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

大数据：通过可视化分析使用大数据调查纵向教育结果数据科学技术已经彻底改变了许多学术领域，并在商业领域取得了巨大的成就。迄今为止，在解决美国教育体系中的关键问题方面，特别是在理解科学、技术、工程和数学（STEM）的学习和学习环境、扩大STEM的参与以及提高传统上未得到STEM教育的学生的保留率方面，它们没有得到充分的利用。教育和人力资源理事会（EHR）的目标是通过EHR核心研究计划，推进大数据科学与工程（BIGDATA）项目的基础和应用的关键技术和技术，以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应用。这项探索性研究的早期概念资助（EAGER）将使用来自全国代表性数据集的数据，其中包括大约35,000名学生，以调查高中课程的学习模式，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。很少有研究者试图用如此大规模的数据来回答这个问题。因此，这一建议将有助于该领域对影响高中和大学成功因素的理解。在这些新见解的基础上，将有可能在高中和大学层面创建基于数据的干预措施，这些数据可以改善毕业和就业结果。《2002-2012年教育纵向研究》（ELS:2002）和《2009年高中纵向研究》（HSLS:2009）是对从大学到职业生涯早期的高中生进行跟踪调查的代表性样本。ELS有15000名学生，HSLS有21000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，然后是大约26岁的学生。NCES收集了高中和大学的成绩单，高中和大学的成绩，态度，参与和人口统计变量。首席研究员将首先使用可视化来识别结果差异的潜在模式，然后使用生长混合模型（GMM）和受试者工作特征分析（ROC）来调查这些模式的统计显著性。该奖项由EHR核心研究（ECR）项目支持。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM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