BIGDATA: EAGER: Using Big Data to Investigate Longitudinal Education Outcomes through Visual Analytics

BigData：使用大数据通过可视化分析调查纵向教育成果数据科学技术已经彻底改变了许多学术领域，并在商业领域带来了巨大的收益。到目前为止，它们在解决美国教育系统中的关键问题方面一直没有得到充分利用，特别是在了解科学、技术、工程和数学(STEM)学习和学习环境、扩大STEM的参与以及增加传统上STEM服务不足的学生的留住方面。教育与人力资源局(EHR)通过EHR核心研究计划，为推进大数据科学与工程(BigData)计划的基础和应用的关键技术和技术而制定的目标是推进旨在理解和解决这些关键问题的基础研究，并促进数据科学在教育研究中的使用。这项探索性研究的早期概念助学金将使用来自全国代表性数据集的数据，包括大约35,000名学生，以调查高中的课程选择模式，以及这些模式与大学出勤率、高中和大学成功以及职业选择等关键结果的关系。很少有研究人员试图用这种规模的数据来回答这个问题。因此，这项建议将大大有助于该领域对影响高中和大学成绩的因素的理解。在这些新见解的基础上，将有可能在高中和大学层面创建干预措施，基于有关哪些措施有效改善毕业和劳动力结果的数据。2002年至2012年的教育纵向研究(ELS：2002)和2009年的高中纵向研究(HSLS：2009)是具有代表性的高中生的样本，他们在整个大学期间被跟踪，并进入了他们的早期职业生涯。ELS有15,000名学生，HSLS有21,000名学生。对于ELS，NCES调查了10年级和12年级的学生，然后是大学二年级的学生，然后是大约26岁的学生。NCES收集了高中和大学的成绩单，以及高中和大学的成绩、态度、参与度和人口统计变量。首席调查员将首先使用可视化来确定结果差异的潜在模式，然后使用生长混合建模(GMM)和接收器操作特征分析(ROC)来调查这些模式的统计意义。该奖项由EHR核心研究(ECR)计划支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展

BIGDATA: Using Big Data to Investigate Longitudinal Education Outcomes through Visual AnalyticsData science techniques have revolutionized many academic fields and led to terrific gains in the commercial sector. They have to date been underutilized in solving critical problems in the US educational system, particularly in understanding Science, Technology, Engineering and Mathematics (STEM) learning and learning environments, broadening participation in STEM, and increasing retention for students traditionally underserved in STEM. The goals of the Directorate for Education and Human Resources (EHR), through the EHR Core Research program, for the Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA) program are to advance fundamental research aimed at understanding and solving these critical problems, and to catalyze the use of data science in Education Research. This Early Concept Grant for Exploratory Research (EAGER) will employ data from national representative datasets including approximately 35,000 students to investigate course taking patterns in high school and how these relate to critical outcomes such as college attendance, high school and college success, and career choices. Few investigators have attempted to answer this question with this scale of data. Therefore, this proposal will contribute significantly to the field's understanding of factors that affect success in high school and college. Building on these new insights will enable the potential to create interventions at the high school and college level based on data about what works to improve graduation and workforce outcomes. The Education Longitudinal Study of 2002-2012 (ELS:2002) and High School Longitudinal Study of 2009 (HSLS:2009) are representative samples of high school students who were tracked through college and into their early career. ELS has 15,000 students and HSLS has 21,000 students. For ELS, NCES surveyed students in 10th and 12th grade, then sophomore year of college, and then when they were approximately 26. NCES collected transcripts for high school and college, and high school and college achievement, attitudinal, participation and demographic variables. The Principal Investigator will first use visualizations to identify potential patterns of difference in outcomes and then use growth mixture modeling (GMM) and receiver operating characteristic analysis (ROC) to investigate the statistical significance of those patterns.This award is supported by the EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development