mLearning Edugame taxonomy dimensions
I'd like to suggest three dimensions of analysis when categorizing mLearning edugames: content form, activity structure, and topic area.
Let's define "edugame" as a game that intends to increase your skills/knowledge as a result of play. It has common surface elements of games (like chance, movement on a board, players, levels of difficulty) but also has an informational topic focus and is intended to increase player knowledge or skill such that a learning effect is intended in pre-post game comparative measurement. It may be fun, but Angry Birds is not an edugame. You might learn from it, but a classic TOEFL test prep app is also not an edugame.
At the recent mLearn Con I attended an excellent, data filled presentation by Ambient Insight. In it, they mentioned a taxonomy of 6 types of "mobile edugame", which is absent from the slides and executive overview that they later posted.
The 6 types of mobile edugames Ambient listed are:
- Knowledge based
- Skills based
- Brain trainers & cognitive fitness
- Role-playing and business simulations
- Language learning
- Location based
This is a useful taxonomy, but to me is sort of jumbled. Sorting it out, I found three analysis dimensions: content form, activity structure, and topic area.
I'll define content form as the chunking of the information that is manipulated. For example, I interpret "knowledge based" as "needing facts to win", like Trivial Pursuit. "Skills based" is to me "have to express the answer in the proper, often physical, form", for example Mario Super Sluggers if it were calibrated to require exactly the right movements as actual baseball (which it doesn't, and is therefore not an edugame) .
Activity structure refers to how the task is accomplished. For example, brain trainers and cognitive fitness games present batteries of problems in a knowledge domain and, usually, score is determined by combination of speed and accuracy. In contrast, role-playing and business simulations require users to adopt a contextual persona to fulfill the game's requirements. We could get into sub-dimensions here, for example depth of context-- brain trainers tend to be casual games while role-playing games can be very elaborate. However, both kinds of games can teach vocational skill (contrast: timed flashcards for study of drug interactions vs. role-playing a pharmaceutical sales call). I would also place location based gaming on this dimension since it refers to the how: The game structure is influenced by physical context.
However, location-based might also appear on the topic area dimension: Imagine a mobile Trivial Pursuit that changes questions whether it is played in New York City or San Francisco. Language learning is clearly a topic area, as are mathematics, science, and vocational areas.
These dimensions of analysis are not just abstract academic thoughts. In my professional life I need to make strategic product decisions based on fit of pedagogy, platform, and target market. These dimensions can help me.
- Serving students who want quick practice while waiting for a train: Probably I want to avoid elaborate activity structures requiring large chunks of content.
- Helping language learners with memorization of grammatical facts: The topic of language is in the user story, a discrete task is a likely candidate, and the content form is knowledge based.
- In contrast, helping language learners with pronunciation would be more skill based and could more easily benefit from role playing (pronunciation varies in talking to friends vs. your boss).
This topic is far from exhausted, but conceptualizing mLearning games with a taxonomy can be very helpful.