Category Archives: gamification

A few thoughts on Google News badges

Yesterday Google announced they were adding “badges” to the Google News reading experience.  The basic gist is that Google will give you private badges (which can also be shared – this is key) based on which topics you read most.

There’s been some comment / criticism from the gamification folks, like BunchBall, that this is an inane thing to do.  And from a user’s point of view I tend to agree. Simply awarding a badge, which may or may not be visible to my network, for reading isn’t much of a motivator.  The system doesn’t offer a sense of accomplishment to the user, or even offer a sense of what they need to do to advance or “achieve” the next badge. The only real positive aspect of this for the user is better feedback and visibility of their reading habits.

But, if we think about this from Google’s point of view – collecting validated information about reading habits is GOLD. By validated, what I mean is that if a user “shares” a reading badge, say it’s for “Mobile Industry”, they’re going on record as someone with some sense of expertise on the topic. “I’m so-and-so and I’m an expert on the mobile industry” is what the shared badge means. As Google accounts get linked to content creation via the author tag all of this can feed into the quality / credibility calculator that drives Google search. Moreover, a validated badge helps Google target ads more effectively. Better targeting = more clickthroughs = more money.

An interesting issue here is the granularity of badges in Google News. Can I get a badge in “Visual Analytics Research” or is that too specific? It would be helpful to be able to see the badge landscape of what’s available.

There’s no doubt that Google News badges are immature as a product offering. The user experience needs to get better and offer more incentives and motivation. Klout perks could be an interesting direction to take this. Ultimately though I think this is less of a UX play and more of an opportunity for Google to collect more data.

Balance and Challenge in Playable Data

Note: A version of this will appear at the CHI2011 Gamification Workshop.


Work published this year at CHI has introduced the notion of game-y information graphics which take raw datasets from sources such as and create playable visualizations by adding elements of goals, rules, rewards, and mechanics of play. One example is Salubrious Nation, which uses geographically tagged public health data such as smoking and obesity rates, to create a guessing game. The goal of the game is to accurately guess the magnitude of the given health parameter for a randomly selected target county. A player’s guess can be informed by looking at the map (See screenshot below) for visual clues as a slider is changed, or by using hover-over information on correlated variables (e.g. poverty rate or elderly population rate).

In addition to allowing players to use the map-based graphic to arrive at insights about the data and to redistributing players attention to different aspects of the data, such an approach also offers the promise of reducing the amount of effort needed to repurpose that data into new playable experiences. Interested readers can see the paper for all of the details.

In the remainder of this post, however, I would like to expound on and explore the design difficulty associated with creating a challenging and balanced game experience when drawing on raw datasets as input for the construction of a game. Ordinarily when designing games, substantial effort is directed to level design. In fact, many games employ dedicated level designers who work with the game designer in order to provide the right amount of challenge, reward, and balance to the game experience (See Game Design Workshop for more details).

In contrast to such heavily authored experiences, gamified data experiences (whether they be based on infographics as in Salubrious Nation, or not), may draw on data that is incomplete, inconsistent, or dynamic. For instance, if a dataset is missing values, such missing values must be taken into account so that this does not completely break the game, or at least does not substantially reduce the engagement of the experience. Salubrious Nation relies on correlations between health variables to demographic variable such as poverty rate, to help users predict the public health variable (e.g. smoking rate). If the data were updated in such a way that relationships (i.e. such as a correlation) was diminished or removed, this would affect the playability of the game.

Dealing with data that is updated, refreshed, or otherwise dynamic represents a design challenge. Another example, the California Stimulus Map Game was a game-y infographic created for the Sacramento Bee newspaper website. In this trivia game players had to answer a series of trivia questions about stimulus funds by interacting with a visual map of the state of California. Two weeks after the initial publication the data for the map in the game had already been updated by the government. Not only did this affect the visual representation of the map, but it also impacted the answers to some of the trivia questions, thus forcing the designers to update the game in order to accommodate the new data. One approach to dealing with this issue would be to devise better automatic authoring routines so that trivia answers could be extracted directly from the data without human intervention (e.g. “What is the county with the largest (or smallest) amount of stimulus money”). More research needs to be done to determine the best way for dealing with changes to data which can impact a play experience. Methods developed should be robust to incomplete, inconsistent, or dynamic data and should provide for a playable experience regardless of reasonable changes to such data.

A more general issue with raw data is that the challenge or difficulty of the experience produced in the game is hard to control. With one set of data as an input a game may be too easy but with another it could become too hard. For instance, in Salubrious Nation there were 8 levels, each using a different public health parameter. For each of the levels we measured the average accuracy of the guesses that were produced by the 41 players in our experiment. This is shown in the figure below (with error bars showing the standard deviation of accuracy). As can be seen in the graph, some levels were more difficult than others, even considering some potential learning and improvement by players in the latter levels. This is in contrast to the typical game design pattern of increasing difficulty of levels. Indeed, based on the collected data it may be advisable to re-order the levels in Salubrious Nation so that easier levels are first and more difficult ones later.

In the absence of carefully authored levels of a game, we can still collect log data from players in order to infer difficulty and challenge. While this is relatively straightforward for a puzzle where there is a correct answer and a relatively simple metric can be used to infer difficult, there remain open questions for research. How can log data be used to infer other measures of difficulty (frustration even)? How can playable data games be rapidly and perhaps automatically re-adjusted to assess difficulty so that in a short period when a game is first being played it is able to evolve and adjust itself to provide an appropriately balanced and challenging experience?

These questions apply generally to the gamification of any data-based resource. When gamifying a dynamic, perhaps arbitrarily defined data source, how can we arrive at estimates for the challenge, balance, and playability of those experiences? Properly instrumented such games could perhaps automatically adapt their levels and difficulty to compensate for differences in the input data. I believe that answering these questions will be essential to being able to more rapidly create compelling gamified data experiences in the future.

Playing With Data

Recent years have brought a steadily growing international interest in openly publishing government and other civic datasets online. Government efforts such as the United States’, the United Kingdom’s, and other European efforts such as in Norway immediately come to mind. There are commercial interests in this space as well, such as the newly launched Data Market, which goes a step beyond data curation to provide visualization tools as well.

So now that we have droves of data, what do we do with it?

One novel approach toward facilitating engagement with such datasets is to create playable experiences from that data. In particularly I have been exploring the design space of data-driven information graphics which include familiar aspects of games, such as goals, rules, rewards, competition, and advancement. The promise is that engagement and learning will be enabled by connecting game activities and goals together with interactions which facilitate visual analysis of the data. For instance, if in order to achieve a high score in the game, the player has to visually compare the values for a data attribute, then in the course of playing they may have an additional opportunity to engage with the data in a meaningful analytic capacity.

At Rutgers University we recently created a game called Salubrious Nation, which explores this idea of playable data. Salubrious Nation takes U.S. public health data, such as smoking, obesity, or diabetes rates, and creates a playable map-based game. The goal of the game is to guess the public health data value (e.g. binge drinking rate) for a randomly selected target county. The player can inform their guess by using other correlated demographic data such as poverty rate, or by looking at how the graphic changes colors when the player manipulates the slider to enter their guess. The player earns points based on how close their guess was to the actual. Each level is a different public health issue and at the end the player can compare their score to others who have played and finished. A screenshot is shown below, and the reader is invited to try the game online at

We ran an online study of Salubrious Nation with dozens of people in order to better understand how such a game-y (i.e. game-like) presentation of information impacts exploration of the data, insights and learning, and fun. What we found is that, when compared to a standard information graphic presentation of the same data, the game-y version was able to redistribute player’s attention in interesting ways. For instance, people’s interaction with counties was more uniformly distributed across the country due to the random nature of selecting target counties in the game. Also, players used the slider more in the game-y presentation, likely because that was both necessary to enter a guess as well as helpful in informing the guess since regional patterns could be see by tweaking it.
Considering the selective attention issue, where people are more likely to pay attention to things that they already agree with, this result suggests an opportunity to get players to look at aspects of the data that they might not otherwise be inclined to look at. While the game-y presentation wasn’t any more fun or engaging than a standard infographic, Salubrious Nation does demonstrate that the goals embedded in a game can successfully motivate interactions and bias both the exploration and the nature of the insights for players.

While Salubrious Nation is an initial foray into the design space of playable data and game-y information graphics, there still remains a huge array of possibilities for exploring this design space. Future designs might incorporate different player interaction pattern (e.g. player vs. player), game resources (e.g. lives, currency, power-ups) and goals or mechanics (e.g. collecting, building). Moreover, there are many other possibilities for game-y presentations of data depending on different data types (e.g. network, tree, temporal, etc.) and different non-map-based visual representations (e.g. node-link, timeline, etc.). Some game mechanics may be more general and adaptable to different data types whereas others may be more specific. More research needs to be done to better understand the generalizeability of these methods.

Opportunities for innovation abound here. The gamification of data does not have to be limited to simply presentation, but could be applied to all facets of a news information pipeline such as information gathering or dissemination. Imagine gamified mobile apps that motivate users to interact with the world in a news gathering capacity. This could enhance activities such as citizen science, or add to news organizations’ ability to gather more and diverse information.

Indeed there is a spectrum of artifacts that can be imagined within the realm of playable data. Some will look more like information graphics, others more like casual games, and still others like something we haven’t even conceived of. I hope that others will join in exploring this design space to make the continuing flood of online civic data more engaging, insightful, and fun for users to interact with.

For more details on Salubrious Nation and the results of the study readers are invited to see a pre-print of the paper, which will be published at the 2011 Conference on Human Factors in Computing Systems in Vancouver, CA.