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Modelling Learning Experiences in adaptive multi-agent learning environments

机译：自适应多代理学习环境建模学习经验

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摘要

In next generation technology-enhanced learning environments, intelligent educational systems can benefit from tapping into multi-agent, adaptive, gamified learning experiences, which transform the traditional instructional paradigm from classroom-based learning to personalised learning in any setting, whether collective or individual. Such settings enable learning targeted to each individual's learning styles and needs, through the use of autonomous technological agents as actuators of the learning process. Learning components which will respond to the needs of such an educational framework should provide capabilities for adaptive, affective and interactive learning, automatic feedback and automatic assessment of the learners' behavioural state. A novel methodology is proposed to model such components, which focuses on the representation and management of learning objects (LOs) for any educational domain, any type of learner and learning style and any learning methodology, while fostering non-linearity in the educational process. This methodology is supported by a strategy for modelling and adapting re-usable learning objectives, coupled with an ontology that enables scalable and personalized decision-making over learning activities on autonomous devices, enabling dynamic modularisation of learning material during the learning process.
机译：在下一代技术增强学习环境，智能教育系统可以从窃听到多智能体，自适应，游戏化学习经验，从改造传统的教学模式有利于课堂教学为基础的学习，在任何环境下个性化的学习，无论集体或个人。这样的设置使学习针对每个人的学习方式和需求，通过采用自主技术剂作为学习过程的执行机构。学习这将这样的教育框架的需要作出回应组件应提供适应性，情感和互动学习，自动反馈和学习者的行为状态的自动评估能力。一种新颖的方法，提出了这样的组件，其重点是学习对象（LOS）对于任何教育领域，任何类型的学习者的学习风格和任何的学习方法的表示和管理模式，而在教育过程促进非线性。这种方法是通过战略模拟和调整重复使用的学习目标，加上本体，使可扩展性和个性化的决策在学习上自主设备的活动，使学习过程中的学习材料的动态模块化支持。

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