Towards the Identification of Players' Profiles Using Game's Data Analysis Based on Regression Model and Clustering

摘要

Personalization of serious games is an important factor for motivating and engaging players. It requires the identification of players' profiles through the analysis of large volume of data including game data. This research study aims at identifying relevant data from an online serious game and the appropriate data mining methods for deduction of players' profiles. Multiple linear regression is applied to analyze the influence of player's characteristics on his performance. Moreover, clustering technique is used, in particular K-means, to extract players' clusters and to identify their common characteristics. The regression models showed that the number of access to the game, completed quests and advantages used contribute significantly to the scores and the gaming duration, while the clustering revealed three forms of players' participation: beginner, intermediate and advanced; who interact with the game according to their experiences.