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

AutoMentor：STEM 学习计算机游戏中的虚拟指导和评估

• 批准号: 0918409
• 负责人: David Shaffer
• 金额: $ 350万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0918409&HistoricalAwards=false
• 关键词: AutoMentor; Virtual; Mentoring; Assessment; Computer

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.

该项目建立在现有研究的基础上，并将其扩展到包括参与实践社区的计算模型。 这些产品包括使用自然语言对话帮助学生学习科学和技术的自动指导系统，以及量化学生STEM行为的评估/分析协议。 它通过自动辅导技术AutoMentor扩展了自动辅导，并将其与认知网络分析相结合。 AutoMentor是在土地科学中实现的，土地科学是一个多人的城市规划游戏，并由马萨诸塞州奥杜邦协会测试，大约有700名中学生参加了课后或校内课程。 该系统的设计借鉴了三个伙伴机构的开创性工作。 这些方法包括智能辅导（孟菲斯大学的Graesser）、评估（马里兰州大学的Mislevy）和基于游戏的学习（威斯康星大学麦迪逊分校的Shaffer）。 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提供支持。 该项目有可能通过提供一个完全可实施的系统来改变所有三个核心领域，该系统支持学生通过沉浸在STEM过程中学习实质性内容。 PI预计这将是未来学习系统的组成部分。