Thomas Hurtut

Students in higher education often face challenges related to motivation, self-regulation, and engagement. This article presents TIRIA, a user-centered learning analytics dashboard designed for Moodle and adaptable to other Learning Management Systems (LMSs), and reports a formative evaluation of its high-fidelity prototype. The design followed a Design Thinking methodology informed by a targeted literature review, interviews with 10 instructors and 9 students, and iterative prototyping. TIRIA integrates visual analytics, personalized feedback, and a gamification layer (points, badges, goal-setting, and a virtual assistant), aligned with Self-Regulated Learning (SRL) theory and interpreted through Self-Determination Theory (SDT). Because the evaluation used a Figma prototype populated with mock data rather than a deployed integration, the study reports perceptions rather than learning outcomes. Six undergraduate students completed think-aloud sessions, a semi-structured interview, and a survey combining the System Usability Scale (SUS), a Technology Acceptance Model (TAM) measure, and two five-item measures adapted from learning analytics quality indicators and from gamification research. Participants rated usability highly (SUS = 93.75, PEOU = 4.75) and consistently valued organizational features, while responses to the gamification layer were markedly polarized (individual means ranging from 1.2 to 5.0). The article contributes a documented design case mapping features to SRL phases and SDT constructs, formative evidence supporting an opt-in approach to gamification, and implementation considerations covering privacy and Moodle integration. Findings are exploratory and require confirmation through deployment in an authentic learning environment.

The reconstruction in Fukushima offers a plan that does not exist anywhere in the world. The implications of this reconstruction, presented as a boon, are nevertheless numerous, both on an environmental and human level. It involves drastic choices that are deeply inscribed in the landscape, without, for the moment, generating the expected dynamics. The implementation of large-scale photovoltaics is also questioned, as is the return encouraged by the authorities of the population to territories that are still contaminated. In this chapter, we rethink the nature of these choices, their motivations, and their consequences through quantitative and qualitative analysis enabled by monthly access to the field since time T0. A geo-ethnographic approach allows us to embrace the complexity of the factors at play. In addition to regular interviews with residents and those involved in reconstruction, a map (the methodology of which is specified in a dedicated paragraph) has been made in order to visually reflect the transformations observed on-site and to facilitate their analysis.

In the digital landscape, the ubiquity of data visualizations in media underscores the necessity for accessibility to ensure inclusivity for all users, including those with visual impairments. Current visual content often fails to cater to the needs of screen reader users due to the absence of comprehensive textual descriptions. To address this gap, we propose in this paper a framework designed to empower media content creators to transform charts into descriptive narratives. This tool not only facilitates the understanding of complex visual data through text but also fosters a broader awareness of accessibility in digital content creation. Through the application of this framework, users can interpret and convey the insights of data visualizations more effectively, accommodating a diverse audience. Our evaluations reveal that this tool not only enhances the comprehension of data visualizations but also promotes new perspectives on the represented data, thereby broadening the interpretative possibilities for all users.

Data-driven stories (DDS) are digital forms of storytelling that arrange data and visualizations to communicate a narrative of information to an audience. They have been growing rapidly over the past decades. As a result, a great degree of versatility appears in the forms of published DDS. The recent structures of DDS are more complex, respecting their arrangement, composition, features, and inner parts. In the current academic research, neither storytelling techniques nor any taxonomies suggest visual mechanisms to distinguish between different layouts, compositions, and arrangements. The lack of an expressive visual solution that integrates different parts of DDS under one structure prevents the authors from trying more alternative design paths in the story design process. In this proposed work, we unify all the constructing parts of DDS to define the narrative structure as a visually structured representation of the DDS narrative, which is formed and designed by their constructing elements. This solution proposes a design language consisting of a set of design rules that integrate the visual elements to represent the DDS narrative structure. Our evaluation of the audit process out of 100 DDS examples confirms that the design language is comprehensive, expressive, and versatile. Additionally, we developed DataStoryDesign, a system that incorporates this visual solution to facilitate prototyping and storyboarding DDS for a team of DDS authors. The preliminary result of the exploratory evaluation indicates that such a solution is effective in prototyping and storyboarding DDS. In addition, our findings confirmed that the existence of our design language improves the visual communication between different personas in the DDS production workflow.

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

The rise of open data in the cultural domain is democratizing access to complex datasets usually presented as large multivariate and multilayered graphs. However, the exploration of such datasets is challenging for laypersons. The objective of this work is to develop and evaluate a new method for exploring and understanding a specific type of multilayered graph that combines a large bipartite graph with a set of tree structures. This paper proposes MuzLink, an interactive visualization tool that allows the user to navigate, search, locate, and compare collaborative and influential relationships between musical artists through the exploration of musical adaptations. The proposed tool is based on a set of connected timelines visualizing how an artist’s collaborations, inspirations, and influences evolved over time. This design study is conducted in close collaboration with BAnQ, the national library and archives agency of the Quebec government. A controlled user study, done with a group of BAnQ users, and two case studies, show how the proposed approach is capable of performing a considerable set of analytical and exploratory tasks.

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high‐quality three‐dimensional (3D) data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that reproduce the visual characteristics of the examples along some directions. To cope with memory limitations and computation complexity that are inherent to both high resolution and 3D processing on the GPU, only 2D textures referred to as ‘slices’ are generated during the training stage. These synthetic textures are compared to exemplar images via a perceptual loss function based on a pre‐trained deep network. The proposed network is very light (less than 100k parameters), therefore it only requires sustainable training (i.e. few hours) and is capable of very fast generation (around a second for 256³ voxels) on a single GPU. Integrated with a spatially seeded pseudo‐random number generator (PRNG) the proposed generator network directly returns a color value given a set of 3D coordinates. The synthesized volumes have good visual results that are at least equivalent to the state‐of‐the‐art patch‐based approaches. They are naturally seamlessly tileable and can be fully generated in parallel.

In the context of custom map design, handling more artistic and expressive tools has been identified as a carto-graphic need, in order to design stylized and expressive maps. Based on previous works on style formalization, an approach for specifying the map style has been proposed and experimented for particular use cases. A first step deals with the analysis of inspiration sources, in order to extract ‘what does make the style of the source’, i.e. the salient visual characteristics to be automatically reproduced (textures, spatial arrangements, linear stylization, etc.). In a second step, in order to mimic and generate those visual characteristics, existing and innovative rendering techniques have been implemented in our GIS engine, thus extending the capabilities to generate expressive renderings. Therefore, an extension of the existing cartographic pipeline has been proposed based on the following aspects: 1- extension of the symbolization specifications OGC SLD/SE in order to provide a formalism to specify and reference expressive rendering methods; 2- separate the specification of each rendering method and its parameterization, as metadata. The main contribution has been described in (Christophe et al. 2016). In this paper, we focus firstly on the extension of the cartographic pipeline (SLD++ and metadata) and secondly on map design capabilities which have been experimented on various topographic styles: old cartographic styles (Cassini), artistic styles (watercolor, impressionism, Japanese print), hybrid topographic styles (ortho-imagery & vector data) and finally abstract and photo-realist styles for the geovisualization of costal area. The genericity and interoperability of our approach are promising and have already been tested for 3D visualization.