基于五大个人特征的电子游戏玩家行为测量

Muhannad Quwaider, Abdullah Alabed, R. Duwairi
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引用次数: 2

摘要

前几年电子游戏的传播导致了与电子游戏产业相关的新领域的出现。其中一个领域是探索玩家在受控环境(如计算机实验室)中玩特定游戏后的情绪和行为。在本文中,我们将介绍一种分析玩家在受控环境之外的情绪和行为的新方法,并使用游戏内数据而不是传统的问卷调查和访谈。该系统有望成为人类互动所需的未来物联网(IoT)应用的一部分。我们将使用游戏内部数据分析玩家的个性。数据是使用手机开发的视频游戏生成和收集的。然后,收集到的数据使用一个著名的人格特征模型,称为大五因素模型(FFM)进行评估。为了基于FFM创造一组合适的场景去分析玩家的个性,我们开发了一款第一人称射击游戏(FPS)。通过这款游戏,我们成功地生成并收集了数百人的游戏内数据。结果表明,该方法能够通过FFM特征来研究玩家的行为。结果表明,通过重复游戏,FFM性状得分得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In-Video Game Player's Behavior Measurement using Big Five Personal Traits
The propagation of video games in the previous years has led to the emergence of new areas associated with the video game industry. One of these areas is exploring the emotions and the behaviors of players after playing a specific game within a controlled environment such as a computer lab. In this paper, we will introduce a new way of analyzing the emotions and the behavior of players outside a controlled environment and using in-game data rather than using traditional questionnaires and interviews. The proposed system is expected to be part of future Internet of Things (IoT) application that is needed for human interaction. We will analyze the player's personality using in-game data. The data is generated and collected using mobile developed video game. Then, the collected data is evaluated using a well-known personality traits model called big Five-Factor Model (FFM). In order to create a set of appropriate scenarios to analyze the player's personality based on FFM we developed a First Person Shooter (FPS) video game. Using this game, we managed to generate and collect in-game data from hundreds of people. The results show that it was able to study the player's behavior over FFM traits. It was shown that the FFM traits scores are improved by repeating the game.
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