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Mashael AlKadi

dblp:336/6741 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2025
0000-0003-0377-1815ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Computing education · 100%
Computer graphics and multimedia
2 papers
Visualization and visual analytics · 100%
Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › visual computing education
visualization education
1.022024
Challenges and Opportunities in Data Visualization Education: A Call to Action · IEEE Trans. Vis. Comput. Graph. 2024
Understanding Barriers to Network Exploration with Visualization: A Report from the Trenches · IEEE Trans. Vis. Comput. Graph. 2023
Visualization and visual analytics
graph visualization
0.712023
Understanding Barriers to Network Exploration with Visualization: A Report from the Trenches · IEEE Trans. Vis. Comput. Graph. 2023
Empirical software engineering
developer studies
0.712023
Understanding Barriers to Network Exploration with Visualization: A Report from the Trenches · IEEE Trans. Vis. Comput. Graph. 2023
Visualization and visual analytics › visualization literacy
visualization education
0.212024
Challenges and Opportunities in Data Visualization Education: A Call to Action · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

workshop study · 2.0observation · 2.0interaction logging · 2.0
YearPublicationVenuePosition
2025 Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts
abstract
Abstract In this paper, we introduce instructional comics to explain concepts and routines in data visualization tools. As tools for visual data exploration proliferate, there is a growing need for tailored training and onboarding demonstrating interfaces, concepts, and interactions. Building on recent research in visualization education, we detail our iterative process of designing instructional comics for four different types of instructional content. Through a mixed‐method eye‐tracking study involving 20 participants, we analyze how people engage with these comics when using a new visualization tool, and validate our design choices. We interpret observed behaviors as unique affordances of instructional comics, supporting their use during tasks and complementing traditional instructional methods like video tutorials and workshops, and formulate six guidelines to inform the design of future instructional comics for visualization.
Magdalena Boucher, Mashael AlKadi, Benjamin Bach, Wolfgang Aigner
Comput. Graph. Forum2
2024 Challenges and Opportunities in Data Visualization Education: A Call to Action
abstract
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.
Benjamin Bach, Mandy Keck, Fateme Rajabiyazdi, Tatiana Losev, Isabel Meirelles, Jason Dykes, Robert S. Laramee, Mashael AlKadi, Christina Stoiber, Samuel Huron, Charles Perin, Luiz Augusto de Macêdo Morais, Wolfgang Aigner, Doris Kosminsky, Magdalena Boucher, Søren Knudsen, Areti Manataki, Jan Aerts, Uta Hinrichs, Jonathan Roberts 0002, Sheelagh Carpendale
IEEE Trans. Vis. Comput. Graph.8
2023 Understanding Barriers to Network Exploration with Visualization: A Report from the Trenches
abstract
This article reports on an in-depth study that investigates barriers to network exploration with visualizations. Network visualization tools are becoming increasingly popular, but little is known about how analysts plan and engage in the visual exploration of network data-which exploration strategies they employ, and how they prepare their data, define questions, and decide on visual mappings. Our study involved a series of workshops, interaction logging, and observations from a 6-week network exploration course. Our findings shed light on the stages that define analysts' approaches to network visualization and barriers experienced by some analysts during their network visualization processes. These barriers mainly appear before using a specific tool and include defining exploration goals, identifying relevant network structures and abstractions, or creating appropriate visual mappings for their network data. Our findings inform future work in visualization education and analyst-centered network visualization tool design.
Mashael AlKadi, Vanessa Serrano, James Scott-Brown, Catherine Plaisant, Jean-Daniel Fekete, Uta Hinrichs, Benjamin Bach
IEEE Trans. Vis. Comput. Graph.1