution of time spent on different aspects of the InfoVis pipeline; 3) pipeline task separation; and 4) freedom to manipulate visual variables. From these observations, we discuss the implications of the variations in activity sequences, noting tool design considerations and pointing to future research questions.     

Can differences in learning strategies explain the benefits of learning from static and dynamic visualizations?

The effects of dynamic and static visualizations in understanding physical principles of fish locomotion were investigated. Seventy-five students were assigned to one of three conditions: a text-only, a text with dynamic visualizations, or a text with static visualizations condition. During learning, subjects were asked to think aloud. Learning outcomes were measured by tests assessing verbal factual knowledge, pictorial recall as well as transfer. Learners in the two visualization conditions outperformed those in the text-only condition for transfer and pictorial recall tasks, but not for verbal factual knowledge tasks. Analyses of the think-aloud protocols revealed that learners had generated more inferences in the visualization conditions as opposed to the text-only condition. These results were mirrored by students’ self-reported processing demands. No differences were observable between the dynamic and the static condition concerning any of the learning outcome measures. However, think-aloud protocols revealed an illusion of understanding when learning with dynamic as opposed to static visualizations. Furthermore, learners with static visualizations tended to play the visualizations more often. The results stress the importance of not only using outcome-oriented, but also process-oriented approaches to gain deeper insight into learning strategies when dealing with various instructional materials.     

Uncovering strengths and weaknesses of radial visualizations--an empirical approach

Radial visualizations play an important role in the information visualization community. But the decision to choose a radial coordinate system is rather based on intuition than on scientific foundations. The empirical approach presented in this paper aims at uncovering strengths and weaknesses of radial visualizations by comparing them to equivalent ones in Cartesian coordinate systems. We identified memorizing positions of visual elements as a generic task when working with visualizations. A first study with 674 participants provides a broad data spectrum for exploring differences between the two visualization types. A second, complementing study with fewer participants focuses on further questions raised by the first study. Our findings document that Cartesian visualizations tend to outperform their radial counterparts especially with respect to answer times. Nonetheless, radial visualization seem to be more appropriate for focusing on a particular data dimension.     

Visualization rhetoric: framing effects in narrative visualization

Narrative visualizations combine conventions of communicative and exploratory information visualization to convey an intended story. We demonstrate visualization rhetoric as an analytical framework for understanding how design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation. We draw a parallel between narrative visualization interpretation and evidence from framing studies in political messaging, decision-making, and literary studies. Devices for understanding the rhetorical nature of narrative information visualizations are presented, informed by the rigorous application of concepts from critical theory, semiotics, journalism, and political theory. We draw attention to how design tactics represent additions or omissions of information at various levels-the data, visual representation, textual annotations, and interactivity-and how visualizations denote and connote phenomena with reference to unstated viewing conventions and codes. Classes of rhetorical techniques identified via a systematic analysis of recent narrative visualizations are presented, and characterized according to their rhetorical contribution to the visualization. We describe how designers and researchers can benefit from the potentially positive aspects of visualization rhetoric in designing engaging, layered narrative visualizations and how our framework can shed light on how a visualization design prioritizes specific interpretations. We identify areas where future inquiry into visualization rhetoric can improve understanding of visualization interpretation.     

Knowledge precepts for design and evaluation of information visualizations

The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a framework for the design and evaluation of information visualization systems, and demonstrate its use.     

Exploring Online Learners' Interactive Dynamics by Visually Analyzing Their Time‐anchored Comments

MOOCs (Massive Open Online Courses) are increasingly prevalent as an online educational resource open to everyone and have attracted hundreds of thousands learners enrolling these online courses. At such scale, there is potentially rich information of learners' behaviors embedded in the interactions between learners and videos that may help instructors and content producers adjust the instructions and refine the online courses. However, the lack of tools to visualize information from interactive data, including messages left to the videos at particular timestamps as well as the temporal variations of learners' online participation and perceived experience, has prevented people from gaining more insights from video‐watching logs. In this paper, we focus on extracting and visualizing useful information from time‐anchored comments that learners left to specific time points of the videos when watching them. Timestamps as a kind of metadata of messages can be useful to recover the interactive dynamics of learners occurring around the videos. Therefore, we present a visualization system to analyze and categorize time‐anchored comments based on topics and content types. Our system integrates visualization methods of temporal text data, namely ToPIN and ThemeRiver, which can help people understand the quality and quantity of online learners' feedback and their states of learning. To evaluate the proposed system, we visualized time‐anchored commenting data from two online course videos, and conducted two user studies participated by course instructors and third‐party educational evaluators. The results validate the usefulness of the approach and show how the quantitative and qualitative visualizations can be used to gain interesting insights around learners' online learning behaviors.     

Provenance and annotation for visual exploration systems

Exploring data using visualization systems has been shown to be an extremely powerful technique. However, one of the challenges with such systems is an inability to completely support the knowledge discovery process. More than simply looking at data, users will make a semipermanent record of their visualizations by printing out a hard copy. Subsequently, users will mark and annotate these static representations, either for dissemination purposes or to augment their personal memory of what was witnessed. In this paper, we present a model for recording the history of user explorations in visualization environments, augmented with the capability for users to annotate their explorations. A prototype system is used to demonstrate how this provenance information can be recalled and shared. The prototype system generates interactive visualizations of the provenance data using a spatio-temporal technique. Beyond the technical details of our model and prototype, results from a controlled experiment that explores how different history mechanisms impact problem solving in visualization environments are presented     

Exploring the Visualization Design Space with Repertory Grids

There is an ongoing discussion in the visualization community about the relevant factors that render a visualization effective, expressive, memorable, aesthetically pleasing, etc. These factors lead to a large design space for visualizations. To explore this design space, qualitative research methods based on observations and interviews are often necessary. We describe an interview method that allows us to systematically acquire and assess important factors from subjective answers by interviewees. To this end, we adopt the repertory grid methodology in the context of visualization. It is based on the personal construct theory: each personality interprets a topic based on a set of personal, basic constructs expressed as contrasts. For the individual interpretation of visualizations, this means that these personal terms can be very different, depending on numerous influences, such as the prior experiences of the interviewed person. We present an interviewing process, visual interface, and qualitative and quantitative analysis procedures that are specifically devised to fit the needs of visualization applications. A showcase interview with 15 typical static information visualizations and 10 participants demonstrates that our approach is effective in identifying common constructs as well as individual differences. In particular, we investigate differences between expert and nonexpert interviewees. Finally, we discuss the differences to other qualitative methods and how the repertory grid can be embedded in existing theoretical frameworks of visualization research for the design process.     

Information visualizations for knowledge acquisition: The impact of dimensionality and color coding

Information visualizations – interactive graphical representations of large amounts of abstract data which do not have a natural visual representation – have mainly been used to support information retrieval. This article investigates whether information visualizations are also suitable for fostering knowledge acquisition as well as how information visualizations, from a cognitive perspective, have to be designed to be efficient learning tools. An experimental study provided evidence that information visualizations support knowledge acquisition. In addition, with regard to the appropriate design, the empirical results showed that two-dimensional information visualizations are better suited for supporting processes of knowledge acquisition than three-dimensional ones and that color-coded information visualizations slightly increase performance in a knowledge test compared to monochromatic ones.     

Where is the teacher? Digital analytics for classroom proxemics

The term Classroom Proxemics refers to how teachers and students use classroom space, and the impact of this and the spatial design on learning and teaching. This study addresses the divide between, on the one hand, substantial work on proxemics based on classroom observations and, on the other hand, emerging work to design automated feedback that helps teachers identify salient patterns in their use of the classroom space. This study documents how digital analytics were designed in service of a senior teacher's practice-based inquiry into classroom proxemics. Indoor positioning data from four teachers were analysed, visualized and used as evidence to compare three distinct learning designs enacted in a physics classroom. This study demonstrates how teachers can make effective use of such visualizations, to gain insight into their classroom practice. This is evidenced by (a) documenting teachers' reflections on visualizations of positioning data, both their own and that of peers and (b) identifying the types of indicator (operationalized as analytical metrics) that foreground the most useful information for teachers to gain insight into their practice.     

浅析我国网络教育在线辅导的现状

随着信息时代和网络时代的到来,以信息技术为核心的知识经济占据着主导地位。教育终身化、人才科技化,导致了传统的课堂教学模式已经不能完全满足社会对人才的需求。网络教学凭借灵活性、自主性和资源共享等特点,为社会各层次的学习需求者提供了终身学习的机会和便利条件,降低了学习成本,提高了社会效益,较好解决了成人学习工学矛盾这一突出问题。为了帮助学生更好的学习,各网络教育学院提供了多种类型的学习参考资料,但这并不能满足学习者的需要,还应提供与学习过程密切相关的辅导、答疑、导学、促学等非学术性学习服务,对每一个学习者提供个性化的指导和帮助。目前,我国远程教育学生规模不断上升,如何为学生提供多方位、全过程的在线学习辅导是提高我国远程教育教学质量的关键问题。     

Interactive tree comparison for co-located collaborative information visualization

In many domains increased collaboration has lead to more innovation by fostering the sharing of knowledge, skills, and ideas. Shared analysis of information visualizations does not only lead to increased information processing power, but team members can also share, negotiate, and discuss their views and interpretations on a dataset and contribute unique perspectives on a given problem. Designing technologies to support collaboration around information visualizations poses special challenges and relatively few systems have been designed. We focus on supporting small groups collaborating around information visualizations in a co-located setting, using a shared interactive tabletop display. We introduce an analysis of challenges and requirements for the design of co-located collaborative information visualization systems. We then present a new system that facilitates hierarchical data comparison tasks for this type of collaborative work. Our system supports multi-user input, shared and individual views on the hierarchical data visualization, flexible use of representations, and flexible workspace organization to facilitate group work around visualizations.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

Finding a Clear Path: Structuring Strategies for Visualization Sequences

Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data. We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers’ perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis.     

Visual Debugging Techniques for Reactive Data Visualization

Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the “spaghetti code” of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step‐by‐step. On‐demand annotations overlay the output visualization to expose relevant state and scale mappings in‐situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first‐time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.