Expanding Applications of Network Analysis to STEM Education Research

将网络分析的应用扩展到 STEM 教育研究

• 批准号: 2225298
• 负责人: Jennifer Cromley
• 金额: $ 16.9万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225298&HistoricalAwards=false
• 关键词: Expanding; Applications; Network; Analysis; STEM

To better support college students in learning about science, it is important to understand how they make connections among the concepts and ideas as they are learning. Network Analysis (NA) has potential to enable new ways of analyzing student learning data to reveal such connections. This project seeks to serve the national interest by developing a researcher’s skills to apply the statistical techniques of NA to real-time data about students’ science learning. The principal investigator (PI) of the project will participate in professional development to hone her skills in Network Analysis and apply them to analyze data collected while college students learn about the immune system. The PI and other members of the project team will subsequently share what they learn about the use of this approach via workshops and publications, which will enable other science education researchers to apply the techniques to real-time data to make new discoveries about students’ knowledge and learning processes.This capacity-building project at University of Illinois at Urbana-Champaign will develop the PI’s analytical skills in Network Analysis while exploring how this approach can be applied to better understand undergraduate students’ science learning. Statistical techniques of NA can be applied to student knowledge structures, but this has rarely been done with real-time data from individuals. These novel approaches to analyzing students’ knowledge structures have potential to advance knowledge regarding, for example, which concepts are most central and how tightly linked individual pieces of knowledge are within a knowledge network. The research findings are expected to contribute fundamental understandings to both Information Processing Theory and an emerging theories of multi-text multimedia comprehension. The PI will learn about Network Analysis and apply it to two extant think-aloud/eye tracking datasets collected from undergraduate biology students. To accomplish these objectives, the PI will take a course in NA and receive coaching from mentors and advisors with expertise in the subject. Subsequently, the project team will design and implement two workshops on novel applications of Network Analysis to real-time data. These applications are expected to be valuable to a wide range of researchers in STEM education and related disciplines such as medical education, cognitive psychology, and human-computer interaction. The project is supported by NSF's EHR Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research.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.

为了更好地支持大学生学习科学，重要的是要了解他们如何在学习过程中将概念和想法联系起来。网络分析（NA）有可能使分析学生学习数据的新方法，以揭示这种联系。该项目旨在通过发展研究人员的技能来服务于国家利益，将NA的统计技术应用于学生科学学习的实时数据。该项目的主要研究者（PI）将参与专业发展，以磨练她在网络分析方面的技能，并将其应用于分析大学生学习免疫系统时收集的数据。PI和项目团队的其他成员随后将通过研讨会和出版物分享他们对使用这种方法的了解，这将使其他科学教育研究人员能够将这些技术应用于实时数据，从而对学生的知识和学习过程进行新的发现。伊利诺伊大学厄巴纳分校的这一能力建设项目-尚潘将开发PI的网络分析的分析技能，同时探索如何将这种方法应用于更好地了解本科生的科学学习。NA的统计技术可以应用于学生的知识结构，但这很少与个人的实时数据。这些分析学生知识结构的新方法有可能促进知识的发展，例如，哪些概念是最核心的，以及知识网络中各个知识片段的联系有多紧密。研究结果有望为信息处理理论和新兴的多文本多媒体理解理论提供基本理解。PI将学习网络分析，并将其应用于从本科生物学学生收集的两个现存的有声思维/眼动跟踪数据集。为了实现这些目标，PI将参加NA课程，并接受具有该主题专业知识的导师和顾问的辅导。随后，项目团队将设计和实施两个关于网络分析对实时数据的新应用的研讨会。预计这些应用程序将对STEM教育和相关学科（如医学教育，认知心理学和人机交互）的广泛研究人员有价值。该项目由NSF的EHR Core Research Building Capacity in STEM Education Research（ECR：BCSER）项目提供支持，该项目旨在培养研究人员开展高质量STEM教育研究的能力。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。