Interpreting data visualizations is an essential skill in today's education, yet students often struggle with understanding unfamiliar formats. This study investigates how four learning materials - textbook, comic, video, and game - affect middle- and high school students' ability to interpret line charts, area charts, stacked area charts, and stream graphs. We conducted a comparative classroom study with 68 students, using pre- and post-tests, worksheet activities, and group discussions to assess learning outcomes and understanding. Our results show statistically significant improvement in students' understanding of stacked area charts and stream graphs, while no significant differences between the learning materials were found. This suggests that more factors than initially anticipated - such as engagement, motivation and active learning strategies - influence the learning outcome. The analysis of the worksheets revealed that while students could infer surface-level insights from charts, over 70% struggled to identify underlying patterns or relationships. Additionally, a common challenge across all learning materials was reading fatigue, which often led students to skim content, disengage, or misinterpret key information. These findings highlight the need for educational tools and approaches that foster deeper understanding of unfamiliar visualizations, reduce cognitive load, and encourage active engagement.

Efficient monitoring of machine downtimes and order throughput is critical for productivity in metal manufacturing. Existing shopfloor management boards are often either too detailed or too superficial, and lack adaptability to user needs and cognitive capacities. These limitations hinder operators’ and managers’ ability to detect and respond to unplanned downtimes in a timely manner. Therefore, we iteratively designed a near-real-time shopfloor board visualisation supporting users in efficiently identifying machine downtimes. We closely collaborated with a metal manufacturer over a couple of months to develop a tailored solution using an iterative user-centered design process, including semi-structured interviews, low- and high-fidelity prototyping, cognitive walkthroughs with experts, and a task-based usability study. The final shopfloor board visualisation adopts a card-based layout that presents key machine-level metrics such as throughput, order list, and production counts. Our findings of the user-centered design process show that clear hierarchies, intuitive colour coding, and minimalism can enhance shopfloor board visualisation design.

Long lead times in supply chain processes can result in significant tied-up working capital in inventory, limiting operational flexibility and profitability. To address this challenge, we present VAILO, a Visual Analytics Dashboard for Inventory and Logistics Operations, designed to support sales and operations managers in identifying inefficiencies and bottlenecks in supply chain workflows. Developed through a co-design process with domain experts, VAILO integrates two coordinated visualizations—a hierarchical treemap and a throughput diagram—to facilitate the exploration of material flow and lead time variability across industries and customers. The dashboard enables secure data uploads, customizable visual encodings, and interactive filtering to support user-driven analysis. We conducted a formative usability study with participants experienced in industrial contexts and explored a real-world usage scenario. While initial results suggest that VAILO may help surface actionable insights, they also highlight areas for improvement in onboarding and interaction design. Our findings offer a preliminary contribution toward the design of domain-specific visual analytics tools that aim to bridge the gap between complex data and decision-making in supply chain contexts.

Abstract Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit— NODKANT —for constructing modular node‐link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed‐methods between‐subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low‐level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step‐by‐step instructions to create a predefined layout, and the third group received a pre‐constructed representation. While free construction proved on average more time‐consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step‐by‐step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre‐constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/ .

As visualization literacy and its implications gain prominence, we need effective methods to prepare students for the variety of visualizations in an increasingly data-driven world. Recently, the potential of comics has been recognized in various data visualization contexts, including educational settings. We describe the development of a workshop in which we use our "comic construction kit" as a tool for students to understand various data visualization techniques through an interactive creative approach of creating explanatory comics. We report on our insights from holding eight workshops with high school students and teachers, university students, and lecturers, aiming to enhance the landscape of handson visualization activities that can enrich the visualization classroom. The comic construction kit and all supplemental materials are open source under a CC-BY license and available at https://fhstp.github.io/comixplain/vis4schools.html.

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize . VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars — Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website ( https://datavisyn.github.io/visAhoi/ ) that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix/heatmap, a scatterplot, and a treemap visualization.

Visual exploration of large and complex data is becoming increasingly important in different domains. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To make new forms of visualizations understandable and interpretable for a broad range of audiences, visualization onboarding methods can support users. However, it is unclear whether concrete or abstract materials yield better results to foster learning. In order to answer this question, we conducted a within-subject study with 40 students to compare abstract and concrete onboarding messages for a treemap visualization. The results show that (1) concrete onboarding messages are more helpful than abstract, whereas the length of the abstract messages is ranked higher; (2) abstract onboarding messages lead to more valuable descriptions; and (3) either concrete or abstract onboarding messages can lead to high valuable insights.

A typical problem in Visual Analytics (VA) is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user’s experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual analysis often have difficulties in correctly reading and interpreting information from novel visualization idioms. Visualization onboarding can support novices in learning how to use new digital technologies. Therefore, we developed an interactive step-by-step guide and applied the method to four visualization techniques—a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. Results using Amazon Mechanical Turk workers show that there is a need for onboarding, especially for more complex visualization techniques. We further investigated the perception and rating of a scrollytelling and a video tutorial for the most unfamiliar visualization—the parallel coordinates plot. A comparison between the three onboarding methods indicates that participants appreciated the easy-to-understand examples, the precise wording of the onboarding messages in a step-by-step manner, and the introduction of interaction concepts by highlighting the most relevant information over all onboarding methods. The video tutorial supported the introduction of unknown interaction techniques best.

Abstract Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four‐year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story‐finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/

Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace "diff" tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selected tables. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios.

Mobile devices are more and more used in parallel, esp. in the field of TV viewing as second screen devices. Such scenarios aim to enhance the viewers' user experience while watching TV. We designed and implemented a second screen prototype intended to be used in parallel to watching a TV documentary. It allows to interactively explore a combination of spatial and time-oriented data to extend and enrich the TV content. We evaluated our proto-type in a twofold approach, consisting of expert reviews and user evaluation. We identified different interaction habits in a second screen scenario and present its benefits in relation to documentaries.

While quantified-self applications and wearable sensors for running, cycling or strength training are receiving broad interest from science and industry, little attention has been paid to the increasingly popular climbing sport, so far. To fill this gap, specialized wrist-worn sensor devices for tracking climbers have been developed recently. To support climbers and make the best of the available sensor data use possible, we designed a set of interactive visual interfaces which provide detailed insights into training data and support self-assessments of various aspects of the climbing technique. Our approach consists of a mobile web application to be used during the training and a desktop tool for presentation and analysis. In our design study we conducted semi-structured interviews with climbers, developed a scenario-based prototype in D3.js and evaluated our prototype. The initial interviews, a formative expert review and a summative usability study indicate the importance of providing manual input possibilities in addition to the automatically detected data and visualization techniques showing an overview of their training data. The findings of this design study provide an understanding of how climbers will interact with quantified-self applications and what the individual requirements for such a system are.