My thesis project is a game which constantly transforms itself for each player. It does so by ‘understanding’ players through their gameplay and designs itself in such a way that each player gets a unique, personalized and optimal experience every time they play the game. The purpose of doing so is to cater to the diverse needs of players and tailor the game based on their skills and preferences. The game in question is Monkey Swing, which is a web-based, color-matching, memory game to develop Executive Functions (E.F).
Process Overview: Data logs generated through gameplay are analyzed and stored into a persistent learner model residing on an external database. A data stream runs from the learner model to the adaptation engine which constantly analyzes the data to generate content blueprints, these blueprints are updated frequently as more data is received from the learner model database . When the game engine needs to create more content it asks the adaptation engine for updates. The adaptation engine, in response to the query, sends the most recent content blueprint. The game engine then materializes the content blueprint into game content.
2 thoughts on “Concept Overview”
Thanks for the reference Bhavana! I am definitely working on personalized feedback in my adaptivity framework. The interesting thing to consider in such feedback is that how much do you want to share with the learner and what are the relevant details of gameplay (other than just the score) that might prove useful to the players?
I was playing this game from Piece Of Mind (http://www.pomindcake.com/game/soda, similar to Diner Dash) and was wondering if you will also be providing personal feedback to your players and if that has anything to do with the Adaptivity process integrated within the game.
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