Teaching a virtual pet

Playing educational games, as well as learning in general, should be seen as being an active process that is meaningful from the subject's point of view. The importance of motivational themes should also be considered. The positive relationship between cognitive and motivational themes in sciences and mathematics learning has been widely studied (e.g. Rao, Moely & Sasch 2000; Lapointe, Legault & Batiste 2005; Mason & Scrivani 2004). While a positive relationship between learning and motivation exists, there is no absolute understanding that increased motivation automatically increases learning outcomes. Because of this, motivation and educational outcome should be studied as unconnected phenomena.

In the Animal Class game series (e.g. Ketamo & Suominen 2008; Kiili & Ketamo 2007) motivation to learn is based on a need to teach a virtual pet and on the challenge of competing with this virtual pet against the schooled pets of others. One of the most important features of the Animal Class game series is the freedom of teaching. A pupil can freely teach whatever s/he wants in a given subject area and the game mechanics do not restrict the process. The community, including other pupils, teacher or parents, is the directing force in learning. Animal Class is a game series meant for pupils aged 6-12 years. While playing pupils are asked to teach mathematics, natural sciences, languages and arts to their virtual pets. To date 22 different Animal Class games have been published. When allowing learners to use their knowledge and letting them see how their knowledge fits into the framework of the game, the game retains the idea of 'Learning by Doing' (Dewey 1938). With this approach, the game is no longer the teacher: the game only ensures that the learner has the skills and knowledge required to pass the game.

One of the key elements during the development process of Animal Class was related to the artificial intelligence underlying the game. The traditional goal of AI is to make machines perform cognitive tasks that humans can do, or try to do. In the game industry, the definition of AI is extended so that the most important task of a game's AI is to entertain. It is allowable for game AI to cheat or be 'stupid' in order to achieve the illusion of intelligent behavior (Scott 2002). The balancing issue is also challenging within the domains of game development and AI research: It is easy to create a poor or perfect opponent; the challenge is building a reliable and entertaining opponent. (Liden 2003; Scott 2002). In Animal Class the intelligence of opponents and game balancing is constructed by game players themselves: the virtual pets are teachable intelligent agents and game mechanics are based upon their behavior.

Technically, the game AI in Animal Class is based on a dynamically extensible Bayesian (belief) Network (e.g. Reye 2004). The extensible user model is relatively close to Semantic Bayesian Networks (e.g. Kim, Hong & Sho 2007) and the Qualitative Probabilistic Network (e.g. Lucas 2005). Parallel methods, behavior recording (e.g. Bowling, Furnkranz, Graepel & Musick 2006, Houlette 2003) and behavior mining (e.g. Mukkamala, Xu & Sung 2006, Kuo, Huang, Chen & Jeng 2005), have been studied and used in the game industry for some time. Behavior recording refers to game AI studies and behavior mining usually refers to intrusion detection in networks.

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