Game analytics and player psychology: creating reliable models of player motivation

游戏分析和玩家心理：创建可靠的玩家动机模型

• 批准号: 1946124
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1946124
• 关键词: Game; analytics; player; psychology; creating

Motivation can be loosely defined as a process of the brain and the mind, capable of driving and deeply shaping human behaviour. Motivational processes are embedded in many everyday life situations, exerting their effects via a wide range of incentive mechanisms and objects. Understanding this process in a videogame context, however, requires a more holistic approach considering not just the incentive properties of the game but also the player personal characteristics.My project aims to create reliable cross-games models of player motivation taking into account the contribution of natural inter individual variability. This will be accomplished linking in-game behavioural data and psychological models via a hybrid approach, where findings from small scale experimental studies (hypothesis-driven) will guide the realization of large scale (data-driven) applications for predicting players' characteristics, future behaviour and motivation evolution. Being able to model player motivation and predict the trajectories of its evolution could possibly lead to personalized and dynamic engagement strategies able to adapt accordingly to the player characteristics and in-game behaviour. Achieving a similar goal would be of pivotal importance in industrial and gamification contexts.

动机可以粗略地定义为大脑和思想的一个过程，能够驱动和深刻地塑造人类行为。激励过程嵌入在许多日常生活中，通过广泛的激励机制和对象发挥其作用。然而，在视频游戏中理解这一过程需要一种更全面的方法，不仅要考虑游戏的激励属性，还要考虑玩家的个人特征。我的项目旨在创建可靠的玩家动机跨游戏模型，同时考虑到自然的个体差异。这将通过混合方法将游戏中的行为数据和心理模型联系起来，其中小规模实验研究（假设驱动）的结果将指导大规模（数据驱动）应用程序的实现，以预测玩家的特征，未来行为和动机演变。能够模拟玩家的动机并预测其演变的轨迹可能会导致个性化和动态的参与策略，从而能够适应玩家的特征和游戏中的行为。实现类似的目标在工业和游戏化背景下至关重要。