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).

孟菲斯大学的辅导研究小组开发了一名计算机老师（称为自动者），该研究人员模拟了未完成的人类导师的话语模式和教学策略。学校系统中的典型导师无法解决，因为教师没有接受辅导策略的培训，并且只对该主题具有简介与中等知识。自动公司的开发由NSF赠款（SBR 9720314，在学习和智能系统计划中）资助。自动人士的话语模式和教学策略是基于一个先前的项目，该项目剖析了100个小时的自然主义辅导会议。目前，Autotutor针对的是入门计算机识字课程的大学生，他们学习了硬件，操作系统和互联网的基础。自动者不仅是信息传递系统，还可以作为一个话语假体或协作脚手架，可以帮助学生积极建立知识。 Autotutor提出了课程脚本中的问题和问题，试图理解由键盘输入的学习者贡献，回答学生问题，提出对学习者贡献敏感的对话动作（例如，短反馈，泵，提示，提示，断言，校正和提示）以及交付对话框的说话。会说话的头部表现出情感，以话语敏感的语调产生综合语音，并指向图形显示上的实体。 AutoTutor有七个模块：课程脚本，语言提取，语音ACT分类，潜在的语义分析（域知识的统计表示），主题选择，对话管理和谈话头。对自动机构的评估表明，辅导系统可以改善学习效果大小，可与学校系统中的典型人辅导员相提并论，但不如完成的人类导师和智能辅导系统那么高。在话语中，自动融合的对话移动非常顺利，因为学生无法区分自动化的语音法或人类导师是否产生了言语行为。拟议​​的研究将通过设计论述来处理更为复杂的辅导机制，从而大大扩大自动者的能力。这些机制应进一步增强知识的积极构建。一种增强是通过更正式，象征和精确的规范来使​​学生表达更多知识。如果学生不这样说，则不会被自动介绍。另一个增强功能是设置对话框，以便指导用户操纵物理系统的3维微世界。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。拟议的研究将在计算机素养和牛顿物理学领域开发自动机，因此我们将为评估自动机制的通用性提供一些基础。自动公司被设计为通用，而不是特定于域的;将开发一个创作工具，使教师可以轻松地准备有关新主题的新材料。在完成新版本的自动机器后，我们将评估其在学习收益，对话平稳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，拟议的项目将在认知心理学，话语过程，计算机科学和计算语言学方面进行基础研究。这项研究削减了象限2（人类学习的行为，认知，情感和社会方面）和象限3（在正式和非正式的教育环境中的SMET学习）。