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Ontology-based open-corpus personalization for e-Learning.

机译：用于电子学习的基于本体的开放语料库个性化。

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摘要

Conventional closed-corpus adaptive information systems control limited sets of documents in predefined domains and cannot provide access to the external content. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of the World Wide Web and are expected to operate on the open-corpus content. In order to provide personalized access to open-corpus documents, an adaptive system should be able to maintain modeling of new documents in terms of domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning.;Information on the World Wide Web is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of content collections. A central domain ontology is used to maintain overlay modeling of students' knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed.;The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach successfully supports open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content.
机译：常规的封闭语料库自适应信息系统控制预定义域中的有限文档集，并且不能提供对外部内容的访问。当大多数信息系统在万维网的开放文档空间中实现并希望在开放语料库内容上运行时，这种限制与当今的要求相矛盾。为了提供对开放体文档的个性化访问，自适应系统应该能够根据领域知识自动，动态地维护新文档的建模。本文探讨了在电子学习环境下开放主体的个性化和开放主体内容语义建模的问题。互联网上的信息并非没有结构。已经为许多在线教学材料（教程，电子书，数字图书馆等）提供了隐含的知识模型，这些知识模型以目录，索引，章节标题，页面之间的链接以及不同样式的文本片段的形式编码。主要的研究方法试图通过将其隐藏并表示为内容集合的粗粒度模型来利用这一层的隐藏语义。中心领域本体用于维护学生知识的覆盖模型，并用作外部教学材料的多个集合的参考点。为了建立本体和开放语料库内容模型之间的联系，开发了一种特殊的本体映射算法。；该方法已应用于基于本体的开放语料库个性化服务中，该服务推荐并自适应地注释在线阅读材料。已选择Java编程领域来进行概念验证。已经组织了一个控制实验，以评估开发的自适应系统和总体上提出的方法。评估结果证明了已实施的开放语料库个性化的几种重要学习效果。对基于日志的数据的分析还表明，系统的开放语料库版本能够提供与紧密语料库相似质量的个性化设置。这样的结果表明，所提出的方法成功地支持了开放式电子学习个性化。需要进行进一步的研究，以验证该方法是否在其他学科领域和其他类型的教学内容中仍然有效。

著录项

  • 作者

    Sosnovsky, Sergey.;

  • 作者单位

    University of Pittsburgh.;

  • 授予单位 University of Pittsburgh.;
  • 学科 Web Studies.;Information Science.;Artificial Intelligence.;Education Technology of.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 316 p.
  • 总页数 316
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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