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Data Mining Methods to Assess Student Behavior in Adaptive e-Learning Processes

机译:在自适应电子学习过程中评估学生行为的数据挖掘方法

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摘要

How could data mining help the development of e-learning methodologies? How could an instructional designer take benefit from the use of adaptive learning? How could adaptive learning be implemented in an Open Source platform? In this paper will be described the implementation of adaptivity technology in a specific, Open Source, Learning Management System (LMS). After a preliminary study about the adaptive features already built-in and the capabilities ready to perform a suitable student modeling, the research team extended those capabilities with a specific data model, student model and tutoring engine to perform automatic monitoring and sequencing of Learning Objects for each particular learner. The future implementation of this project is related to testing activities in order to prove the efficiency method in content and course delivery. This paper describes some best practices developed during a Tempus IV Project granted by EU.
机译:数据挖掘如何帮助开发电子学习方法?教学设计师如何从自适应学习中受益?如何在开放源代码平台中实施自适应学习?本文将描述自适应技术在特定的开源学习管理系统(LMS)中的实现。在对已经内置的自适应功能和准备执行适合的学生建模的功能进行了初步研究之后,研究团队使用特定的数据模型,学生模型和辅导引擎扩展了这些功能,以针对学习对象进行自动监视和排序每个特定的学习者。该项目的未来实施与测试活动有关,以证明在内容和课程交付方面的效率方法。本文介绍了欧盟批准的Tempus IV项目开发的一些最佳实践。

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