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Evolving expert knowledge bases: Applications of crowdsourcing and serious gaming to advance knowledge development for intelligent tutoring systems.

机译：不断发展的专家知识库：众包和严肃游戏的应用，可促进智能辅导系统的知识发展。

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摘要

This dissertation presents a novel effort to develop ITS technologies that adapt by observing student behavior. In particular, we define an evolving expert knowledge base (EEKB) that structures a domain's information as a set of nodes and the relationships that exist between those nodes. The structure of this model is not the particularly novel aspect of this work, but rather the model's evolving behavior. Past efforts have shown that this model, once created, is useful for providing students with expert feedback as they work within our ITS called Rashi. We present an algorithm that observes groups of students as they work within Rashi, and collects student contributions to form an accurate domain level EEKB. We then present experimentation that simulates more than 15,000 data points of real student interaction and analyzes the quality of the EEKB models that are produced. We discover that EEKB models can be constructed accurately, and with significant efficiency compared to human constructed models of the same form. We are able to make this judgment by comparing our automatically constructed models with similar models that were hand crafted by a small team of domain experts.;We also explore several tertiary effects. We focus on the impact that gaming and game mechanics have on various aspects of this model acquisition process. We discuss explicit game mechanics that were implemented in the source ITS from which our data was collected. Students who are given our system with game mechanics contribute higher amounts of data, while also performing higher quality work. Additionally, we define a novel type of game called a knowledge-refinement game (KRG), which motivates subject matter experts (SMEs) to contribute to an already constructed EEKB, but for the purpose of refining the model in areas in which confidence is low. Experimental work with the KRG provides strong evidence that: 1) the quality of the original EEKB was indeed strong, as validated by KRG players, and 2) both the quality and breadth of knowledge within the EEKB are increased when players use the KRG.
机译：本文提出了一种新的努力，即通过观察学生的行为来开发适应性的ITS技术。特别是，我们定义了一个不断发展的专家知识库（EEKB），该知识库将域的信息构造为一组节点以及这些节点之间存在的关系。该模型的结构不是本工作的特别新颖之处，而是模型的演变行为。过去的努力表明，这种模型一旦创建，就可以为学生提供专业反馈，因为他们可以在我们称为ITS的ITS中工作。我们提出了一种算法，可观察在Rashi中工作的学生群体，并收集学生的贡献以形成准确的域级别EEKB。然后，我们提出了模拟真实学生互动的15,000多个数据点并分析所产生的EEKB模型的质量的实验。我们发现，与相同形式的人类构建模型相比，EEKB模型可以准确构建，并且效率显着。通过比较自动构建的模型与由一小组专家组成的类似模型，我们可以做出此判断。我们专注于游戏和游戏机制对模型获取过程各个方面的影响。我们讨论在源ITS中实现的显式游戏机制，从中收集我们的数据。为我们的系统提供游戏机制的学生贡献了更多的数据，同时也完成了更高质量的工作。此外，我们定义了一种新型的游戏，称为知识完善游戏（KRG），它可以激发主题专家（SME）为已经构建的EEKB做出贡献，但目的是在信心低下的领域中完善模型。与KRG进行的实验工作提供了有力的证据：1）原始EEKB的质量确实很强，经KRG玩家证实，并且2）玩家使用KRG时EEKB中知识的质量和广度都得到了提高。

著录项

  • 作者

    Floryan, Mark.;

  • 作者单位

    University of Massachusetts Amherst.;

  • 授予单位 University of Massachusetts Amherst.;
  • 学科 Education Technology of.;Computer Science.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 217 p.
  • 总页数 217
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:42:03

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