Developing Auto Tutor for Computer Literacy and Physics

开发计算机知识和物理自动辅导员

• 批准号: 0106965
• 负责人: Arthur Graesser
• 金额: $ 127.41万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0106965&HistoricalAwards=false
• 关键词: Developing; Auto; Tutor; Computer; Literacy

The Tutoring Research Group at the University of Memphis has developed a computer tutor (called AutoTutor) that simulates the discourse patterns and pedagogical strategies of unaccomplished human tutors. The typical tutor in a school system is unaccomplished in the sense that the tutor has had no training in tutoring strategies and has only introductory-to-intermediate knowledge about the topic. The development of AutoTutor was funded by an NSF grant (SBR 9720314, in the Learning and Intelligent Systems program). The discourse patterns and pedagogical strategies in AutoTutor were based on a previous project that dissected 100 hours of naturalistic tutoring sessions.AutoTutor is currently targeted for college students in introductory computer literacy courses, who learn the fundamentals of hardware, operating systems, and the Internet. Instead of merely being an information delivery system, AutoTutor serves as a discourse prosthesis or collaborative scaffold that assists the student in actively constructing knowledge. AutoTutor presents questions and problems from a curriculum script, attempts to comprehend learner contributions that are entered by keyboard, answers student questions, formulates dialog moves that are sensitive to the learner's contributions (such as short feedback, pumps, prompts, assertions, corrections, and hints), and delivers the dialog moves with a talking head. The talking head displays emotions, produces synthesized speech with discourse-sensitive intonation, and points to entities on graphical displays. AutoTutor has seven modules: a curriculum script, language extraction, speech act classification, latent semantic analysis (a statistical representation of domain knowledge), topic selection, dialog management, and a talking head. Evaluations of AutoTutor have shown that the tutoring system improves learning with an effect size that is comparable to typical human tutors in school systems, but not as high as accomplished human tutors and intelligent tutoring systems. The dialog moves of AutoTutor blend in the discourse context very smoothly because students cannot distinguish whether a speech act was generated by AutoTutor or a human tutor.The proposed research will substantially expand the capabilities of AutoTutor by designing the discourse to handle more sophisticated tutoring mechanisms. These mechanisms should further enhance the active construction of knowledge. One enhancement is to get the student to articulate more knowledge, with more formal, symbolic, and precise specification; if the student doesn't say it, it is not considered covered by AutoTutor. Another enhancement is to set up the dialog so that it guides the user in manipulating a 3-dimensional microworld of a physical system; the student attempts to simulate a new state in the physical system by manipulating parameters, inputs, and formulae. The proposed research will develop AutoTutor in the domains of both computer literacy and Newtonian physics, so we will have some foundation for evaluating the generality of AutoTutor's mechanisms. AutoTutor has been designed to be generic, rather than domain-specific; an authoring tool will be developed that makes it easy for instructors to prepare new material on new topics. After the new versions of AutoTutor are completed, we will evaluate its effectiveness on learning gains, conversational smoothness, and pedagogical quality. During the course of achieving these engineering and educational objectives, the proposed project willconduct basic research in cognitive psychology, discourse processes, computer science, andcomputational linguistics. This research cuts across quadrant 2 (behavioral, cognitive, affective, and social aspects of human learning) and quadrant 3 (SMET learning in formal and informal educational settings).

孟菲斯大学的家教研究小组开发了一种计算机家教（称为AutoTutor），它模拟了未完成的人类家教的话语模式和教学策略。学校系统中的典型导师是不成功的，因为导师没有接受过辅导策略的培训，对该主题只有入门到中级的知识。AutoTutor的开发由NSF资助（SBR 9720314，在学习和智能系统计划中）。AutoTutor中的话语模式和教学策略是基于以前的一个项目，该项目剖析了100小时的自然主义辅导课程。AutoTutor目前针对的是大学生的入门计算机素养课程，他们学习硬件，操作系统和互联网的基础知识。AutoTutor不仅仅是一个信息传递系统，而是一个话语假体或协作支架，帮助学生积极构建知识。AutoTutor呈现课程脚本中的问题和难题，尝试理解通过键盘输入的学习者贡献，回答学生的问题，制定对学习者贡献敏感的对话移动（例如简短反馈，泵，提示，断言，更正和提示），并使用说话的头部提供对话移动。说话的头显示情感，产生具有话语敏感语调的合成语音，并指向图形显示器上的实体。AutoTutor有七个模块：课程脚本、语言提取、言语行为分类、潜在语义分析（领域知识的统计表示）、主题选择、对话管理和讲话头。对AutoTutor的评估表明，辅导系统改善了学习，其效果大小与学校系统中的典型人类辅导员相当，但不如成功的人类辅导员和智能辅导系统高。AutoTutor的对话移动融合在话语语境中非常顺利，因为学生无法区分是否AutoTutor或人类tutor.The拟议的研究将大大扩展AutoTutor的能力，通过设计的话语处理更复杂的辅导机制。这些机制应进一步加强知识的积极建设。一个增强是让学生表达更多的知识，更正式，符号和精确的规范;如果学生没有说出来，它不被认为是AutoTutor所涵盖的。另一个改进是设置对话框，以便它指导用户操纵物理系统的三维微观世界;学生试图通过操纵参数，输入和公式来模拟物理系统中的新状态。本研究将有助于发展AutoTutor在电脑素养与牛顿物理领域的应用，因此我们将有一些基础来评估AutoTutor机制的通用性。AutoTutor的设计是通用的，而不是特定领域的;将开发一个创作工具，使教师可以轻松地准备新主题的新材料。在新版本的AutoTutor完成后，我们将评估其在学习收益，会话流畅性和教学质量方面的有效性。在实现这些工程和教育目标的过程中，拟议项目将进行认知心理学、话语过程、计算机科学和计算语言学的基础研究。这项研究跨越象限2（人类学习的行为，认知，情感和社会方面）和象限3（正规和非正规教育环境中的SMET学习）。