CHS: Small: EXP: Open Corpus Personalized Learning

这个项目挑战了自适应超媒体系统需要昂贵的知识工程来进行领域和内容建模的假设。它用自动创建的领域模型取代了精心制作的领域模型，降低了开发自适应教育超媒体软件的成本，同时还通过内容提供了更广泛的教学路径。适应性教育超媒体以其提高学习成果和参与的能力而闻名，最大限度地提高了不同知识水平学习者的教育机会。这种更加自动化、开放语料库的自适应教育超媒体方法的发展，将增加有意义的在线学习可用资源的数量和种类，特别是对个人自学而言。教育内容的自动知识索引使系统易于维护和更新。这些新的开放语料库用户建模技术自动调整用户模型和个性化指导，以获得新的材料。自动组织、索引和自适应推荐分布式教育内容的能力，而不需要系统开发人员手工处理，使新材料能够动态集成，并且以最小的努力响应学生的需求。该项目融合了文本分析、人类学习和个性化的研究，以实现开放语料库的个性化学习。它从一组组织良好、人工选择的初始材料中开发了领域和人类学习的模型。自动文本分析创建了具有不同特征的领域模型的集合。每个单独的模型可能有缺陷或不完整，但是它们从多个角度提供了对主题的全面覆盖，从而减少了创建自适应教育超媒体所需的手工工作。多重视角也使该系统在如何指导每个学生方面更具灵活性。这些领域模型被用作构建和维护用户知识动态模型的基础。领域和用户模型的集成用于在开放语料库上下文中提供反应性和主动性的自适应指导。一个人的知识增长是通过观察学习者的行为和获得偶尔的反馈来推断的。这一探索性研究为开启语料库个性化学习开辟了道路。本研究中开发的领域建模、用户建模和个性化技术将使用多层框架进行评估，包括主题专家的评估、性能预测、交叉验证和用户研究。

This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain models with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs. This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.