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Using adaptive hypermedia and machine learning to create intelligent Web-based courses.

机译：使用自适应超媒体和机器学习来创建基于Web的智能课程。

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摘要

This work focuses on Web-based intelligent instructional systems and research issues associated with the development of student modeling in an adaptive hypermedia system. The framework is iMANIC (intelligent Multimedia Asynchronous Networked Individualized Courseware), in which courses originating from existing video-taped lectures provide an initial set of slides, audio, and class notes. However, the existing course structure is initially linear, which, though usable, is not optimal for a WWW presentation. Web courses are used asynchronously and thus can provide a more individualized and interactive learning experience than can live courses. Therefore, we investigate ways in which personalized instruction can be delivered via the WWW.; The domain organization used in iMANIC supports a non-linear, individualized course. However, once we introduce a non-linear topic structure, the “lost in hyperspace” problem might arise, in which students become confused about what to study next and how to remember where they have been. To combat these problems, adaptive navigation techniques are used to help guide the student through the course material.; The original class material is presented so that each student sees the same content. This does not take into account learning differences of individual learners. However, iMANIC can consider those differences and adapt the information presented to each user. This adaptive content is achieved through a two phase approach which considers the user's level of understanding and the content that matches the user's preferences. A Naïve Bayes Classifier is used to learn the student's preferences by observing what type of content he chooses to see.; An empirical study of the iMANIC system was conducted during 2000/2001 with 24 students learning Unix Network Programming. Results from this study show distinct differences in students' learning styles and provide evidence that using the same teaching strategies for each student cannot adequately support all students. This is demonstrated through two examples. The first shows that there is not a consistent direction for the correlation between time spent studying and quiz performance. The second shows that using the same parameters for the Naïve Bayes Classifier for every student results in poor overall performance of the classifier.
机译：这项工作的重点是基于Web的智能教学系统以及与自适应超媒体系统中的学生建模开发相关的研究问题。该框架是iMANIC（智能多媒体异步网络化个性化课件），其中源自现有录像带的讲座的课程提供了一组初始的幻灯片，音频和课堂笔记。但是，现有的课程结构最初是线性的，尽管可以使用，但对于WWW演示并不是最佳的。网络课程是异步使用的，因此与现场课程相比，可以提供更多的个性化和交互式学习体验。因此，我们研究了可以通过WWW传递个性化指导的方法。 iMANIC中使用的领域组织支持非线性的个性化课程。但是，一旦我们引入了非线性主题结构，就可能出现“迷失在超空间中”的问题，使学生对下一步学习以及如何记住自己曾经去过的地方感到困惑。为了解决这些问题，自适应导航技术用于帮助指导学生完成课程材料。呈现原始课程材料，以便每个学生看到相同的内容。这没有考虑个别学习者的学习差异。但是，iMANIC可以考虑这些差异并调整为每个用户提供的信息。这种自适应内容是通过两阶段方法实现的，该方法考虑了用户的理解水平以及与用户的偏好匹配的内容。朴素的贝叶斯分类器通过观察学生选择看哪种类型的内容来学习其偏好。在2000/2001年期间对24名学习Unix网络编程的学生进行了iMANIC系统的实证研究。这项研究的结果表明，学生的学习风格存在明显差异，并提供证据表明，对每个学生使用相同的教学策略不能充分支持所有学生。通过两个示例可以证明这一点。第一个表明，花在学习时间和测验成绩之间的相关性并不一致。第二个结果显示，对于每个学生，对于朴素贝叶斯分类器使用相同的参数会导致分类器的整体性能较差。

著录项

  • 作者

    Stern, Mia Keryn.;

  • 作者单位

    University of Massachusetts Amherst.;

  • 授予单位 University of Massachusetts Amherst.;
  • 学科 Computer Science.; Artificial Intelligence.; Education Technology.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 170 p.
  • 总页数 170
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;人工智能理论;
  • 关键词

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