Using Natural Language Processing to Inform Science Instruction

使用自然语言处理为科学教学提供信息

• 批准号: 2101669
• 负责人: Marcia Linn
• 金额: $ 224.75万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2101669&HistoricalAwards=false
• 关键词: Using; Natural; Language; Processing; Inform

Often, middle school science classes do not benefit from participation of underrepresented students because of language and cultural barriers. This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. This work continues a partnership among the University of California, Berkeley, Educational Testing Service, and science teachers and paraprofessionals from six middle schools enrolling students from diverse racial, ethnic, and language groups whose cultural experiences may be neglected in science instruction. The partnership will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic. The partnership leverages a web-based platform to implement adaptive guidance designed by teachers that feature dialog and peer interaction. Further, the platform features teacher tools that can detect when a student needs additional help and alert the teacher. Teachers using the technology will be able to track and respond to individual student ideas, especially from students who would not often participate because of language and cultural barriers. This project develops AI-based technology to help science teachers increase their impact on student science learning. The technology is aimed to provide accurate analysis of students' initial ideas and adaptive guidance that gets each student started on reconsidering their ideas and pursuing deeper understanding. Current methods in automated scoring primarily focus on detecting incorrect responses on test questions and estimating the overall knowledge level in a student explanation. This project leverages advances in natural language processing (NLP) to identify the specific ideas in student explanations for open-ended science questions. The investigators will conduct a comprehensive research program that pairs new NLP-based AI methods for analyzing student ideas with adaptive guidance that, in combination, will empower students to use their ideas as starting points for improving science understanding. To evaluate the idea detection process, the researchers will conduct studies that investigate the accuracy and impact of idea detection in classrooms. To evaluate the guidance, the researchers will conduct comparison studies that randomly assign students to conditions to identify the most promising adaptive guidance designs for detected ideas. All materials are customizable using open platform authoring tools. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通常，由于语言和文化障碍，中学科学课程不会受益于代表性不足的学生的参与。该项目利用语言来帮助学生形成自己的想法，并在科学课堂上追求更深入的理解。这项工作继续在加利福尼亚大学，伯克利大学，教育测试服务以及来自六所中学的科学教师和专业人士之间建立了合作伙伴关系，其中包括来自各种种族，种族和语言群体的学生，他们的文化经验可能会被忽略在科学教学中。该合作伙伴关系将开发一项全面的研究计划，以开发和测试技术，该计划将使学生能够利用自己的想法作为加深科学理解的起点。研究人员将使用一种检测学生观念的技术，这些技术超出了学生的一般知识水平，以适应学生对科学主题的文化和语言理解。该合作伙伴关系利用一个基于Web的平台来实现由教师设计的自适应指导，这些指导和同行互动具有功能。此外，该平台具有教师工具，可以检测学生何时需要更多帮助并提醒老师。使用该技术的教师将能够跟踪和回应个别学生的想法，尤其是从不经常因语言和文化障碍而参加的学生。 该项目开发了基于AI的技术，以帮助科学教师增加对学生科学学习的影响。该技术的目的是对学生的最初想法和适应性指导进行准确的分析，使每个学生开始重新考虑自己的想法并追求更深入的理解。自动评分中的当前方法主要集中于检测在测试问题上的错误回答并估算学生解释中的总体知识水平。该项目利用自然语言处理的进步（NLP）来确定学生解释开放式科学问题的特定思想。调查人员将开展一项全面的研究计划，该计划将新的基于NLP的AI方法与自适应指导分析学生的想法，结合使用，该方法将使学生能够利用自己的想法作为提高科学理解的起点。为了评估思想检测过程，研究人员将进行研究，以研究教室中思想检测的准确性和影响。为了评估指导，研究人员将进行比较研究，将学生随机分配给条件，以确定最有希望的自适应指导设计。所有材料均可使用开放平台创作工具进行自定义。 Discovery Research Prek-12计划（DRK-12）试图通过研究和开发创新资源，模型和工具来显着增强PreK-12学生和教师的科学，技术，工程和数学（STEM）的学习和教学。 DRK-12计划中的项目基于STEM教育和先前的研发工作的基础研究，为拟议项目提供了理论和经验的理由。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。

项目成果

Designing an Adaptive Dialogue to Promote Science Understanding

设计适应性对话以促进科学理解

• 发表时间: 2022
• 期刊: Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022
• 作者: Gerard, L.;Bichler, S.;Bradford, A.;Linn, M. C.;Steimel, K.;Riordan, B.
• 通讯作者: Riordan, B.