What if your favorite board games didn’t just entertain you but also taught you how to interpret scatter plots, recognize chart types, and sharpen your data storytelling?
This intuition led us to rethink popular board games such as Guess Who?, Pictionary, and Taboo as educational board games for Data Visualization. By embedding educational objectives into familiar game mechanics, we aimed to make visualization literacy more accessible and engaging—especially for newcomers or less motivated learners. Read our full work here.
The core idea: Adaptation, engagement, participation
Having more practical and immersive interactions with didactic material can make learning experiences more successful and playing board games can effectively enhance academic knowledge and motivation. However, this presumes that learners are already motivated to engage with educational games and open to learning. Moreover, learners may initially encounter challenges in understanding the rules of a new game and adapting to its mechanics and design.
Adapting popular board games by changing their content while keeping their mechanics unvaried can reduce barriers for the general population and popularize visualization concepts among them. Thus, adapting classic board games for this purpose is crucial, as they are widely recognized, making the game more accessible and appealing.
Engagement is essential for learning. Games are engaging by definition thanks to their interactive nature and narrative centered on the achievement of a final goal, which is generally winning. Keeping learners focused can be easier through edutainment which disguises collaborative learning as playing a game with friends.
Participation in learning activities also improves learning achievements. Think about Monopoly, initially created for didactic purposes, and how you learn several financial skills, from budgeting to risk management, by simply playing it. Through participation in activities involving educational material, we learn the concepts we are exposed to and required to process. Therefore, our core idea was to adapt popular board games to engage learners in participating in educational activities.
The games: Guess Vis?, VisMemory, PictionaryVis, TabooVis, and The VisChameleon
We selected five games that we found ideal as a start for their popularity and simplicity in terms of rules. Simplicity was particularly important since it can balance the unfamiliarity of the topic for some players. The games are Guess Who?, Memory, Pictionary, Taboo, and The Chameleon.
The adapted games are based on basic visualization concepts and designed to let learners achieve different visualization learning objectives based on Bloom’s taxonomy of learning goals. Depending on the mechanics and characteristics of the original game, the learning goals can be to define different data visualizations (Remember), to recognize data types, chart types, and visualization tasks (Understand), use data visualization taxonomies (Apply), relate data visualizations to visualization requirements (Analyze), or select the right data visualization for a specific context (Evaluate)—just to make some examples.
Guess Vis? requires players to guess their opponent’s chart by asking yes/no questions about chart features, like type or data encodings.
Learning objectives: Players build vocabulary by creating yes/no questions (Remember), differentiate visualization types through elimination (Understand), and use hints to categorize visualizations (Apply). Discussions during eliminations deepen understanding of visualization functions (Analyze), and players ultimately determine the most suitable visualization for specific scenarios (Evaluate).
Specifications: 2+ players, 10–20 min, high accessibility.
Game material: The materials required for Guess Vis? include a card deck and a board to hold the cards. There are two card decks, one for each player, consisting of 15 to 25 cards to maintain balanced gameplay. The cards incorporate clues that require prior knowledge of data visualization to interpret effectively.
VisMemory requires players to match cards either by visualization name (basic variant) or chart type (advanced variant).
Learning objectives: In the basic variant, players reinforce associations between visualizations and their names (Remember) and improve recognition and matching (Understand). In the advanced variant, players use visualization vocabulary to match cards (Remember), improve recognition and classification (Understand), and group similar charts to analyze their characteristics, deepening their understanding of visualization types (Analyze).
Specifications: 3+ players, 30–60 min, high accessibility.
Game material: VisMemory relies on a customizable card deck, with the number of cards ranging from 20 to 40 (10 to 20 pairs) depending on the desired difficulty level.
The game primarily uses cards to match visualizations based on their visual representation or their name. In this way, how the cards are used can be changed to fit specific learning objectives and accessibility needs.
PictionaryVis? is about drawing visualizations for the other players to guess and encourages sketching, decoding, and discussion.
Learning objectives: Players reinforce their understanding of chart structures by drawing and recognizing data visualizations (Remember, Understand). Through discussions about drawing choices and different interpretations, they refine their understanding of chart differences and analyze the correct chart (Analyze). Finally, players evaluate and decide on the best representation for a given case (Evaluate).
Specifications: 4+ players, 20–40 min, medium accessibility.
Game material: For PictionaryVis, the primary materials include cards that specify which data visualization a player must draw. A pencil and paper are also needed for drawing (see Figure 2). The game encourages creativity and recognition through drawing, making it a versatile tool for teaching data visualization.
TabooVis players must describe a chart without using key ‘taboo’ terms like axis or correlation. The game forces creative communication and requires prior knowledge of data visualization to play it.
Learning objectives: Players enhance their ability to recognize charts through active participation and listening to explanations (Understand). Conversations about visualizations help them apply categories (Apply), and this engagement improves their skills in analyzing charts and related concepts (Analyze).
Specifications: 4+ players, 20–40 min, medium accessibility
Game material: TabooVis requires a card deck containing visual representations along with related keywords.
In The VisChameleon all players except the Chameleon(s) are secretly given a chart. Players take turns giving one-word clues about the word, trying not to reveal it outright. The Chameleon(s), who don’t know the chart, try to blend in and guess it. Each round, one player is voted out. If the Chameleon(s) avoid detection until the end, they win; otherwise, the other players win.
Learning objectives: Players engage in discussions that deepen their understanding of visualization concepts (Understand). By justifying how a visualization fits a category, they apply their knowledge of taxonomies (Apply). They also compare charts to spot inconsistencies and identify the “VisChameleons,” developing differentiation skills (Analyze). Throughout, players challenge each other’s explanations, promoting critical thinking and deeper understanding (Evaluate).
Specifications: 3–8 players, 15–30 min, low accessibility.
Game material: The VisChameleon uses a set of cards (or a simple paper-based game board) to select secret visualizations and identify the VisChameleon (see Figure 2). There is no strict upper limit on the number of cards, allowing the deck to be expanded based on learning objectives. The game is designed to be flexible and adaptable to different teaching needs.
Final remarks: preview of one of the games and where to find it.
Watch the presentation video for our prototype of the first game we developed, Guess Vis?. Soon, the other games will be developed and all of them will be available. Although an initial set of cards based on 20 charts will be developed, all games do not have a strict card limit, which allows for expandable and customisable decks based on the target audience and learning objectives.
With the hope that it will let students, practitioners, and data viz enthusiasts learn and have fun, do not hesitate to contact us for more information, questions, or comments.
Lorenzo Amabili
Lorenzo is a data scientist working in Buzzi’s RTD team and a PhD student in Computer Science at TU Wien, Austria. His research interests are Visualization Education, Visual Storytelling, HCI, and Information Visualization.
Eduard Gröller
Eduard Gröller is Professor at TU Wien, Austria, and a key researcher at VRVis, specializing in visualization and computer graphics. He also holds an adjunct position at the University of Bergen, Norway. His research interests include computer graphics, visualization, and visual computing.
Renata G. Raidou
Renata G. Raidou is Associate Professor in Biomedical Visualization and Visual Analytics at TU Wien, Austria. She is a holder of the Dirk Bartz Prize for Visual Computing in Medicine (1st Place) at Eurographics 2017 and the Best PhD Award 2018 of the EuroVis Awards Programme. In 2022, she was awarded the EuroVis Young Researcher Award. She is currently on the Steering Committee of EG VCBM, GI-VCBM, and GI-Vis, and a Eurographics Junior Fellow. She has also been elected in the Executive Committee of Eurographics and acts as the Austrian representative in EASAC (European Academies’ Science Advisory Council), a collaborative body for independent, evidence-based scientific advice to policymakers—specifically in the area “AI in Healthcare”. Her research focus is on the interface between Visual Analytics, Image Processing, and Machine Learning.
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Renata G. Raidou#molongui-disabled-link
