EAGER: Using Crowdsourced Virtual Students to Create Intelligent Tutors

EAGER：利用众包虚拟学生来创建智能导师

• 批准号: 1352207
• 负责人: Andrew Olney
• 金额: $ 16.38万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1352207&HistoricalAwards=false
• 关键词: EAGER; Using; Crowdsourced; Virtual; Students

This project will develop and evaluate the potential of a new human-computer system that bridges the roles of virtual student and virtual teacher to allow humans and computers to take turns teaching and learning from each other. The key insight is that reading comprehension activities (e.g., vocabulary building, summarizing, question generation, concept mapping) closely parallel the knowledge engineering required to create virtual teachers for intelligent tutoring systems (ITSs). The system links these activities so that when students read online, they engage a virtual student in educational tasks that both improve their reading comprehension and simultaneously contribute to the creation of ITSs for future students. An important aspect of the proposed research is to find the optimum balance between student learning (which benefits the individual) and the creation of ITS knowledge representations (which benefits many). Specific research objectives are: (1) to develop a baseline platform (called BrainTrust) such that students can create ITS knowledge representations by teaching a virtual student; (2) to study the relationship between the student's ability, the virtual student's ability, the student's learning outcomes, and the quality of knowledge representations produced. A distinctive characteristic of the proposed research is the study of these questions in ecologically valid conditions, as students engage in authentic study, while also participating in randomized experiments.The research may lead to the development of systems that improve reading comprehension, which may have broad benefits given the centrality of reading comprehension to all learning. In particular, problems with reading comprehension have been linked to first-year college student dropout that disproportionately affects African-American students. The research will also enhance infrastructure for research and education through the development and dissemination of the BrainTrust platform, a next-generation computing infrastructure to rapidly create and deploy ITSs tailored to specific needs. If this exploratory project demonstrates that the dual outcomes of human learning and high-quality knowledge representations can be achieved, it will open a new area of research that brings teaching these virtual students full circle with learning from their derived intelligent tutoring systems.

该项目将开发和评估一种新的人机系统的潜力，该系统将虚拟学生和虚拟教师的角色连接起来，使人类和计算机能够轮流相互教学和学习。关键的见解是，阅读理解活动（例如，词汇构建、总结、问题生成、概念映射）紧密地平行于为智能辅导系统（ITS）创建虚拟教师所需的知识工程。该系统链接这些活动，使得当学生在线阅读时，他们使虚拟学生参与教育任务，这既提高了他们的阅读理解能力，又同时有助于为未来的学生创建ITS。拟议的研究的一个重要方面是找到学生学习（这有利于个人）和ITS知识表示（这有利于许多人）的创建之间的最佳平衡。具体的研究目标是：（1）开发一个基线平台（称为BrainTrust），使学生可以通过教授虚拟学生来创建ITS知识表示;（2）研究学生的能力、虚拟学生的能力、学生的学习成果和所产生的知识表示质量之间的关系。该研究的一个显著特点是在生态有效的条件下研究这些问题，学生参与真实的学习，同时也参与随机实验，该研究可能会导致系统的发展，提高阅读理解，这可能会有广泛的好处，因为阅读理解的中心地位，所有的学习。特别是，阅读理解的问题与大学一年级学生辍学有关，这对非洲裔美国学生的影响不成比例。该研究还将通过开发和传播BrainTrust平台来加强研究和教育的基础设施，这是一个下一代计算基础设施，可以快速创建和部署针对特定需求的ITS。如果这个探索性的项目证明了人类学习和高质量知识表示的双重结果是可以实现的，它将开辟一个新的研究领域，使这些虚拟学生能够从他们的衍生智能辅导系统中学习。