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Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners

机译:社区组织代理:用于在大量学习者中建立学习社区的人工智能系统

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摘要

Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is peda-gogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners' learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others' learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.
机译:基于网络的(或在线)学习为学习者和讲师提供了前所未有的灵活性和便利性。但是,依赖于以讲师为中心的演示文稿的大型在线课堂可能会孤立许多学习者。这些课程的规模以及学习者的广泛分散,使教师与个别学习者互动或促进学习者协作变得充满挑战。由于大量文献已经证实学习者互动对学习成果的重大影响,因此在学上至关重要的是,帮助分布式学习者进行基于社区的协作学习,并帮助单个学习者改善自我调节。上海交通大学电子学习实验室创建了一个人工智能系统,以帮助将志趣相投的学习者引入规模合理的学习社区。该系统使用多主体机制,根据学习者的学习兴趣,经验和行为来组织和重新组织支持性社区。通过有效的奖励和交换算法,具有相似兴趣和经验的学习者将形成一个社区,以相互支持。仿真实验结果表明,这些算法可以提高识别和分组同类学习者的速度和效率。在这里,我们将详细描述该系统,并介绍其组织学习社区的机制。我们将在不久的将来进行人体实验,以进一步完善该系统。

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