Expanding Applications of Network Analysis to STEM Education Research

为了更好地支持大学生学习科学，了解他们在学习过程中如何将概念和想法联系起来是很重要的。网络分析(NA)有可能使分析学生学习数据的新方法能够揭示这种联系。该项目旨在通过培养研究人员的技能，将NA的统计技术应用于学生科学学习的实时数据，以服务于国家利益。该项目的首席调查员(PI)将参与专业发展，以磨练她在网络分析方面的技能，并将其应用于分析在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此方法使用的了解，这将使其他科学教育研究人员能够将此技术应用于实时数据，以对学生的知识和学习过程做出新的发现。这个伊利诺伊大学厄巴纳-香槟分校的能力建设项目将发展PI在网络分析方面的分析技能，同时探索如何将此方法应用于更好地理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，但这很少用来自个人的实时数据来完成。这些分析学生知识结构的新方法有可能推进关于哪些概念最核心以及知识网络中各个知识片段的紧密联系等方面的知识。这一研究成果有望为信息加工理论和新兴的多文本多媒体理解理论提供基础性的理解。PI将学习网络分析，并将其应用于从本科生生物学学生那里收集的两个现有的有声思维/眼球跟踪数据集。为了实现这些目标，PI将参加NA课程，并接受具有该学科专业知识的导师和顾问的指导。随后，项目组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。这些应用有望对STEM教育和相关学科(如医学教育、认知心理学和人机交互)的广泛研究人员具有价值。该项目由NSF的STEM教育研究中的EHR核心研究能力建设计划(ECR：BCSER)支持，该计划旨在建设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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.