EAGER: Machine learning of discourse structure for personalized online tutoring

EAGER：用于个性化在线辅导的话语结构的机器学习

• 批准号: 1450543
• 负责人: Geoffrey Gordon
• 金额: $ 20万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450543&HistoricalAwards=false
• 关键词: EAGER; Machine; learning; discourse; structure

Technology in education provides both tremendous opportunities and tremendous challenges.  By delivering instruction online, we may be able to reach a much wider range of students than previously possible.  But, while technology allows instruction to reach more students, it also risks losing the rich interaction and feedback that in-person instruction provides.  To be truly effective, courses need both rich content and personalized interaction and feedback. So, this project seeks to provide a technical foundation for restoring richer interaction to the online learning experience, particularly through natural language content such as discussion boards and peer grading.The project will develop machine learning techniques for discovering high-level structure in natural language text, including discourse structure (relationships among pieces of text), topic structure, and semantic structure.  To discover this structure, the project investigates the use of spectral learning methods.  These methods, which rely on factoring a matrix or tensor of observed moments, are able to learn latent structures efficiently and without local optima.  Of particular interest and challenge is to develop tractable methods that can learn latent structure from unlabeled or weakly-labeled data.  Many current NLP techniques have difficulty with informal language and do not extract higher level structure; our goal is to develop spectral methods to address these weaknesses. The project will attempt to learn this high-level latent structure in data taken from discussion boards of large online classes.  The idea is that future work could use this understanding to help personalize the way students access this sort of rich natural language content: e.g., by helping to focus searches, find relevant and cohesive content, synthesize answers to student questions, promote productive behaviors in online discussions, and support self and peer grading.  The eventual goal is to provide personalized interfaces to students that facilitate their learning of novel material.

教育技术既提供了巨大的机遇，也带来了巨大的挑战。 通过在线提供指导，我们可能能够比以前更广泛地接触到学生。 但是，尽管技术允许教学接触到更多的学生，但它也有失去面对面教学所提供的丰富互动和反馈的风险。 为了真正有效，课程需要丰富的内容和个性化的互动和反馈。因此，本项目旨在为恢复在线学习体验中更丰富的交互提供技术基础，特别是通过讨论板和同行评分等自然语言内容。本项目将开发用于发现自然语言文本中高级结构的机器学习技术，包括话语结构（文本之间的关系），主题结构和语义结构。 为了发现这种结构，该项目研究了频谱学习方法的使用。 这些方法依赖于对观测矩的矩阵或张量进行因子分解，能够有效地学习潜在结构，并且没有局部最优。 特别感兴趣和挑战是开发易于处理的方法，可以从未标记或弱标记的数据中学习潜在结构。 许多当前的NLP技术难以处理非正式语言，并且不能提取更高层次的结构;我们的目标是开发谱方法来解决这些弱点。该项目将尝试从大型在线课程的讨论板中获取的数据中学习这种高层次的潜在结构。 我们的想法是，未来的工作可以使用这种理解来帮助个性化学生访问这种丰富的自然语言内容的方式：例如，通过帮助集中搜索，找到相关和有凝聚力的内容，综合学生问题的答案，促进在线讨论中的生产行为，并支持自我和同伴评分。 最终的目标是为学生提供个性化的界面，以促进他们对新材料的学习。