Player and Learner Models: Independency of Bartle, Kolb and BFI-K (Big5)
Key: KGS13-2
Author: Johannes Konert, Stefan Göbel, Ralf Steinmetz
Date: September 2013
Kind: In proceedings
Publisher: Academic Bookshop
Book title: Proceedings of the 7th European Conference on Games Based Learning (ECGBL)
Keywords: Player Modeling, Bartle Test, Learning Style, Personality, Big5
Abstract: For adaptation and personalization of game play sophisticated player and learner models are used in game-based learning environments. Thus, the game flow can be optimized to increase efficiency and effectiveness of gaming and learning in parallel. In the field of gaming still the Bartle model is commonly used due to its simplicity and good mapping to game scenarios, for learning the Learning Style Inventory from Kolb or Index of Learning Styles by Felder and Silverman are well known. For personality traits the NEO-FFI (Big5) model is widely accepted. When designing games it is always a challenge to assess one player’s profile characteristics properly in all three models (player/learner/personality). Still, it is valuable to collect information to refine the models continuously to adapt the game experience precisely to a player’s models. To reduce the effort and amount of dimensions and questionnaires a player might have to fill out, we proved the hypothesis that both, Learning Style Inventory and Bartle Player Types could be predicted by knowing the personality traits based on NEO-FFI. Thus we investigated the statistical correlations among the models by collecting answers to the standardized questionnaires of Bartle Test, Kolb LSI 3.1 and BFI-K (short version of NEO-FFI). The study was conducted in summer 2012 with seven school classes of grade 9 (12-14year old students) in three different secondary schools in Germany. 76 students participated in the study which was offered optionally after the use of a game-based learning tool for peer learning. We present the results, statistics and correlations among the models as well as the interdependencies with the student’s computer-use preferences and other demographic data. In conclusion, the evaluation proved the independency of the models and the validity of the dimensions.
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