AutoMentor: Virtual Mentoring and Assessment in Computer Games for STEM Learning

该项目建立在现有研究的基础上，并将其扩展为包括社区实践参与的计算模型。这些产品包括一个自动指导系统，该系统使用自然语言对话来帮助学生学习科学和技术，以及一个评估/分析协议来量化学生的STEM行为。它通过自动化指导技术AutoMentor扩展了自动化辅导，并将其与认知网络分析相结合。AutoMentor是在多人城市规划游戏《Land Science》中实现的，并由马萨诸塞州奥杜邦协会（Massachusetts Audubon Society）对大约700名中学生进行了课外或校内项目的测试。来自三个合作机构的开创性工作为该系统的设计提供了信息。这些包括智能辅导（格雷瑟，孟菲斯大学），评估（Mislevy，马里兰大学）和基于游戏的学习（谢弗，威斯康星大学麦迪逊分校）。核心技术由计算机科学、科学内容、测量和STEM教育编程方面的专业知识支持。通过提供一个完全可实施的系统，支持学生通过沉浸在STEM过程中学习实质性内容，该项目有可能改变所有三个核心领域。pi预计这将成为未来学习系统的组成部分。

The project builds on existing research and extends it to include a computational model of participation in a community of practice. The products include an automated mentoring system that uses natural language conversations to help students learn about science and technology and an assessment/analysis protocol to quantify students' STEM behavior. It extends automated tutoring with an automated mentoring technology, AutoMentor, and combines it with Epistemic Network Analysis. AutoMentor is implemented in Land Science, a multi-player urban planning game, and tested by the Massachusetts Audubon Society with approximately 700 middle school aged students either in after-school or in-school programs. Pioneering work drawn from three partner institutions inform the design of the system. These include intelligent tutoring (Graesser, University of Memphis), assessment (Mislevy, University of Maryland), and game-based learning (Shaffer, University of Wisconsin-Madison). The core technologies are supported by expertise in computer science, science content, measurement, and STEM educational programming. The project has the potential to transform all three core areas by providing a fully implementable system that supports student learning substantive content through immersion in a STEM processes. The PIs anticipate that this will be integral to learning systems of the future.