Developing Auto Tutor for Computer Literacy and Physics

孟菲斯大学的辅导研究小组开发了一种计算机导师(称为AutoTutor)，它可以模拟没有成就的人类导师的话语模式和教学策略。在学校系统中，典型的家教是不称职的，因为家教没有接受过家教策略方面的培训，对这一主题只有初级到中级的知识。AutoTutor的开发是由美国国家科学基金会拨款(学习和智能系统计划中的SBR 9720314)资助的。AutoTutor的话语模式和教学策略是基于之前的一个项目，该项目剖析了100个小时的自然主义辅导课程。目前，AutoTutor的目标客户是计算机基础课程的大学生，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，它还充当了一个话语假体或协作脚手架，帮助学生积极构建知识。AutoTutor从课程脚本中提出问题和问题，试图理解通过键盘输入的学习者的贡献，回答学生的问题，制定对学习者的贡献敏感的对话动作(如简短的反馈、泵、提示、断言、更正和提示)，并使用会说话的头部提供对话动作。说话的头部显示情绪，产生具有话语敏感语调的合成语音，并在图形显示器上指向实体。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、对话管理和对话头部。对AutoTutor的评估表明，该辅导系统改善了学习，其效果规模与学校系统中典型的人类导师相当，但没有优秀的人类导师和智能辅导系统那么高。AutoTutor Blend在语篇语境中的对话动作非常流畅，因为学生无法区分言语行为是由AutoTutor还是人类导师产生的。本研究将通过设计语篇来处理更复杂的辅导机制，从而大大扩展AutoTutor的能力。这些机制应进一步加强知识的主动建构。其中一个改进是让学生清楚地表达更多的知识，更正式、更象征性和更精确的说明；如果学生不说，就不被认为是AutoTutor涵盖的内容。另一项改进是设置对话框以引导用户操纵物理系统的三维微世界；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。所提出的研究将在计算机素养和牛顿物理学领域发展AutoTutor，因此我们将为评估AutoTutor机制的通用性奠定一定的基础。AutoTutor被设计为通用的，而不是特定于领域的；将开发一个创作工具，使教师可以轻松地准备关于新主题的新材料。在新版本的AutoTutor完成后，我们将评估其在学习收益、对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。这项研究跨越了象限2(人类学习的行为、认知、情感和社交方面)和象限3(正规和非正规教育环境中的SMET学习)。

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).