Playing to learn and learning to play are two fundamental concepts of the human experience. Learning games, often associated with the idea of “serious play”, can refer to either games intentionally designed for learning or entertainment games used to teach. This article will explore some of the many ways serious play with learning games can be used in the classroom.
You can also explore Learning by Making Games, Social MetaGaming, and Gamification in their own articles
Learning Games Summary and Theory
Learning by playing games is a powerful and versatile approach to games-based learning. Because learning by playing games was the first field of games-based learning research, it has the most robust literature and expansive set of approaches. Even in our expanding wiki, it would be difficult to cover all of the theories and practices surrounding this approach.
Hopefully, though, the brief summary below will give readers a quick overview of the primary theories behind playing games and the affordances we see in them.
The Magic Circle
One of the most foundational aspects of learning games is the idea of the magic circle. When you play a game, you put yourself in a different world. The rules that govern your actions in the rest of the day just don’t apply, and it’s in this magic circle that the social and cognitive norms change. Power relationships, how we talk to each other, and how we define successful interaction all change the moment the game starts!
The power of the magic circle cannot be understated. Inside the circle, a shy child can become a king, a litterbug can become an environmentalist, and the most complex concepts like physics can become simple interactions.
Affordances of Learning by Playing Games
There are many affordances of playing games to learn. Scholars are constantly studying new ways to employ games and discovering how those learning games affect the learning experience. Below are some of the most popular and often referenced affordances of the approach.
Engagement / Flow
Though engagement and flow aren’t the same thing, they are often discussed in the same perspective. One of the first and most often cited affordances of a game is the fact that learners tend to feel engaged with them and find them fun to play. This can be thought of as the “entertainment value” of learning games. Sadly, the total focus on this aspect of games and learning was led to the “edutainment” games movement.
Flow, simply put, is a scientific way for us to understand and measure engagement. Flow is deserving of its own complete article, but we would be remiss to not at least summarize it here.
We can think of flow state as absorption in a task or immersion in the play experience, and is often considered the “optimal experience” in a game. We can best understand flow by thinking about external indicators of flow and the design requirements for achieving it.
Indicators of Flow
- Concentration on task: the player is able to forget about different tasks. (Csikszentmihalhyi, 1991)
- Loss of Self Consciousness: the player is absorbed in the task and doesn’t self-scrutinize. (Csikszentmihalhyi, 1991)
- Transformation of Time: the player loses a sense of time passage. (Csikszentmihalhyi, 1991)
- Autotelic Experience: the act of completing the task becomes its own reward. (Kiili, 2005)
Design Requirements of Flow
- Challenge-Skill Balance: the challenge of the game is balanced with player skill. (Jackson and Marsh, 1996)
- Action-Awareness Merging: Involvement is high enough that activity is automatic (Winn, 2004)
- Goals: clearly defined goals are present (Novak, Hoffman, and Duhachek, 2003)
- Unambiguous Feedback: the game provides clear feedback on correct or incorrect actions.
- Control: the player feels a sense of agency. (Csikszentmihalhyi, 1991)
Scholars often cite the ability of games to provide an authentic experience or a simulated experience as a major affordance. Instead of reading about chemical reactions in a book, players can combine chemicals in game to witness realistic interactions. This allows teachers and instructors to provide experiences that may otherwise be difficult to achieve because of high costs (expensive materials), dangerous conditions (flight training), or logistical challenges.
You can learn more about authentic experiences here.
Scaffolding is a learning theory concept that is at the core of educational theory. The basic principle of scaffolding in formal education is that a teacher (or other mentor) can provide increasing challenges and guidance. As the learner grows in skill and knowledge, the support provided by the mentor gradually decreases and eventually disappears.
Games are natural scaffolding machines. The perfect example of this is the class Super Mario Bros. The game introduces increasing challenges with less and less guidance as how to overcome the challenges until the player is capable of extremely difficult feats of physics-based puzzle solving. The co-existence of challenge and learning in games is so core to play that many scholars argue that, once a player has nothing to learn, they are no longer challenged and disengage from the game (Fabricatore, 2007).
A sub element of scaffolding is the concept of immediate feedback. Games can provide immediate feedback on the correctness or incorrectness of actions. Being able to judge the validity of an action and tell the player how to correct their action is something that even the most attentive school teacher cannot do, as it requires an inhuman level of attention.
You can learn more about Scaffolding and immediate feedback in games here.
Dynamic Assessment / Intelligent Tutoring
One of the most exciting areas of current learning games research is in the application of dynamic assessment or “intelligent tutoring”. Advances in machine learning have allowed designers to build games that learn from the player’s behaviors and can recommend actions. The game may see a player making mistakes and recommend they re-read a chapter or take a quiz. Or, the game may intervene to suggest an in-game course of action. As the learning games field continues to grow, the application of machine learning will only become more exciting!
Emotional connection to learning is an important aspect of internalizing content. The emotional connection to a game is often achieved through the inclusion of compelling narrative and characters (Whittle, 2010; Barab et al, 2010). By encouraging character identification or building transformational narratives, the games allow players to feel connected to the outcomes of their in-game actions.
A topic that cannot be ignored when talking about learning games, is the concept of learning transfer. For years, scholars and teachers alike assumed that knowledge gained within the game simply transferred to the real world. This, however, is not the case.
Often, game content does not coincide with practical usage in the real world, leaving players unable to connect their in-game experience with actual application. In order to encourage transfer of knowledge, designers and teachers must facilitate players in associating their in-game learning with real-world applications.
Playing Learning Games vs Playing Commercial Games
The games for learning community remains involved in a great debate over the value and cost of specifically design learning games and learning by playing commercial games. The two are certainly not mutually exclusive, but they each require their own set of theories and methods. Before diving in, though, what does each mean?
Commercial games are just what they sound like: games that are commercially available to the public. Scholars from many fields have sought to apply games from Sim City to Civilizations to Catan in learning contexts, with mixed success. Researchers tout these commercial games for their high production value, engaging game designs, and ability to connect to learner interests. Not to mention, the games already exist and don’t require the massive investment of creating a game from scratch. The downside to such games is that they are not designed to teach, and therefore the target knowledge can get lost in the game play. Players may very well engage with the games and find playing them fun, but only through very precise facilitation can we ensure players are actually learning what we want them to learn.
We like to think of learning games as games that were specifically built to teach something. These games are created with the primary purpose of conveying knowledge either in formal school settings, museums, or in self-guided learning at home. The great advantage of games like this is that an expert in the subject matter is often collaborating with a team of learning designers to create a system that is very effective in teaching the target concept without providing a lot of “background noise”. The game is focused on building a skill or knowledge base and interacting with that knowledge base is so critical that the player really can’t succeed in the game without some level of mastery over the knowledge. Unfortunately, the development cost of these games, combined with the relatively low distribution and popularity makes designing them a somewhat monumental task.
Keep reading to learn about some of the primary frameworks used to design learning games.
Learning Games Design Frameworks
There are a significant number of game design frameworks for game-based learning. Our goal here at NASAGA is to eventually map them all, but that will take a very long time.
Check out the game design framework page for details.
Objects to Think With
Holbert and Wilensky proposed this model in 2019 as a way of using constructivism to guide game design. In this approach, the game uses authentic representations of content and players get very little guidance, using the representation as a tool for experimentation.
Not many games have been made with this fairly new framework. Holbert and Wilensky’s example game, Particles!, used an in-game atomic modeling system to make blocks with different elemental qualities (heavy, bouncy, sticky, etc). They could then use the blocks to navigate jumping puzzles in the level.
In 2010 and 2012 Sasha Barab led a team releasing a series of articles exploring transformational play. This complicated design framework empowers agency learners by showing how their choices (informed or otherwise) interact with the narrative of the game. The framework has been heavily tested in Quest Atlantis.
Intrinsically Integrated Games
Kafai (1996) put forth that in order for games to teach knowledge playing the game must require use of that knowledge. The idea is that games must use the core knowledge as part of the mechanics of play. Zombie Divider by Habgood and Ainsworth (2011) used this model to build their mechanics. In their game players needed to constantly use division to power weapons and defeat enemies.
About the Author
Clayton Whittle is a Ph.D. candidate at Pennsylvania State University, focusing on games for social impact research. He serves on the board of NASAGA and handles the blog.
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Barab, S. A., Gresalfi, M., & Ingram-Goble, A. (2010). Transformational play: Using games to position person, content, and context. Educational Researcher, 39(7), 525–536. https://doi.org/10.3102/0013189X10386593
Csikszentmihalyi, M. (1991). Flow: The psychology of optimal experience. New York: Harper Perennial.
Fabricatore, C. (2007) “Gameplay and game mechanics design: a key to quality in videogames”. In Proceedings of OECD-CERI Expert Meeting on Videogames and Education, Santiago de Chile, Chile, 2007. [online]
Jackson, S. & Marsh, H. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport & Exercise Psychology, 18, 17–35.
Kiili, K. (2005). On educational game design: Building blocks of flow experience. Tampere, Finland: Tampere University of Technology Press.
Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the flow construct in online environments: A structural modeling approach. Marketing Science, 19, 22–42.
Whittle, J. C. (2010). Measuring Morality: Moral Frameworks in Videogames. Texas A&M University.
Winn, W. (2004). Cognitive perspective in psychology. In D. H. Jonassen (Ed.), Handbook of research on educational communication and technology (79-112). Mahwah, NJ: Lawrence Erlbaum.