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.

网络学习和未来的学习技术计划资助了支持设想学习技术未来的工作，并促进我们对人们在技术丰富的环境中学习的了解。网络学习探索（EXP）项目通过设计和构建新型的学习技术并研究其促进学习和有效使用它们的挑战的可能性，探索新型学习技术的生存能力。该项目解决了学生社会身份对学习的影响，这是数学和科学教育的重要因素。具体来说，它将通过研究使用推理思维的100,000多名不同的学生，这是一种用于K-8数学的混合学习系统，以证明其结果，这将提高对数学身份的科学理解（即“我（不是）数学人员”）。推理思维通过创新的设计支持数学身份，该设计使学生可以通过电子邮件发送动画角色（又称精灵）并获得人工响应。这项研究将展示数学身份如何在学生使用推理思维期间表现和变化，以告知软件设计师和课堂老师的最佳实践，以鼓励数学身份。这将产生更广泛的影响，即增强国家提供科学和技术领域的能力，以训练有素的劳动力。这项研究研究了数学身份的两个组成部分：自我效能和对数学的兴趣。可以通过个性化的课程来提高自我效能感和兴趣，这些课程是为了适当地挑战每个学生（教育技术的力量），但是社会刻板印象（即“女孩在数学上不好”）可能会降低数学身份或以其他方式干扰其发展。这项研究调查了教育技术如何扭转这些趋势。在本研究的第一阶段中，将使用调查方法，自然语言处理（NLP）和教育数据挖掘（EDM）技术的组合来确定在与天生邮件（以及在推理思维的其他部分中）与学生互动的语言互动方式中差和/或不断变化的数学身份如何出现。然后，这些发现将用于通过创建混合人类/AI系统来增强与推理思维系统的其他教学互动，以改善各种学生人群的数学身份。