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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9600
Title: Detection of player learning curve in a car driving game
Authors: Bontchev, Boyan
Vassileva, Dessislava
Keywords: player learning curve
detection
pattern
video game
component
RAGE
Issue Date: Mar-2018
Citation: Bontchev, B., Vassileva, D. (March, 2018) Detection of player learning curve in a car driving game, accepted for presentation at 12th annual International Technology, Education and Development Conference (INTED2018), Valencia, 5- 7 March, 2018
Abstract: Detection of learning curves of player metrics is very important for the serious (or so called applied) games, because it provides an indicator representing how players master the game tasks by acquiring cognitive abilities, knowledge, and necessary skills for solving the game challenges. Real time identification of specific patterns in the learning curve of a particular player may be applied for dynamic adjusting of learning task difficulty and audio-visual features of the game. The paper presents a method for automatic and straightforward detection of specific learning curves at run time within a 3D video game of car driving in various weather conditions. The method uses a client-side software component called “Player-centric rule-and-pattern-based adaptation asset” and developed in the scope of the RAGE (Realising and Applied Gaming Ecosystem) H2020 project. This component is integrated within the video game in order to detect dynamically specific player learning curve patterns of moving average of overall player performance. The paper explains how these patterns were identified while players play the game and, next, how are defined formally within the asset for a detection at run time. The presented results show that the patterns are found in consecutive game sessions, although the changes in player performance and playing time.
URI: http://hdl.handle.net/1820/9600
Appears in Collections:1. RAGE Publications

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