Privacy policies are long, complex, and rarely read, which limits their effectiveness in informed consent. We investigate scrollytelling, a scroll-driven narrative approach, as a privacy policy presentation format. We built a prototype that interleaves the full policy text with animated visuals to create a dynamic reading experience. In an online study (N = 454), we compared our tool against text, two nutrition-label variants, and a standalone interactive visualization. Scrollytelling improved user experience over text, yielding higher engagement, lower cognitive load, greater willingness to adopt the format, and increased perceived clarity. It also matched other formats on comprehension accuracy and confidence, with only one nutrition-label variant performing slightly better. Changes in perceived understanding, transparency, and trust were small and statistically inconclusive. These findings suggest that scrollytelling can preserve comprehension while enhancing the experience of policy reading. We discuss design implications for accessible policy communication and identify directions for increasing transparency and user trust.

AI research in healthcare often promotes disruptive innovations for diagnosis and care, yet the infrastructural and clinical realities of many public health systems raise questions about whether such transformations are sustainable. We present an exploratory qualitative study with ten Parkinson’s disease (PD) specialists and decision-makers in Ecuador’s public healthcare system—a low-resource setting—examining how they perceive disruptive AI approaches for PD. Our findings show that infrastructural constraints shape not only AI adoption, but also stakeholders’ capacity to imagine disruptive technological futures. Participants favored AI systems that support existing clinical workflows over stand-alone predictive systems for early diagnosis. We discuss methodological and ethical implications for responsible, sustainable AI design and argue for revalorizing non-disruptive, support-oriented AI as a legitimate goal for low-resourced public healthcare systems.

As algorithmic systems increasingly mediate human activities across diverse domains, they shift more responsibility for data collection onto users, fundamentally altering the nature of data work. This paper examines the implications of this shift by investigating student-led data collection and automated feedback interpretation using a mobile, multimodal learning analytics (MMLA) tool designed to coach oral presentation skills. Our findings reveal that while this user-controlled data collection provides greater flexibility, it also imposes speculative labor, compelling students to adjust their behavior to align with perceived standards of good data even when such changes are unwarranted. The study highlights the often-overlooked informal labor involved in managing the socio-material conditions of data collection, emphasizing the need for MMLA tools that offer adaptive support and guidance. These insights extend to algorithmic system design in educational and professional contexts, advocating for systems that balance user autonomy with workload-minimizing guidance to achieve equitable accountability.

Visualizing academic lineage is valuable for understanding the transmission of knowledge and the development of disciplines over time.We present Mentorship Navigator, an interactive visualization tool that allows users to explore academic lineages shaped through mentorship.Our tool leverages data from the Math Genealogy Project to examine mentor-mentee relationships dynamically.It also incorporates data from OpenAlex to explore the academic contributions of thesis advisors and their descendants.By supporting interactive and incremental construction of a scholar's genealogy, Mentorship Navigator provides a nuanced view of scholarly trajectories, contributing to a better understanding of their influence on contemporary academic thought.

Learning to program poses significant challenges, not only to learners but also teaching challenges to instructors. Several previous approaches to facilitate learning to program or analyze algorithms have employed visuals and visualizations, but they are limited in interactivity and in the ability of instructors and learners to customize the visuals to their learning goals. In this work, we present the design and implementation of P-Inti, an interactive constructive learning aid designed to allow learners to use a visual canvas to explore aspects of the state, control flow and execution of a program or algorithm. Instructors can also use the tool to generate interactive visual explanations for code and support quick switching between different algorithms.

Academic advising brings numerous benefits to the mission of Higher Education Institutions. One central and challenging duty of advisors is course recommendation for term planning. This task requires both knowledge of the study programs as well as a thorough analysis of the students’ unique circumstances. Limited time and a large student population make this task overwhelming. As a result, an important body of research has sought to expedite term planning via data-oriented decision-support tools. The impact of such tools on students has been extensively studied. However, the advisors’ perspective remains largely unexplored. We contribute to redressing this gap by studying how a grade prediction tool shapes academic advisors’ approach to course recommendation. We found that while the advisors’ usual strategies tend to prevail, their recommendations largely depend on the advisee’s historical performance. That said, advisors also acknowledge the limitations of grades as a measure of academic success.

A large body of work has sought to create effective ways of communicating and interacting with visual information. This paper presents insights into this research landscape based on the proceedings from the International Symposium on Visual Information Communication and Interaction (VINCI). We analyze several aspects of VINCI’s scholarship, including key contributors, emerging topics, and collaboration and citation patterns. Our findings reveal the dynamic character of VINCI and its advancements, highlighting its interdisciplinary nature with contributions from diverse institutions and countries, with the majority of authors engaging in both domestic and international collaborations. The insights from this study can inform future research directions and support more fruitful collaborations among VINCI researchers.

Relational datasets capture insights into complex networks of entities and their interconnections. The analysis of such datasets is critical for various domains, from social and biological networks to scientific research. The complexity and interdependencies inherent to relational datasets present significant challenges for analysts aiming to explore and understand such data. These challenges are particularly notable for individuals lacking expertise in data visualization tools and techniques, as well as those without training in deriving complex relations between the entities contained within the dataset. In this paper, we present a visualization tool designed to facilitate this kind of exploration. We illustrate the use of our tool with a dataset on the scientific production of the VINCI symposium.

Appropriate communication and understanding of political data are key to achieve a healthy dialogue between civil society and political institutions. This is particularly important for data generated by bodies that most directly represent citizens, such as congresses, senates, parliaments, and other forms of legislatures. Visualization tools have the potential to support the exploration and understanding of such data and make it more accessible and appealing to the general public. With this vision, we present LegisLatio, an interactive visualization tool that enables open-ended analysis of legislative roll-call vote data from multi-party electoral systems. We describe the design of the tool and illustrate its effectiveness with data generated by the Ecuadorian legislature. We also discuss the findings of a qualitative observation that hints at LegisLatio’s potential to promote citizen engagement and participation.

Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools.

We present an interactive visualization tool that enables exploration and comparative analyses of election data in multi-partisan systems. We motivate and explain our design in the context of the Ecuadorian political landscape. We demonstrate the tool with data from Ecuador’s three most recent presidential elections. Our tool enables both relative and absolute comparisons of the election results.

We present a scrollytelling visualization that explains the concept of “voting power”, which refers to the influence that a group of voters have, relative to the geographical area they live in, on an election’s outcome. We explain this in the context of Ecuador, a country with electoral districts of varied sizes and populations (and, thus, varied voting power). Our visualization is designed to explain that a bigger territory does not necessarily imply more votes. Understanding this concept is particularly important in Ecuador, a country that officially depicts elections results through maps that use color hue to indicate the winning candidate of a given geographical area.

Previous research has identified trade-offs when it comes to designing visualization tools. While constructive "bottom-up' tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.

The emergence of tools that support fast-and-easy visualization creation by non-experts has made the benefits of InfoVis widely accessible. Key features of these tools include attribute-level operations, automated mappings, and visualization templates. However, these features shield people from lower-level visualization design steps, such as the specific mapping of data points to visuals. In contrast, recent research promotes constructive visualization where individual data units and visuals are directly manipulated. We present a qualitative study comparing people's visualization processes using two visualization tools: one promoting a top-down approach to visualization construction (Tableau Desktop) and one implementing a bottom-up constructive visualization approach (iVoLVER). Our results show how the two approaches influence: 1) the visualization process, 2) decisions on the visualization design, 3) the feeling of control and authorship, and 4) the willingness to explore alternative designs. We discuss the complex trade-offs between the two approaches and outline considerations for designing better visualization tools.

iVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, is a web-based pen-and-touch interface that graphically supports construction of interactive visualizations. iVoLVER is designed to enable data extraction from different types of artifacts (e.g., photos) and to use that data to generate original representations of that data. People can create visualizations from data that is not structured in traditional formats without the need of textual programming or sitting at their desk. This demonstration shows how iVoLVER visualizations are constructed and also demonstrates the possible uses of iVoLVER in several contexts.

We present the design and implementation of iVoLVER, a tool that allows users to create visualizations without textual programming. iVoLVER is designed to enable flexible acquisition of many types of data (text, colors, shapes, quantities, dates) from multiple source types (bitmap charts, webpages, photographs, SVGs, CSV files) and, within the same canvas, supports transformation of that data through simple widgets to construct interactive animated visuals. Aside from the tool, which is web-based and designed for pen and touch, we contribute the design of the interactive visual language and widgets for extraction, transformation, and representation of data. We demonstrate the flexibility and expressive power of the tool through a set of scenarios, and discuss some of the challenges encountered and how the tool fits within the current infovis tool landscape.

Information Visualization (InfoVis) often supports the analysis of structured data that is organized in documents with specific formats such as databases, Excel tables, or comma-separated files. Informal analyses that take place without anticipation and away from the desktop, however, might involve the use of data contained in digital artifacts that lack this structure (e.g., photographs, bitmaps, web pages). Such artifacts cannot provide immediate input for most existing visualization systems, as the data they contain does not exist as a set of variables with associated values. This research seeks to explore new opportunities in the design and implementation spaces of InfoVis authoring tools to support visualization in opportunistic scenarios. This document briefly defines the Opportunistic Visualization (OpportuVis) domain and describes iVoLVER, a research prototype that supports the construction of interactive visuals from non-traditional data sources. Future stages of this endeavor include the evaluation of iVoLVER from two perspectives: its analytical support and its usability features.

Proposed as “data analysis anywhere, anytime, from anything”, Opportunistic Information Visualization (Opportu-Vis) [1] seeks to provide analytical support in scenarios where the data of interest is not explicitly available and has to be retrieved from digital artifacts that are not traditionally used as data sources. Examples include raster images, web pages, vector files, and photographs. This showpiece presents how iVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, provides support in such settings. We briefly describe the overall construction approach of the tool in scenarios where different digital artifacts are used to compose interactive visuals. All of this becomes possible by using the data extraction capabilities of iVoLVER together with the elements of its visual language.

One of the key promises of Learning Analytics research is to create tools that could help educational institutions to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, dependance estimation, curriculum coherence, dropout paths and load/performance graph. The description of these techniques is accompanied by its application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

Multimodal Learning Analytics is a field that studies how to process learning data from dissimilar sources in order to automatically find useful information to give feedback to the learning process. This work processes video, audio and pen strokes information included in the Math Data Corpus, a set of multimodal resources provided to the participants of the Second International Workshop on Multimodal Learning Analytics. The result of this processing is a set of simple features that could discriminate between experts and non-experts in groups of students solving mathematical problems. The main finding is that several of those simple features, namely the percentage of time that the students use the calculator, the speed at which the student writes or draws and the percentage of time that the student mentions numbers or mathematical terms, are good discriminators be- tween experts and non-experts students. Precision levels of 63% are obtained for individual problems and up to 80% when full sessions (aggregation of 16 problems) are analyzed. While the results are specific for the recorded settings, the methodology used to obtain and analyze the features could be used to create discriminations models for other contexts.