Methods and Tools for Active Adapation in Serious Games Based on a Rich User Model

尽管智能辅导系统的有效性已经得到了明确的证明，但它们的广泛使用并没有取得成功。缺乏让学生保持积极性和投入的学习环境是导致这种失败的主要原因之一。严肃游戏提供有趣的一面，以克服动机的缺乏，但它们应该适应玩家，以增加学习收益和游戏体验。然而，在这方面，适应性的基本原理、条件和影响仍未得到充分探讨和评估。我未来五年的研究目标是研究创新方法和设计方法，使严肃游戏在认知、情感和社交方面更具适应性，以增加学习收益和游戏体验。该研究计划有四个具体目标：1。调查和确定游戏中的主要特征，以便在严肃游戏中进行积极的调整。探索和开发从多模态用户行为数据中提取新特征的新方法。3 .研究新的适应模型，以准确预测学习者-参与者行为，并采取适当的适应措施对其作出反应；研究设计具有最佳学习收益的高适应性严肃游戏的方法。两个主要假设将指导我们的研究：1)考虑所有学习者-玩家的许多方面，包括技能，影响，社会行为（导致丰富的学习者玩家模型RPLM）的整体建模方法是成功改编严肃游戏的基础，就其教育效果而言；2)挖掘游戏过程中收集的多模态和多源交互数据需要调整或改进现有的数据挖掘技术，或者发明更合适的技术。RLPM假说将通过探索与它的每一个方面相关的因素而得到完善。行为多模态数据挖掘技术和适当的机器学习方法将被研究，以建立一个准确的适应模型，包括基于游戏期间收集的交互数据的用户行为预测引擎。这项研究的结果将为更明智、适应性更强、更智能的严肃游戏打开大门，并为学习者玩家的行为提供更准确的预测模型。新一代游戏将预测玩家的行动和反应，能够产生积极的行为和情绪，并出于教学目的抑制那些偏差或消极的行为和情绪。根据我们的RLPM调整游戏将带来更相关的游戏体验和优化的教学策略。除了在严肃游戏、教育数据挖掘和ITS研究领域对知识的进步做出贡献外，游戏在

Although the effectiveness of Intelligent Tutoring Systems has been clearly demonstrated, their widespread use has not been a success. The lacking of learning environments that keep students motivated and engaged is one of the major causes of this failure. Serious games provide the playful side, to overcome the lack of motivation but they should adapt to the player to increase the learning gain as well as gameplay experience. However, the rationale, conditions and effects of adaptability remain poorly explored and inadequately assessed in this context. The aim of my research program for the next five years is to investigate innovative methods and design methodology that can make serious games cognitively, emotionally, and socially more adaptive for increased learning gain and gameplay experience. The research program has four specific objectives:1. To investigate and determine the main features in play for an active adaptation in serious games2. To explore and develop novel methods for extracting new features from multi-modal user behaviour data 3. To investigate new adaptation models that could accurately predict learner-player behaviour and respond to it by using appropriate adaptation measures;4. To investigate a methodology for designing highly adaptive serious games with optimal learning gain.Two main hypotheses will guide our studies:1) A holistic modelling approach that considers all of the learner-player's many facets, including skills, affects, social behaviours (leading to a Rich Learner Player Model RPLM) is the basis for a successful adaptation of a serious game in terms of its educational effectiveness;2) Mining multi-modal and multi-source interaction data collected during the game will require adapting or improving existing data mining techniques or inventing more appropriate ones. The RLPM hypothesis will be refined by exploring factors associated with every of its aspects. Behaviour multi-modal data mining techniques and appropriated machine learning approaches will be investigated in order to build an accurate adaptation model, including a user behaviour prediction engine based on interaction data collected during the game. The results of this research will open the door to more informed, adaptive and intelligent serious games, with a more accurate prediction model of the learner-player's behaviour. This new generation of games will anticipate the player's actions as well as reactions and will be able to generate positive behaviours and emotions and inhibit those deviants or negatives for pedagogical purposes. Adapting the game based on our RLPM will lead to a more relevant gameplay experience and optimized pedagogical strategies. In addition to the contribution to the advancement of knowledge in serious games, educational data mining and ITS research fields, the well-established game in