ITR : Tutoring scientific explanations via natural language dialogue

ITR：通过自然语言对话辅导科学解释

• 批准号: 0908146
• 负责人: Kurt VanLehn
• 金额: $ 13.95万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0908146&HistoricalAwards=false
• 关键词: ITR; Tutoring; scientific; explanations; via

It is widely acknowledged, both in academic studies and the marketplace, that the most effective form of education is the professional human tutor. A major difference between human tutors and computer tutors is that only human tutors understand unconstrained natural language input. Recently, a few tutoring systems have been developed that carry on a natural language (NL) dialogue with students. Our research problem is to find ways to make NL-based tutoring systems more effective. Our basic approach is to derive new dialogue strategies from studies of human tutorial dialogues, incorporate them in an NL-based tutoring system, and determine if they make the tutoring system more effective. For instance, some studies are determining if learning increases when human tutors are constrained to follow certain strategies. In order to incorporate the new dialogue strategies into our existing text and spoken NL-based tutoring systems, two completely new modules are being developed. One new module will interpret student utterances using a large directed graph of propositions called an explanation network, which is halfway between the shallow and deep representations of knowledge that are currently used. The second new module uses machine learning to improve the selection of dialogue management strategies.The research is thus a multidisciplinary effort whose intellectual merit lies in new results in the cognitive psychology of human tutoring, in the technology of NL processing, and in the design of effective tutoring systems. Improved NL-based tutoring systems could have a broad impact on education and society.

在学术研究和市场中，人们广泛认为，最有效的教育形式是专业的人类导师。 人类导师和计算机导师之间的主要区别在于，只有人类导师才能了解无约束的自然语言输入。 最近，已经开发了一些与学生进行自然语言（NL）对话的辅导系统。 我们的研究问题是找到使基于NL的辅导系统更有效的方法。 我们的基本方法是从人类教程对话的研究中得出新的对话策略，将其纳入基于NL的辅导系统中，并确定它们是否使辅导系统更有效。 例如，某些研究正在确定当人类导师被限制以遵循某些策略时，学习是否会增加。 为了将新的对话策略纳入我们现有的文本和基于NL的辅导系统中，正在开发两个全新的模块。 一个新的模块将使用称为解释网络的大型命题的大图来解释学生的话语，该图是当前使用的知识的浅层和深度表示之间的一半。 第二个新模块使用机器学习来改善对话管理策略的选择。因此，该研究是一种多学科的努力，其智力优点在于人类辅导的认知心理学，NL处理技术以及有效的辅导系统的设计。 改进的基于NL的辅导系统可能会对教育和社会产生广泛的影响。