Constructive visualization enables users to create personalized data representations and facilitates early insight generation and sensemaking. Based on NODKANT, a toolkit for creating physical network diagrams using 3D printed parts, we define a competitive network physicalization game: BattleGraphs. In BattleGraphs, two players construct networks independently and compete in solving network analysis benchmark tasks. We propose a workshop scenario where we deploy our game, collect strategies for interaction and analysis from our players, and measure the effectiveness of the strategy with the success of the player to discuss in a reflection phase. Printable parts of the game, as well as instructions, are available through the Open Science Framework.

Games have long been recognized as effective tools for engagement, learning, and problem-solving. In visualization, games and activities have been explored as methods for enhancing the understanding of complex visualizations or concepts and promoting creativity. Construct-a-Vis and Diagram Safari are noteworthy examples, which encourage participants to interact with data and develop insights through structured activities and play. Physicalization—the representation of data through tangible objects—has been gaining attention and promotes a method to deepen comprehension through active construction and manipulation.

Physical interaction with data representations enhances understanding, engagement, and long-term retention. Prior work suggests that actively constructing representations leads to more profound insights compared to passive observation. Studies indicate that people will naturally organize network structures in meaningful ways, i.e., enclosing clusters in hulls. Data Strings highlights the benefits of participatory physical visualization, allowing individuals to actively shape collective data through direct interaction. Further related work investigates interactive physical representations of networks, including NODKANT, suggesting that this added interactivity enhances perception, memorability, and analytical reasoning. WonderNet similarly explores the physicality of network structures, transforming them into tangible objects that highlight their inherent connectivity and spatial relationships. This approach reinforces the importance of physicalization as a method for deepening comprehension and engagement. Willett and Huron introduce the concept of input visualizations, highlighting how visual structures can serve not only as representations but as interactive mechanisms for data input. Their framework expands our understanding of how visualization can facilitate engagement and structured exploration, offering new perspectives on physicalization and interactivity. HoloGraphs demonstrates how dynamic networks can be represented physically and how they can raise visualization literacy by offering engaging interfaces and tangible interactions.

Inspired by such examples, we investigate whether competitive, hands-on network construction can enhance graph comprehension, memorability, and problem-solving in an interactive and playful setting. Here, we introduce BattleGraphs, a two-player competitive board game that integrates physical network construction with analytical problem-solving. Drawing from the findings of prior research on user-generated layouts and interactive physicalization of networks, with BattleGraphs, we explore how the familiarity of self-constructed representations of networks influences task performance, particularly in competitive time-constrained scenarios. The intended contribution of the game is to provide an experimental platform for studying network construction through physical and interactive means, as well as to investigate the benefits of engagement through competition.

BattleGraphs is designed to engage players in various levels of cognitive processes as defined by Bloom’s Taxonomy. The proposed game aligns with the following levels (progressing from easy to complex): (1) Remember—players must recall basic graph concepts such as nodes, edges, and connectivity; (2) Understand— Players need to understand the visual encoding to construct their graph and understand graph analysis problems; (3) Apply—Players apply their knowledge of graphs to construct a physical representation using the NodKant kit; (4) Analyze—Players must break down the structure of graphs to solve tasks efficiently; (5) Evaluate—Players evaluate the effectiveness of a graph layout, which promotes reflection on learning strategies and problem-solving approaches. They judge the speed and accuracy of answers against their opponents; and (6) Create—Players develop strategies for organizing and constructing their networks. We intend to further validate this alignment using the workshop setting as a platform to elicit feedback and reflect on our visualization game in practice.

Sources and materials

BattleGraphs is based on the NODKANT toolkit by Pahr et al. NODKANT is designed to be a simple, dynamic, and effective toolkit, specifically aimed at the construction of physical network diagrams. For BattleGraphs, we additionally introduce two types of card decks, representing the graph’s edge list and the graph tasks to be solved, as an aspect of gamification.

Toolkit NODKANT consists of two 3D printable parts (Figure 2), for which the mesh files are available on osf.io. Firstly, edges con- sist of two spools with a yarn in between them (Figure 2a). The spools can be rotated to alter the length of the yarn freely after placement (Figure 2b). Secondly, nodes are represented by cylindrical disks with labels printed on top (Figure 2a). Placing small magnets in the rotational center of the parts allows for quick assembly, while also ensuring the parts can be freely rotated individually (Figure 2c). Using a magnetic whiteboard as a base provides a 2D canvas to embed physical graphs.

Cards The Edge Card deck serves the construction of the graph, each card containing an edge of the graph. Pahr et al. propose to use an edge list, sorted by associated node degree, to provide users with step-by-step instructions during construction. For BattleGraphs, we decide to gamify this aspect by providing each player with a random initial sorting (in the form of a shuffled deck of cards, each representing a particular edge of the graph) to create their own construction strategy. We propose an Assembly Phase of about 30 minutes, similar to Pahr et al., choosing the same dataset of animal interactions, mammalia-raccoon-proximity-50 to produce comparable results. The Task Card deck contains textual descriptions of the tasks used by Pahr et al. for their study. Each card presents a (low-level) graph task, derived from Lee et al.’s graph task taxonomy, on one side, and solutions to the question on the other.

Replay Value and Difficulty Selection In order to ensure BattleGraphs can be enjoyed multiple times by players, the game can be played in one of three difficulty settings: easy, medium, and hard. Each difficulty setting corresponds to a graph of increasing complexity, i.e., easy difficulty corresponds to a graph of lower complexity (i.e., few interesting structures, low density, low number of nodes and edges), whereas high difficulty corresponds to a graph of high complexity (many interesting structures, motifs, and a larger number of nodes and edges). Depending on the difficulty selected, i.e., graph data selected, a different set of Edge Card decks is selected, shuffled, and given to players. Conceptually, players can easily create their own decks of cards, based on their own selection of graph data, in order to replay BattleGraphs at their preferred and custom difficulty setting.

Battlegraphs: Game

BattleGraphs is a two-player, competitive, educational board game centered around the construction of one’s physical network layout (see Figure 1). Subsequently, players answer a set of graph analytical questions faster than their opponent and, in doing so correctly, gain a point. Intuitively, the more readable one’s constructed network layout, the faster and more accurately one should be able to solve graph analysis tasks. Broadly speaking, this roughly 90-minute game consists of four distinct phases, namely a 15-minute (instruction and) Setup Phase, a 30-minute Assembly Phase, a 30-minute Battle Phase, and a final 15-minute Discussion Phase. To play, the following materials are required: i) a countdown timer to keep track of time, ii) two magnetic whiteboards, iii) two NODKANT kits, iv) a physical divider to visually separate each player’s whiteboard, v) two identical, but shuffled decks of Edge Cards, each representing the edges of the graph to be assembled, and vi) another shuffled deck of Task Cards, in which each card is a graph task to be solved (Figure 3).

Setup Phase At the start of a game of BattleGraphs, each player receives their magnetic whiteboard, a well-shuffled, face-down deck of Edge Cards, and a NODKANT kit. Each player places the whiteboard and their deck of face-down Edge Cards in front of them. A physical divider is then placed between each player’s whiteboard such that the view of the other’s board is obstructed. Place the well-shuffled deck of Task Cards out of view for now. Finally, set the timer to 30 minutes and place it in an area visible to both players. Once the timer starts, the Assembly Phase begins.

Figure 2: The NODKANT toolkit. (a) Each node is represented as a 3D-printed “puck” with a magnet fitted underneath. Edges are represented as two such magnetic “pucks” connected by an adjustable length of yarn. (b) Edge length, i.e. the length of yarn between an edge’s endpoints, is adjusted by turning the endpoints’ spools until the desired length is achieved, (c) To construct a network, edges and nodes are stacked vertically on the magnetic whiteboard surface. Reprinted, with permission by Pahr et al.

Assembly Phase During the Assembly Phase, each player has 30 minutes to construct their physical layout of the graph represented by the deck of Edge Cards. Each card, in the currently face-down deck of Edge Cards, represents one edge of the graph to be assembled and contains the start and end nodes of the edge. In essence, the shuffled Edge Cards are a random arrangement of the graph’s edge list. Each player may now flip the deck of Edge Cards face-up in order to view, re-arrange, and organize the entirety of the deck as they see fit. Using the provided NODKANT kit, each player, following their organization of their Edge Cards, now constructs their graph on the provided whiteboard. The NODKANT kit consists of physical representations of both nodes and edges (Figure 2). Nodes are represented by black, magnetic, labeled disks. Edges are represented by two white, magnetic, unlabeled, connected by a length of adjustable string. To represent a basic graph of two connected vertices, one edge is magnetically placed on the whiteboard, and each node is magnetically placed atop each end of said edge. Once the timer notifies both players that 30 minutes have elapsed, the Assembly Phase has concluded, and the Battle Phase begins.

Figure 3: Example of task cards used during the Battle Phase.

Battle Phase During the Battle Phase, players compete for 30 minutes to answer a set of graph analysis questions as quickly as possible using their own constructed network layout. Each question (task) is represented by one of the Task Cards. These cards are two-sided, one of which features the graph analysis question, the other the answer (Figure 3). To start the Battle Phase, set the timer to 30 minutes. The well-shuffled deck of Task Cards is placed question-side-up in an area visible to both players. Once the timer is started, the Battle Phase has commenced. During each round of this phase, players read the question of the currently revealed Task Card in silence and subsequently attempt to answer this question as quickly as possible using their own constructed graph layout. The first player to call out an answer checks the correctness of their provided answer using the back side of the current Task Card. If correct, they keep said card, thereby gaining a point. If incorrect, the opponent has a chance to answer to answer said question correctly to gain a point. If neither player is able to answer the question correctly, neither gets to keep the card. Players continue to answer questions in such a manner until either the 30 minutes elapse, or all Task Cards have been answered. The player with the most Task Cards, i.e. points, wins the game.

Reflection

Discussion Phase During the final 15-minute Discussion Phase, players remove the physical divider in order to reveal their network layouts to each other and discuss strategies for both graph layout and graph analysis. Questions worth asking include, but are not limited to: What was learned about the graph? What strategies did players utilize when organizing their Edge Cards and subsequently building their networks? What strategies did they employ to answer task analysis questions? Why did one player do better than the other?

The game’s design aligns with Bloom’s taxonomy on the six cognitive levels–Remember, Understand, Apply, Analyze, Evaluate, and Create. However, we aim to explore and validate this aspect further to investigate how effectively the game supports each level in practice and whether it facilitates meaningful cognitive engagement across these domains. We aim to use the results of the discussion phase from the workshop to perform a brief qualitative analysis of construction strategies and interaction techniques with the NODKANT toolkit. Prior work emphasizes the role of interactive physicalization in supporting deeper comprehension of visualization concepts. By actively constructing representations, players are expected to develop a stronger understanding of network structures, reinforcing prior findings on hands-on engagement in visualization tasks.

Evaluation Plan We aim to assess how players construct and interpret network structures, as well as the level of engagement facilitated by BattleGraphs, using the VisEngage questionnaire. Engagement is a complex construct encompassing multiple dimensions, including captivation, discovery, and challenge. The questionnaire provides a method for assessing interaction-driven immersion in visualization, aligning well with BattleGraphs’ goal of increasing engagement in network visualization tasks. We will assess these aspects through post-game discussions and ethnographic observations throughout the workshop to determine how BattleGraphs encourages deep engagement and involvement. Participants’ reflections will be transcribed and analyzed qualitatively. The qualitative coding will cover observations of player interactions, including strategic adaptations. This process will result in higher-level sentiments that form the basis for our analysis of how players approach network construction in BattleGraphs. Our focus will be on (i) Graph comprehension (i.e., players’ understanding of network structures, like cliques, clusters, and bridge/hub nodes); (ii) Engagement factors (utilizing the VisEngage questionnaire); and (iii) Impact of physicalization and interaction (i.e., comparing to the workshop results to the interactions identified by Pahr et al.—–see Figure 4a-d).

Figure 4: Different interactions with NODKANT: (a) Wiggling to reveal adjacency; (b) & (c) Pulling to reveal common connections; (d) Pushing nodes together to show their degree. Reprinted, with permission by Pahr et al.

Preliminary Expectations We anticipate that, by the end of a game of BattleGraphs, players will have a better understanding of graph (sub)structures, such as cliques (a complete subgraph within a larger graph), clusters (a set of highly interconnected nodes in a graph), bridges (nodes that connect to otherwise disconnected subgraphs), or hubs (highly connected nodes). Through interactive physical construction, we expect participants to actively manipulate these structures. We expect this process to lead to an improved comprehension of network structures compared to passively observing them on virtual screens.

The game mechanics encourage problem-solving under constrained conditions, requiring players to analyze graph connectivity while applying strategic decisions in real-time. Depending on the set of (correctly answered) questions asked, players might also develop an understanding of more abstract descriptive graph metrics, such as a graph’s density, diameter, or average degree.

Furthermore, engagement indicators, such as captivation, discovery, and challenge measured via the VisEngage questionnaire are expected to correlate with effective learning outcomes. Observing how players approach network construction provides valuable insights into the cognitive benefits of actively interacting with tangible objects and being part of the construction and creation process. BattleGraphs will also explore emerging strategies in graph construction, identifying key approaches in layout design, optimal edge placement, and adaptive problem-solving that are still ongoing problems within the broader field of network visualization. Preliminary findings will contribute to research on interactive visualization literacy and network physicalization, establishing a method for engagement-driven learning in network visualization.

Acknowledgements

This work was funded by the Austrian Science Fund (FWF) projects ArtVis [10.55776/P35767] SANE [10.55776/I6635], [ESP 513-N], Vis4Schools [10.55776/I5622] (in cooperation with the Czech Science Foundation [No. 22-06357L]). The financial support by the Austrian Federal Ministry of Labour and Economy, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.

The post BattleGraphs: Forge, Fortify, and Fight in the Network Arena appeared first on Nightingale.

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.

The post Leveraging Popular Board Games to Teach Data Visualization Through Play appeared first on Nightingale.

In today’s society, the ability to read and interpret data visualizations has become a critical skill in both professional and academic contexts. Therefore, fostering visualization literacy from an early stage is essential in teaching students to understand and interpret different types of data visualizations. Research highlights gamification as an effective method for enhancing visualization literacy by promoting active learning and motivating learners of all ages. In this context, a card game has been developed to challenge players to identify the most appropriate visualization type for different given datasets. This educational card game aims to deepen understanding and practical application of data visualization concepts while maintaining an engaging and interactive experience for learners.

Research has shown that data visualization is an essential aspect of a critical and informed society, and therefore, it needs to be part of our education. To better engage students and children in learning about data visualizations, it is essential to employ creative teaching methods. One way can be games.

Games are structured forms of play that engage participants through defined rules, objectives, competition, or collaboration. Games provide an environment for individuals to explore complex concepts, develop skills, and foster creativity. Across various disciplines—including mathematics, science, history, and language—games have been effectively integrated into educational settings, proving to be powerful tools for enhancing learning experiences.

There have also already been ventures in the area of card games to teach data visualization, such as VizItCards, VisFutures, and the The Graphic Continuum: Match It Game. Those games feature a small selection of visualization types and offer a more generic view of data visualization creation. Visualization games that guide players through concrete usage scenarios are rare. 

Therefore, we introduced the card game ”Vizionaire”. It confronts players with specific scenarios in which they have to find the most fitting data visualization type. The different scenarios are represented as cards, as shown in Figure 1, front side. 

The players have to decide in teams which data visualization would be the most fitting one for that specific scenario. They do so by discussing the cards in their hand and brainstorming about which one is the best to choose. Based on a scoring system for each scenario, the visualization card which suits the scenario the best wins the round.

The game combines both cooperative and competitive elements. To keep the scope of the first prototype manageable, the game focuses specifically on data visualization types that fall under the ”Patterns” category of The Data Vis Catalogue schema.

This category includes visualization types that highlight structure or meaning within datasets by revealing underlying forms or trends, such as: arc diagram, area graph, bar chart, box & whisker plot, bubble chart, candlestick chart, choropleth map, connection map, density plot, dot map, dot matrix chart, heatmap, histogram, kagi chart, line chart, multi-set bar chart, open-high-low-close chart, parallel coordinates plot, point/figure chart, population chart, radar chart, scatterplot, spiral plot, stacked area graph, stream graph, timeline, and violin plot.

The selection of these visualization types ensures that players are exposed to visualization techniques that are both versatile and foundational, supporting a wide range of analytical tasks, e.g., identifying trends, comparing categories, visualizing distributions, and uncovering relationships in data. The game aims to reinforce learning through repetition and targeted practice, helping players to remember, understand, and apply these visualization methods in a fun and engaging way.

The card game is designed for undergraduate computer science students taking their first course in data visualization. It serves as a supplementary learning method to reinforce key concepts from lectures, making the material more engaging and easier to grasp for students with no prior experience.

Goals and rules of play

The primary objective of this game is to enhance players’ understanding of data visualizations. By engaging with various real-world scenarios, players will develop the experience and expertise necessary to select the most appropriate visualization for specific tasks and given datasets. 

Each round centers around a ”Scenario card,” which presents a specific data set, its intended use, and the target audience. Within a two-minute timeframe, team members collaboratively discuss which card from their hand best corresponds to the scenario. 

This phase of the game encourages critical thinking and fosters teamwork.

It is crucial that during the phase, the players communicate effectively within their own team to avoid distracting or assisting the opposing team. 

Once the two-minute discussion phase has ended, the ”Visualization card” with the highest scores wins the round. The objective of this game is to accumulate as many round scores as possible. As the game is played more frequently, it aims to enhance players’ understanding of which visualization types are most suitable for specific scenarios, which acquire skills for real-world contexts.

Short game manual

The game is played with two different card decks—Scenario cards & Visualization cards—that will be described in the following two paragraphs (see Figure 2 and Figure 3).

Scenario Cards feature fictional scenarios that require data visualization to represent them in a suitable way, as illustrated in Figure 2 on the front side of the card. 

The front of the card is structured into two main parts, one being the general information featuring a name and an image, whereas the second part provides all the needed information for the gameplay: 

• Who needs this visualization? 
• Who is the target audience?
• Which data have to be visualized?
• What are the requirements for the visualization?

In the game, players must then select the most appropriate data visualization type following these factors presented on the “Scenario Card.”

The backside of the cards displays the results, including the scores for the most suitable visualization types, along with an explanation for the two best options. For additional suitable visualizations, only the score points are provided without explanations. If fewer than five visualizations are possible,  the number of boxes containing score points is reduced accordingly.

In Figure 1, the card design of one specific scenario is illustrated. In this example, the ”Scenario Card” involves a youth soccer team that wants to visualize their game outcomes—wins, losses, and draws—over the past few years, broken down by month. Since the intended audience is teenagers aged fourteen to nineteen, the chosen visualization should be easily readable by students with an average educational background (requirement). In this case, the best-suited visualizations would be a multi-set bar chart or a line graph, as both use intuitive visual mappings and are commonly encountered in the wild.

Visualization Cards picture the different data visualizations (illustrated in Figure 3) that reveal forms or patterns in the data to give it meaning based on the Data Vis Catalogue.

To facilitate the categorization of data visualizations, they are grouped into four categories: Comparison/Relations, Distribution, Temporal, and Geographic. Each category is assigned a specific color to enhance differentiation and improve visual 

The Comparison/Relations group (blue) features the visualization types:  Bar Chart, Multi-set Bar Chart, Scatterplot, Bubble Chart, Radar Chart, Parallel Coordinates Plot, and Arc Diagram. 

In the Distribution group (red) cover the types: Box and Whisker Plot, Violin Plot, Density Plot, Histogram, Dot Matrix Chart, and Population Pyramid can be found. 

The group Temporal (green) includes: Line Graph, Area Graph, Stacked Area Graph, Stream Graph, Candlestick Charts, Open-high-low-close Chart, Point and Figure Chart, Kagi Chart, Timeline and Spiral Plot.

The group Geographic (yellow) consists of a Connection Map, Choropleth Map, Dot Map, and Heat Map.

The layout of the Visualization cards, as shown in Figure 3, includes the title of the visualization next to a colored box representing the group, with the group name displayed inside. The card also features an image illustrating the specific visualization, accompanied by a descriptive text. This text explains the visualization’s function, along with additional relevant details.

Game setup

1. Both the Scenario and the Visualization cards get shuffled. The Scenario deck gets placed in the deck box with the Description-Side face-up. The Visualization Deck is placed face-down next to the deck box.
2. If there are four or more players, the players have to separate into two teams. Otherwise, the players play together as one team against the game.
3. Each player is dealt five ”Visualization cards.”
4. As a last step, the first card dealer is selected. The dealer role rotates each round of the game.

Gameplay

1. To start a round, the first card from the Scenario deck gets drawn by the card dealer and placed face-up on the middle of the table. It is crucial to ensure that no player sees the backside of the card during this process.
2. Every player reads the details of the ”Scenario card” silently. Once everyone is ready for the next phase, the card dealer starts the timer.
3. While the timer is running, the members of each team discuss which of their ”Visualization cards” is most suitable for the current scenario.
   They can choose any of the cards from their hands. After the team has agreed on one ”Visualization card” that they want to play, they have to put that card face down on the table close to them. It is very important to place the card before the time has run out, or the card won’t be counted for the point score.
4. As soon as two minutes have passed and every team has chosen their cards, all the cards lying face-down on the table get turned over, including the ”Scenario card.” On the backside of the ”Scenario card” is a score list written with information about which ”Visualization card” gains which amount of points.
   Both teams calculate their achieved points. The team with the most points wins this round and gains the ”Scenario card” If a team collects their fifth ”Scenario card,” the game ends, and that team is declared the winner. The next round begins if neither team has acquired their fifth ”Scenario card.” Each player draws additional cards to reach their hand limit of five cards, and a new card dealer is selected from the opposite team, as in the previous round. The game then proceeds with step one.
5. If none of the teams has acquired their last card, the next round of the game starts. For that, every player draws up to their hand limit of five cards again and a new card dealer from the opposite team as before gets chosen. Afterwards the round proceeds with step one again.

If there are under three players, different game play rules apply as described as follows:

Extra Gameplay Rules for under three players

1. The steps one to three remain the same. However, unlike the version with two teams, the goal for each team is to reach a specific point total within five rounds. When calculating their points after each round, the team must check if they have reached the required winning score.
2. If that is not the case, the team will choose a new card dealer from their team and proceed with the same actions of redrawing and starting a new round.
3. The game ends either when the winning score is reached or after five rounds have been completed. In the former case, the team is declared the winner; in the latter, the game is considered the winner.

Material Overview

The card game consists of two card decks, one deckbox, and a timer. The timer can be a typical hourglass known from a broad range of board games or a digital timer on a mobile phone. The ”Scenario card” deck is being kept in the deckbox to prevent players from seeing the back side of the cards in advance. 

The ”Visualization cards” have the standard size of playing cards of 6.35 cm x 8.89 cm, whereas the ”Scenario cards” have a size 6.8 cm x 12 cm to include all the information needed for the scenarios.

We provide the cards in the supplementary material.

It is planned to create fifty different ”Scenario Cards” to generate a wide variety. 

For the ”Visualization cards” the correct balance of each type is still being tested. For testing purposes, each type is intended to have three to five cards.

Planned usage

This card game has not yet been played with students prior to the submission of this extended abstract paper. At the time of writing, the game is still being developed as part of a bachelors program thesis. As part of this thesis, an expert interview and a focus group with students from the University of Applied Sciences St. Pölten are planned.

There are two different ways to win this game, depending on the number of players participating. When there are one to three players, they play as a single team and must reach a certain number of points within five rounds. In this version of the game, the team plays against the game itself.

The other variation is with more than three players, where all the players form two teams and compete against each other. Instead of the version with just one team, in this case, the teams need to win five ”Scenario cards” to win the game. They can achieve this by scoring the most points in one round and gaining the Scenario card of that round. A more detailed explanation can be found in the Gameplay Section.

Reflection & conclusion

Through this game, players will gain hands-on experience in selecting the most appropriate data visualizations for various real-world scenarios, enhancing their critical thinking skills in the process. The game aims to bridge theoretical knowledge with practical application, helping players deepen their understanding of data visualization principles and their real-world relevance. 

At the time of this workshop paper, the game hasn’t yet been tested with users; therefore, it is still a prototype and will very likely experience changes based on the feedback gathered from the participants of this VisGames workshop and the planned evaluations.

Acknowledgements

This work was funded by the Austrian Science Fund (10.55776/I5622) and Czech Science Foundation (No. 22-06357L) as part of the Vis4Schools project. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

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Drawagram is a card game based on creativity and interaction that explores the fundamental ideas behind the development of simple time-based data visualizations. Players create visualizations individually on a range of topics, following the guidelines provided by specific cards. The game encourages creativity and innovative thinking and motivates players to find and try different methods to visualize data successfully. By combining learning with gamification, Drawagram offers students, professionals, and educators a fun and accessible way to improve their data literacy.

In today’s data-driven world, the ability to understand and interpret data visualizations has become an essential skill in a variety of fields. However, traditional teaching methods often only rely on lectures and software-based training, which might be overwhelming for beginners. To improve engagement and accessibility, researchers emphasize that the importance of including diverse learning materials should not be underestimated, especially in the context of data visualization and that, in addition, non-computer-based tools such as sketching also provide valuable opportunities for students to be creative.

Gamification has emerged as an effective approach to improve learning outcomes, particularly in data visualization education. Studies suggest that using a game-based approach to learn different data visualization methods is an accessible and straightforward way to teach the general public basics of visual representation and its appropriate use.

Some card games exist, such as “Charty Party”, “The Graphic Continuum: Match it Game” and “The Data Visualization Deck” that reach from purely entertaining party game to visualization cards that support professionals to improve their work and achieve a compelling presentation of their data. 

Building on the success of these existing card games, we introduce Drawagram, taking a step further by focusing specifically on time-oriented data visualizations, offering a more interactive approach. Drawagram is a hands-on educational card game that supports users in engagingly discovering time-oriented data visualization. By allowing players to experiment with different chart types and relevant topics and engage in discussions about their created visualizations, Drawagram fosters creativity and analytical thinking. The game encourages players to make quick, informed decisions about how to structure their visualizations and also to reflect on how effective their design decisions are.

Currently, Drawagram focuses on creating simple time-based data visualizations to help players develop a basic understanding of how to represent data over time. The game is designed for students, educators, and professionals to provide an engaging way to explore the basic principles of data visualization. Through game-based learning, Drawagram helps players improve their data literacy in a fun and engaging environment.

Game description

Drawagram consists of three different card types, as illustrated in Figure 2. “Chart Cards” (Figure 2, blue) determine the type of visualization that needs to be drawn, “Topic Cards” (Figure 2, green) specify the topic for the visualization, and “Special Cards” (Figure 2, yellow) introduce constraints or challenges that must be followed when creating visualizations. To ensure fairness, “Special Cards” have been curated to offer comparable levels of difficulty. While balancing is still ongoing, the current card set is considered to be mostly well-balanced in terms of challenge across players.

Within a time limit of five minutes, players use pencils and colored pens to draw visualizations on a sheet of paper based on the cards assigned to them. At the end, all visualizations are presented and discussed. The goal is to encourage visual thinking, problem-solving, and critical evaluation of data representations while providing an entertaining and educational experience.

Drawagram is designed for three to four players and is best played in a small group setting that encourages creativity, peer learning, and constructive feedback.

Goals and rules of play

The goal of the game is to create the most effective, creative, and accurate sketch of a data visualization within five minutes. While sketching, players must carefully consider what data they want to display that fits the given theme and chart type while following the constraints imposed by the “Special Cards” (see Figure 2, yellow card). After each round, all visualizations are presented and discussed. The group will evaluate each visualization based on three criteria: (1) creativity, (2) accuracy, and (3) legibility. The player with the best overall score in these three categories earns a point. The game continues until a player has earned three points or an agreed number of rounds has been played.

Game manual

Each round of the game consists of four key phases—setup, sketching, presentation & discussion, and voting—as illustrated in Figure 1.

Setup

At the beginning of each round, the group draws one “Chart Card” and one “Topic Card” collectively. The “Chart Card” specifies the type of visualization that must be used, and the “Topic Card” defines the subject for the visualization. Based on these two cards, all players must create their visualizations.

In addition to these shared cards, a “Special Card” is drawn by each player individually. This card introduces constraints or challenges that must be included in the visualization, such as “add a legend”, “highlight anomalies”, or “use at least three different colors”. These constraints encourage players to experiment with different techniques and ensure that there will be a wide variety of visualizations to discuss.

Players unfamiliar with the drawn chart type can find a short description in the added booklet, which contains an illustration and a brief description of each chart type included in the game. This ensures that all players can participate effectively and understand how to represent data in the given format.

Sketching Phase

Once the cards have been drawn and each player understands the drawn chart type, the timer is set for five minutes. During this time period, each player must create a sketch of a visualization on paper using pencils and colored pens. The sketch must align with the given chart type and topic and has to include the restriction from the individually drawn “Special Card”.

The game focuses on creativity, not artistic skills. Players should think about how to communicate data effectively using the given visualization method. The short time limit adds some challenge to the whole sketching process, as players are pushed to make quick but thoughtful decisions.

Presentation and Discussion

As soon as the timer runs out, each player takes turns and presents their visualization to the group by explaining the reason behind their design choice, how they incorporated the restriction of the “Special Card”, why they selected specific colors or labels, and what story or message the visualization should convey. After each presentation, the group discusses the outlined visualization based on factors such as creativity, clarity, accuracy, and legibility. This discussion should encourage the players to reflect on what was done well and what could be done better. The whole process is non-judgmental and intended to foster learning and the exchange of insights.

Voting Phase

After the group presents and discusses all sketches, the sketches will be evaluated. All players evaluate each visualization individually based on the factors of creativity, accuracy, and legibility. Players cannot vote on their own sketches.

Creativity assesses how unique and interesting the approach is. Accuracy checks that the chart type has been implemented correctly and displays the data accurately while taking into account the limitations of the “Special Card”. Legibility determines if the visualization is easy to understand, with clear titles, axis labels, and other important elements.

Each player assigns a score from 1 (Very Poor) to 5 (Excellent) in each category for every other player’s visualization. The scoring is based on the Likert scale outlined below:

Each player’s total score is calculated by summing the points they received across all criteria from the other players. The player with the highest total score at the end of the round wins and earns a point.

Voting is conducted openly, and players are encouraged—but not required—to briefly explain their given scores. This promotes reflection and constructive feedback. If two or more players have the same number of points at the end of a round, they will all earn a point. The game continues until a player reaches three points or until a predefined number of rounds has been played.

To simplify the evaluation process, players can use the Drawagram voting sheet. This is a structured sheet on which all ratings can be noted during the voting phase. This contributes to transparency, avoids errors, and simplifies the final score calculation.

Example round of the game

To illustrate how a game of Drawagram plays out, this example describes a round with three players: A, B, and C.

Setup

The following cards are drawn for this round:

• Chart Card: Spline Chart (same for all players)
• Topic Card: Water Usage (same for all players)
• Special Cards:
  • Player A: Label at least 3 data points
  • Player B: Use at least 2 different colors
  • Player C: Use time intervals in months

Each player must therefore create a spline chart about water usage over time, while also meeting the unique constraint on their “Special Card”.

Sketching Phase

A 5-minute timer is started. Players sketch a chart that displays data related to the previously selected topic using paper, pencils, and colored pens.

Possible ideas could be:

• Daily household water consumption over a week.
• Seasonal variations in a city’s water usage.
• Global water usage by different sectors over time.

Using pencils and colored pens, the players create spline graphs, ensuring they meet the requirements of the special card assigned to them and show suitable data for the chosen topic.  The data does not need to be factually accurate, but should fit the theme in a plausible or illustrative way.

Player A decides to sketch a spline chart based on global water usage by different sectors over time (see Figure 3).

Player B creates a spline chart showing per capita water usage across decades on different continents, using at least two different colors to differentiate between regions (see Figure 4).

Player C sketches a spline chart visualizing the water usage of an average household in Germany across the year, using monthly time intervals from January to December. (see Figure 5).

Presentation and discussion

Once the 5-minute time period is over, each player presents their visualization separately. This could proceed as follows:

Player A – Presenter: Label at least three data points

Player A: “My chart shows global water usage by sector from 1900 to 2000. I used green for agriculture, blue for industry, and purple for domestic use. As required by my Special Card, I labeled three data points from the year 2000. I also included a title, axis labels, and a legend.”

Player B (Providing Feedback): “The colors work well. Maybe labeling a mid-century value like 1950 would better show the growth trend.”

Player C (Providing Feedback): “The message is clear. Adding grid lines and using the right color order in the legend could improve the readability.”

Player B – Presenter: Use at least two different colors

Player B: “I created a spline chart showing per capita water usage in Europe and Asia from 1900 to 2025. I used blue for Europe and green for Asia to fulfill my Special Card. The lines show how Europe’s usage increased slowly while Asia’s increased sharply after 1950. I wanted the contrast in color to help highlight the regional differences more clearly.”

Player A (Feedback): “The color use works well, and the regional comparison is clear. Maybe labeling some data points for each region directly on the chart would help make it even more readable.”

Player C (Feedback): “Color coding by region works well, maybe add some grid lines for better readability of in between values.”

Player C – Presenter: Use time intervals in months

Player C: “I visualized household water usage over the course of one year for an average German household. I used a solid blue line to show the total water consumption per month. As you can see, usage increases during the summer months, likely due to gardening and outdoor activities, and is lower in winter.”

Player A (Feedback): “The chart is easy to follow. Adding exact values to highlight the peak or lowest months might make the trend even clearer.”

Player B (Feedback): “I like the seasonal pattern. Including a simple legend or annotation for the summer peak could help reinforce the interpretation.”

Voting Phase

The voting phase will begin once all the sketches have been presented and discussed by the group. Each player evaluates the other players’ visualizations based on three categories: creativity, accuracy, and legibility. The evaluation follows a Likert scale, as outlined in Table 1, where players can assign a score between 1 (lowest score) and 5 (highest score) for each category. To help keep track of the ratings, players can use the provided voting sheet, which includes a table for player and category and space to sum up the scores. Players do not rate themselves. The players rated the visualizations as follows:

Player A receives:

• From B: Creativity 4, Accuracy 4, Legibility 3
• From C: Creativity 4, Accuracy 4, Legibility 3
• Total: 22 points

Player B receives:

• From A: Creativity 3, Accuracy 3, Legibility 3
• From C: Creativity 4, Accuracy 4, Legibility 4
• Total: 21 points

Player C receives:

• From A: Creativity 3, Accuracy 3, Legibility 4
• From B: Creativity 2, Accuracy 4, Legibility 4
• Total: 20 points

Winner of the round: Player A

The game then continues until a player reaches three points or until a predefined number of rounds has been played.

Usage and future variations

Drawagram has been designed as a hands-on learning tool for use in classrooms, workshops, and training sessions. It supports learners in understanding and applying the fundamentals of time-based data visualizations through play and creativity. 

Currently, Drawagram focuses primarily on creating simple time-based data visualizations to help players develop a basic understanding of how to represent data over time. Future versions of the game may include more advanced visualization methods and chart types with additional functionalities, such as bar charts for categorical comparisons, scatter plots for correlations, and pie charts for ratio-based comparisons. In addition, complex visualizations such as stacked area charts for cumulative trends, radar charts for multi-variable comparisons, and Sankey diagrams for flow visualization could further enhance the gaming experience.

Preliminary Evaluation

To gain initial insights into its educational effectiveness and engagement, a field study was conducted with 22 Master’s students in Digital Business Communications at UAS St. Pölten. During a structured session, participants were introduced to the rules and goals of the game and played two full rounds of the game in small groups.

A pre-game questionnaire revealed that most participants had limited experience with time-based visualizations: 63.6% had created only simple charts, 31.8% had never created a time-based data visualization by themselves and only 4.5% stated that they had created multiple ones. Confidence levels regarding creating one’s own time-based data visualizations were rated at an average of 2.82 out of 5. After playing, this increased to 3.41, suggesting that the game contributed to improving participants’ self-perceived ability to create and evaluate visualizations.

To measure engagement, the VisEngage framework was used, with scores in 11 different dimensions rated on a 15-point scale. The following values represent the average ratings from all 22 participants. The highest-rated dimensions were Aesthetics (12.73), Autotelism (12.41), and Novelty (11.59), reflecting the game’s visual appeal and intrinsic motivation. Lower scores in Challenge (10.05), Discovery (10.64), and Captivation (10.95) indicated areas where future iterations could incorporate greater complexity and exploratory depth.

Open-ended feedback emphasized the game’s clarity, playful structure, and suitability for educational use. Players appreciated the analog format, the time constraint, and the “Special Cards” for stimulating creative thinking. It was noted, however, that the 5-minute sketching phase might be too short for beginners. Participants recommended extending the time limit to seven minutes for players with less experience. This highlights the game’s flexibility—groups are encouraged to adapt the time limit based on their experience level and learning goals. Additional suggestions for improvement included balancing the difficulty of “Special Cards’” and including voting instructions directly on the score sheets.

Overall, all participants expressed that they would recommend Drawagram in academic settings, especially as an introductory tool for learning data visualization concepts. The collaborative nature of the game, along with its focus on discussion and reflection, was particularly appreciated.

Reflection and conclusion

Drawagram is an engaging, educational card game that helps transform the learning process of data visualization into an interactive experience. By encouraging critical thinking, storytelling, and creativity, it acts as an effective learning resource for beginners and experienced data enthusiasts by making complex concepts more accessible and fun. The game provides a learning environment that encourages students to experiment with different visualization methods and have valuable discussions about data visualizations.

Nevertheless, some challenges might still be considered regarding the game. Players with limited drawing skills may hesitate to participate, thinking they are not competent enough. Therefore, it should be underlined that the game emphasizes creativity and correct representation over artistic ability. For players unfamiliar with some visualization methods, the “Card Description Booklet” explains each chart type included in the game. This ensures that everyone can participate in the game regardless of their prior level of knowledge. It is also vital to create an atmosphere of positive and constructive feedback in which all players can learn and improve together.

The game may be successful if participants enjoy it and feel that they have gained valuable insights into data visualization from playing it. After the game, participants should feel more comfortable creating their data visualizations, as the game provides an engaging and educational experience to improve their data visualization skills.

Drawagram draws inspiration from games like “Charty Party”, which also use cards and humor to explore data visualization, and “Viz Futures”, which encourages design thinking through prompts. However, unlike these games, Drawagram focuses on sketching, timed challenges, and peer feedback, creating a more hands-on and reflective learning experience. Gamification elements such as randomized card combinations, time pressure, tactical constraints, and a voting-based reward system are used to maintain engagement while reinforcing core visualization concepts.

Materials overview

The game box includes the three different types of cards, which are displayed in Figure 2. The blue “Chart Cards” determine which visualization has to be created, the green “Topic Cards” define the subject of the visualization, and the yellow “Special Cards” introduce constraints that need to be followed by the players when creating their visualizations. The base game consists of 29 playing cards in total. Of which seven are “Chart Cards”, eight “Topic Cards” and fourteen “Special Cards”. Additionally, a card-sized Instruction is included to provide a quick overview of the rules and gameplay structure. A “Chart Description Booklet” is provided, giving clear explanations and examples of all the different chart types found in the game. A structured “Voting Sheet” is also included to help players record and total scores during the evaluation phase.

While the game box contains all the essential components, some additional materials are required for the game. These include paper, pencils, erasers, and colored pens or markers for sketching the visualizations. A timer (e.g., a smartphone stopwatch) is also required to respect the time limit per round.

Acknowledgements

First and foremost, I would like to thank my bachelor thesis supervisor, Christina Stoiber, for her support and guidance. This game is being developed as part of my bachelor thesis and her valuable feedback greatly contributed to its development. I would also like to thank everyone who participated in the initial online survey as well as those who took part in the field study session. This work was funded by the Austrian Science Fund (10.55776/I5622) and Czech Science Foundation (No. 22-06357L) as part of the Vis4Schools project. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

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Rough translation of the above lines goes something like this:
“These are just balloons
filled with air.
If you prick them with a needle,
they will burst.

People don’t become bombs when they are angry!

Have you ever been intimidated by people who are experiencing intense anger or sadness?

While I am still researching the mood swings of adults, I can say for sure that it doesn’t take long to cheer up the kids. A simple tickle or a playful game can instantly brighten up their day.

The other day, Pariza was sitting at the kitchen table, her eyebrows furrowed in disappointment as she stared at the Scrabble board in front of her. 

Her best friend Zoeya had just left after a fun evening of playing board games, but Pariza was feeling down because she had lost the latest game of Scrabble.

“What’s the matter, Parizu?” I asked, noticing her sad expression.
“Didn’t you have a good time with Zoeya?”
“Yes, we had a great time, but I lost the game and it’s so frustrating,” she said, sighing heavily.

First I teased her, but looking at the seriousness of the situation, I tried to comfort her. “It’s okay to lose sometimes, Parizu. That’s just part of the game. You can always try again and see if you could do better next time.”

She nodded but was still unconvinced, so I offered her my data skills to analyze the game so she could improve the next time =)

She quickly ran to bring the score:

“Wow! This looks like a close game,” I said looking at the score 
“Yes! That’s why it is more disappointing.” 

I wanted to see the trend as opposed to how much they scored in each round, so we went and calculated the running total of their scores.

We first plotted the different points for Pariza’s score:

“Abbu,” Pariza said, “this starts to look like the growth chart the doctor shows me in every visit.”

Ha ha! This indeed reminds me of the growth chart =)

She then connected the dots:

Then we did a similar exercise for Zoeya’s score:

And just like for Pariza, we connected these dots:

“Oh my God! I was leading all the way, Abbu,” Pariza exclaimed with surprise, standing there with her mouth wide open.
“Yes! You were a clear winner but just got beaten with a very small margin in the end.”
“Zoeyu got the letter Q in the last round. She used it to make the word EQUIP with it and got 3 times points as she touched triple point tile.”
“So, Parizu, you now know that you need to target those in the next game.” I tried to pitch in my recommendation just as I do to the business stakeholders

Your turn to play

Next time you play Scrabble or your favorite board game, don’t throw away the scores. Just like shown in this article, you could use it to analyze the scores and create a wonderful data visualization. For more engaging ways to boost your kids’ data literacy, be sure to check out Drawing Data with Kids. It’s packed with creative ideas to help them explore the world of data!

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To make things easy, we’ve included versions in both US letter and A4 sizes. Be sure to print double-sided and at 100% (no resizing necessary)!

US Letter Version:

A4 Version:

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Looking at the wide range of contemporary board games presented there, I couldn’t help noticing how much board games have in common with data visualizations. In fact, at their core, all board games are data visualizations. Data and information are visualized as pieces of different colors and shapes (called meeples) placed on boards specifying coordinate systems. The rules of the game determine how the current situation of the data can be transformed into a more desirable state.

Board game players are willing to pay 30–50 € for standard games, and well over 100 € for elaborate expert games. Players spend hours and hours poring over these visual representations of data. That is a degree of user engagement that would be great to also achieve for data visualizations.

The good news is that many of the elements that make board games so engaging, fun, and accessible, are equally applicable to data visualizations. In the following, I will discuss a few such points. Board games use easily readable data encodings, use overarching plots and metaphors, have graphic design that fits the topic, and represent the data in physical form.

Board games tend to use easily readable encodings of data. Categorical data is usually encoded via color hue and shape. This goes, for example, for the different kinds of meeples controlled by each player. Numerical data is usually encoded via location among common axis, number of elements, and size of elements. Board games seldom include more difficult to discern encodings like shades of a color hue (light to dark) or orientation. Using them would quickly result in misreadings and confusion.

The table shows the encodings used in traditional and contemporary board games. The game of Go uses the simplest encoding with black and white stones (interpreted as categorical color hues here although factually color shades) placed on a grid (position). Modern games very seldom use further encodings beyond those already used in the game of Monopoly, first patented in 1904.

In data visualization, if the intention is to get information clearly across, easily readable encodings should likewise be used. The experimental encodings of data art play a very important role in extending the boundaries of the genre. But for many, such elaborate encodings pose a barrier to understanding. I personally have to admit to often skipping elaborate data art if it is too tiring to decode.

Board games make use of overarching plots and metaphors to integrate masses of complicated information. Typical settings of board games include medieval trade, fantasy adventure, armed conflict, and science fiction exploration. The setting provides the information encoded on the board with an easy to understand and memorize mental model. Entirely abstract board games are much rarer. Chess is the most popular abstract strategic board game in the western world. In 1924, Bauhaus designer Josef Hartwig created suitable abstract pieces for the game. The forms reflected the movements of the pieces. These did not catch on. Today, chess still uses the metaphor of two armies with knights and bishops maneuvering against each other to kill the other’s king. The human brain craves tangible plots and metaphors.

Over the last few years in data visualization design, there has been a strong trend to move from presenting rational arguments towards telling emotionally involving stories. This was especially initiated by Cole Nussbaumer Knaflic’s 2015 book “Storytelling with Data.” Narratives integrate lots of individual data visualizations into a whole to make a clear point. The narrative also makes individual facts much more memorable. A good story usually consists of a three-part structure with introduction, conflict, and resolution of conflict.

Board game publishers go long ways to make the graphic design fit the topic. Often the general mechanism and layout of board games are designed by one person (the board game designer/author), and the final illustrations done by a professional illustrator, who sometimes remains unnamed. Illustrations, color palettes, and fonts are chosen to reflect the content.

A wide range of illustration styles are used from rational flat infographics to realistic and very artistic styles. Photographs are rarely used as image material in board games. One reason could be that the use of somewhat abstract illustrations and icons makes it easier to remain in a mental state of imagining and abstract reasoning. In Germany, there is even an award solely for the visual design of board games, the Graf Ludo. If something is beautifully designed we are much more willing to invest time understanding and engaging with it.

Data visualizations can equally be made more enjoyable by using a graphic design language that fits the topic. Header fonts can be chosen to go along with the topic. Color schemes can set the general mood of a visualization. Integrated illustrations and icons can serve decorative purposes. Many good examples of this can be found in the Tableau Ironviz qualifier Dashboards (not in the quickly prepared finals).

Part of the fun of playing board games is to have tangible objects before you. The quality of the game material plays a big role in the enjoyment of a game. Usually, cardboard, wood, and plastic are used. It is nice to touch and literally walk around visual representations of information.

Most data visualizations are pure digital products for the screen. But for workshops, showrooms, and conferences it can be worthwhile to bring a visualization into the physical world. A low-level method is to print a (static) data visualization out as a large poster. Today, there are many possibilities of turning digital graphics into physical objects by 3D printing plastic, laser cutting plywood, or laser engraving on plastic, metal, or glass. If one is willing to put in some manual work, the possibilities are endless.

In this article, I have demonstrated how many elements that make board games so engaging can also be applied to data visualizations. The points discussed in this article are the use of easily readable data encodings, the use of overarching plots and metaphors, the use of a fitting graphic design, and the physicalization of the visualization. These are the more static aspects of board games. Further discussions would be warranted for the social aspects, the interactivity (think interface design), and the gratification and rewards integrated in board games (think gamification).

After a long day, I left the board game trade fair with lots of new ideas and inspirations. To me, it is clear that data visualization designers can learn quite a few tricks from board game designers and illustrators. But the inverse also holds true. I’ve seen quite a few board games that could have been improved with basic data visualization know-how. And it makes me think: “How would a board game look that was designed from the ground up by a data visualization designer?”

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