CHS: Small: EXP: Open Corpus Personalized Learning

CHS：小型：EXP：开放语料库个性化学习

• 批准号: 1525186
• 负责人: Peter Brusilovsky
• 金额: $ 49.98万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525186&HistoricalAwards=false
• 关键词: CHS; Small; EXP; Open; Corpus

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.

该项目挑战了自适应超媒体系统需要昂贵的知识工程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。自适应教育超媒体以其提高学习效果和参与度的能力而闻名，最大限度地为不同知识水平的学习者提供教育机会。这种更自动化的，开放的语料库的方法，自适应教育超媒体的发展将增加的数量和各种资源，可用于有意义的在线学习，特别是对个人学习自己。教育内容的自动知识索引使系统易于维护和随时间更新。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，以适应新的材料，因为他们被收购。自动组织、索引和自适应推荐分布式教育内容而不需要系统开发人员手动处理的能力，使得新材料能够动态地集成，并且响应于学生的需求而付出最少的努力。该项目融合了文本分析，人类学习和个性化的研究，以实现开放语料库个性化学习。它从一组组织良好的、手动选择的材料中开发了该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是有缺陷的或不完整的，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动工作。多视角也使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的动态模型的基础。领域和用户模型的集合用于在开放语料库上下文中提供反应性和主动性自适应指导。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放式语料库个性化学习开辟了道路。领域建模，用户建模和个性化技术在这项研究中开发的评估将使用一个多层的框架，包括评估由学科专家，性能预测，交叉验证和用户研究。