EXP: Linguistic Analysis and a Hybrid Human-Automatic Coach for Improving Math Identity

EXP：语言分析和提高数学认同的人机混合教练

• 批准号: 1739012
• 负责人: Jaclyn Ocumpaugh
• 金额: $ 54万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739012&HistoricalAwards=false
• 关键词: EXP; Linguistic; Analysis; Hybrid; Human

The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project addresses the effect of students' social identity on learning, an important factor in math and science education. Specifically, it will advance the scientific understanding of math identity (i.e. "I'm (not) a math person") by studying the over 100,000 diverse students who use Reasoning Mind, a blended learning system for K-8 mathematics with demonstrated results. Reasoning Mind supports math identity with an innovative design that allows students to email an animated character (aka the Genie) and receive a human-crafted response. This study will show how math identity manifests and changes during students' use of Reasoning Mind in order to inform software designers and classroom teachers on best practices for encouraging math identity. This will have the broader impact of strengthening our nation's ability to supply science and technology fields with a well-trained workforce.This study examines two components of math identity: self-efficacy and interest in mathematics. Both self-efficacy and interest can be enhanced by curricula that are individualized to appropriately challenge each student (a strength of educational technology), but social stereotypes (i.e. "Girls aren't good in math") may decrease math identity or otherwise interfere with its development. This study investigates how educational technology can reverse these trends. In the first phase of this study, a combination of survey methods, Natural Language Processing (NLP), and Educational Data Mining (EDM) techniques will be used to identify how poor and/or changing math identity emerges in the linguistic patterns of student interaction with GenieMail (as well as in other parts of Reasoning Mind). These findings will then be used to enhance GenieMail and other instructional interactions with the Reasoning Mind system by creating a hybrid human/AI system, with the goal of improving math identity across diverse populations of students.

网络学习和未来学习技术计划为支持设想学习技术的未来并推进我们对人们如何在技术丰富的环境中学习的了解的努力提供资金。网络学习探索（EXP）项目通过设计和构建新型学习技术，研究其促进学习的可能性和有效使用它们的挑战，探索新型学习技术的可行性。这个项目解决了学生的社会认同对学习的影响，这是数学和科学教育的一个重要因素。具体来说，它将通过研究超过100，000名使用Reasoning Mind的不同学生来推进对数学身份的科学理解（即“我（不是）一个数学人”），Reasoning Mind是K-8数学的混合学习系统，并取得了良好的效果。Reasoning Mind通过创新的设计支持数学身份，允许学生通过电子邮件发送动画角色（又名精灵）并收到人工制作的回复。本研究将展示学生在使用推理思维时数学身份是如何体现和变化的，以便为软件设计人员和课堂教师提供鼓励数学身份的最佳实践。这将产生更广泛的影响，加强我们国家的能力，提供科学和技术领域与训练有素的劳动力。本研究探讨了两个组成部分的数学身份：自我效能感和兴趣的数学。自我效能和兴趣都可以通过个性化的课程来提高，以适当地挑战每个学生（教育技术的优势），但社会陈规定型观念（即“女孩不擅长数学”）可能会降低数学身份或以其他方式干扰其发展。本研究探讨了教育技术如何扭转这些趋势。在本研究的第一阶段，调查方法，自然语言处理（NLP）和教育数据挖掘（EDM）技术的组合将被用来确定如何穷人和/或不断变化的数学身份出现在语言模式的学生与GenieMail的互动（以及在其他部分的推理头脑）。这些发现将用于通过创建一个混合的人类/人工智能系统来增强GenieMail和其他与推理思维系统的教学交互，目标是提高不同学生群体的数学认同。