EAGER: BIGDATA: SMART Data - Academic Success Made Affordable, Rapid, and Timely through Integrated Data Analytics

通过集成数据分析实现经济、快速和及时的学术成功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，它们在解决美国教育系统中的关键问题方面没有得到充分利用，特别是在理解科学，技术，工程和数学（STEM）学习和学习环境，扩大STEM的参与，以及提高传统上STEM服务不足的学生的保留率方面。教育和人力资源理事会通过推进大数据科学工程基础和应用的关键技术和技术（BIGDATA）计划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应用。探索性研究早期概念补助金（EAGER）将寻求了解数据处理和分析系统的背景和骨干，这些系统可以使用学院和大学已经拥有的标准类型的数据提供见解（例如，从他们的学习管理系统，行政数据系统，和建议系统），为学生和教师提供预测和建议，以增加在大学取得成功并按时毕业的学生的百分比。这对于提高两年制和四年制大学的毕业率具有巨大的潜力，可以降低家庭和学生的大学费用。此外，它还具有极大的潜力，可以增加STEM领域中代表性不足的少数民族的吸引力和保留力，因为它将确定所有学生成功按时毕业的障碍。团队的愿景是最终建立一个数据平台，整合关键数据源，并提供利用这些来源的重要见解的工具。数据来源的类型是：1）本科生在STEM专业的学术轨迹的纵向数据; 2）学生活动的学习管理系统（LMS）记录，以及3）来自各种来源的文本数据，如顾问。这些工具将包括1）学生，教师和管理员的GUI，以查看学生的学术途径和2）学生，教师和顾问之间的沟通门户。该系统将帮助学生，教师和administers从事数据驱动的决策（D3 M）围绕学术途径，以成功和按时从大学毕业。探索性研究早期概念补助金（EAGER）将解决实现这一愿景所必须面临的许多挑战，并以各种方式为相关社区提供解决这些挑战的方案。 该团队为这个早期概念项目制定了四个目标。首先是检查现有的数据和预测模型，以了解学生的成功和保留，并建立一个应用程序和数据类型的目录，可用于围绕这些结果的途径进行决策。第二个目标是描述设计规范，包括构建数据平台所需的数据类型、算法和机器学习技术。第三是对本科生、教师和管理人员的D3 M培训计划的关键要素进行试点研究。最终目标是开发一个设计框架，从根本上建立道德，隐私和安全。所有产品都将以出版物、课程或软件的形式在GitHub等公共论坛上公开共享。

Academic Success Made Affordable, Rapid, and Timely through Integrated Data 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 through 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 seek to understand the background and backbone of systems of data processing and analytics that can provide insights using standard types of data that colleges and universities already have (for example, from their Learning Management Systems, administrative data systems, and advising systems) to provide predictions and recommendations to students and instructors to increase the percentage of students who succeed in college and graduate on time. This has terrific potential to increase graduation rates at two- and four-year institutions, which can lower college costs for families and students. In addition, it has terrific potential to increase the attraction and retention of underrepresented minorities in STEM fields, as it will identify barriers for all students to graduating successfully and on time.The team vision is to eventually build a data platform that integrates key data sources and provides tools that leverage important insights from these sources. The types of data sources are: 1) longitudinal data from undergraduate students on their academic trajectories in STEM majors; 2) Learning management system (LMS) records of student activity, and 3) text data from a variety of sources, such as advisors. The tools will include a 1) GUI for students, teachers and administrators to see student academic pathways and 2) a portal for communication between students, faculty and advisors. This system will help students, teachers and admistrators engage in Data Driven Decision Making (D3M) around academic pathways to successful and on time graduation from college. This Early Concept Grant for Exploratory Research (EAGER) will solve many challenges that must be undertaken to achieve this vision and provide the solutions to those challenges to relevant communities in a variety of ways. The team has four goals for this early concept project. The first is to examine existing data and predictive models for understanding student success and retention and build a catalog of applications and data types available for decision making around pathways to these outcomes. The second goal is to describe design specifications, including data types, algorithms, and machine learning techniques that are needed to build the data platform. The third is to pilot research on critical elements of a D3M training program for undergraduate students, instructors and administrators. The final goal is to develop a design framework that builds ethics, privacy and security in from the ground up. All products will be made available as publications, curricula, or software shared openly on common forums such as GitHub.