DOI: https://doi.org/10.15368/theses.2015.23
Available at: https://digitalcommons.calpoly.edu/theses/1369
Date of Award
3-2015
Degree Name
MS in Computer Science
Department/Program
Computer Science
Advisor
Michael Haungs
Abstract
Video games are increasingly becoming more intelligent, able to adapt themselves to the individual gamer. Learning styles are a set of models used to categorize people into different types of learners to explain why some people learn better through different methods. Since learning and exploration are such fundamental parts of the video game experience, it is interesting to consider the possibility of applying these learning style models to video games, allowing the video game to adapt to its player, providing a better experience. To consider such adaptation, the game must first be able to detect that learning style from how the player has interacted with it.
Simple metrics collected during game play of an instrumented game (opensource Supertux) are compared to the results of the Hay Group’s Kolb Learning Style Inventory, a paper test designed to determine one’s learning style. A relationship between recordable game play metrics and the academic model for learning would allow a game designer to potentially infer that model from game play and use it to adapt the game to that type of learner.