Using Natural Language Processing to Inform Science Instruction

通常，由于语言和文化障碍，中学科学课不能从代表性不足的学生的参与中受益。这个项目利用语言帮助学生在科学课堂上形成自己的想法，追求更深层次的理解。这项工作延续了加州大学伯克利分校、教育考试服务中心和来自六所中学的科学教师和辅助专业人员之间的合作关系，招收来自不同种族、民族和语言群体的学生，这些学生的文化经历可能在科学教学中被忽视。该伙伴关系将开展一个全面的研究项目，开发和测试技术，使学生能够将自己的想法作为深化科学理解的起点。研究人员将使用一种技术来检测超出学生一般知识水平的学生想法，以适应学生对科学主题的文化和语言理解。该伙伴关系利用一个基于网络的平台来实施由教师设计的适应性指导，其特点是对话和同伴互动。此外，该平台还提供教师工具，可以检测学生何时需要额外帮助并提醒老师。使用该技术的教师将能够跟踪和回应个别学生的想法，特别是那些由于语言和文化障碍而不经常参与的学生。该项目开发基于人工智能的技术，以帮助科学教师提高他们对学生科学学习的影响。该技术旨在对学生最初的想法进行准确分析，并提供适应性指导，让每个学生开始重新考虑自己的想法，并寻求更深入的理解。目前的自动评分方法主要集中在检测测试问题的错误回答和估计学生解释的整体知识水平。该项目利用自然语言处理（NLP）的进步来识别学生对开放式科学问题的解释中的具体想法。研究人员将开展一项全面的研究计划，将新的基于nlp的人工智能方法与适应性指导相结合，用于分析学生的想法，结合起来，将使学生能够将他们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，调查课堂上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，将学生随机分配到条件中，以确定对检测到的想法最有希望的适应性指导设计。所有材料都可以使用开放平台创作工具进行定制。探索研究PreK-12项目（DRK-12）旨在通过研究和开发创新资源、模型和工具，显著提高PreK-12学生和教师对科学、技术、工程和数学（STEM）的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发工作的基础上，为拟议的项目提供了理论和实证依据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.