BD Spokes: Spoke: NORTHEAST: Collaborative: Grand Challenges for Data-Driven Education

BD 发言人： 发言人：东北：协作：数据驱动教育的巨大挑战

• 批准号: 1636851
• 负责人: Ryan Baker
• 金额: $ 22.5万
• 依托单位: Teachers College, Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2016-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636851&HistoricalAwards=false
• 关键词: BD; Spokes; Spoke; NORTHEAST; Collaborative

This project will support teachers, administrators and researchers to collaborate around online education resources and big data. It will increase the capacity of participants in Educational Big Data in the Northeast to analyze data from schools, students and administrators and to improve teaching and learning. However, as more refined data comes from online instructional systems and the use of data mining techniques, participants will learn to search for patterns and associations and to draw conclusions about student knowledge, performance and behavior. This research addresses several grand challenges in education: 1) Predict future student events, e.g., college attendance, college major, from existing large-scale longitudinal educational data sets involving the same thousands of students. 2) Help teachers to make sense of dense online data to influence their teaching, e.g., what should they say or do in response to student activity. 3) Provide personal instruction to each student based on using big data that represents student skills and behavior and infers students' cognitive, motivational, and metacognitive factors in learning. The project will improve the capacity in data-driven education by sharing educational databases, managing yearly data competitions, and conducting educational data science workshops and hackathons. Measurable results include studying gigabytes of data to: create actionable recommendations for classroom teachers; make effective and successful predictions about students; develop new AI methods for education; and create new data science tool sets. Key outcomes include introducing many researchers to educational big data, learning analytics and models of teaching interventions. The team intends to improve classroom learning and leverage the unique types of data available from digital education to better understand students, groups and the settings in which they learn.Computers have been in classrooms for decades and yet educators have not identified the most effective ways of using them. Despite advances in evaluation methods to measure human learning, most researchers still use measures available 50 years ago. This project will leverage and extend state-of-the-art big data bases and technologies to measure online learning, especially features of student engagement and learning associated with improved student outcome. This project has the potential to reach millions of students (while learning), hundreds of researchers while measuring human learning (from education, cognitive science, learning sciences, psychology, and computer science) and a dozen other organizations (publishers, testing organizations, non-profit organizations, teachers, parents, and stakeholders). The team brings together a unique blend of researchers from data science (Baker, Heffernan); adaptive education technology and computer science (Woolf, Arroyo); and learning sciences (Arroyo, Heffernan). It includes women and minorities (Woolf, Arroyo), people who helped develop the largest educational database in the world (Baker), developers of data science teaching materials (Arroyo, Baker), and others who have developed online tutoring systems that achieve significant student success in learning (e.g., Heffernan, Arroyo, Woolf).

该项目将支持教师，管理人员和研究人员围绕在线教育资源和大数据进行协作。它将提高东北地区教育大数据的参与者的能力，以分析学校，学生和管理人员的数据，并改善教学和学习。但是，随着更多精致的数据来自在线教学系统和数据挖掘技术的使用，参与者将学习搜索模式和协会，并得出有关学生知识，表现和行为的结论。这项研究解决了教育中的几个巨大挑战：1）从现有的大规模纵向教育数据集中预测未来的学生活动，例如大学出勤，大学专业，涉及数千名学生。 2）帮助教师理解密集的在线数据，以影响他们的教学，例如，他们应对学生的活动发表些什么。 3）根据使用代表学生技能和行为的大数据为每个学生提供个人指导，并渗透到学习中学生的认知，动机和元认知因素。该项目将通过共享教育数据库，管理年度数据竞赛以及进行教育数据科学研讨会和黑客马拉松来提高数据驱动教育的能力。可衡量的结果包括研究千兆字节的数据：为课堂教师创建可行的建议；对学生做出有效和成功的预测；开发新的AI教育方法；并创建新的数据科学工具集。关键结果包括向许多研究人员介绍教育大数据，学习分析和教学干预模型。该团队打算改善课堂学习并利用从数字教育中获得的独特数据类型，以更好地了解学生，团体和他们学习的设置。computers已在课堂上已经数十年了，但教育工作者尚未确定使用它们的最有效方法。尽管评估方法取得了进步来衡量人类学习，但大多数研究人员仍在使用50年前可用的措施。该项目将利用并扩展最先进的大数据库和技术来衡量在线学习，尤其是与改善学生成绩相关的学生参与和学习的特征。该项目有可能吸引数百万学生（同时学习），数百名研究人员，同时衡量人类学习（从教育，认知科学，学习科学，心理学和计算机科学）以及其他十几个组织（出版商，测试组织，非营利组织，非营利组织，教师，父母，父母和利益相关者）。该团队汇集了来自数据科学的研究人员（贝克，赫弗南）的独特融合；自适应教育技术与计算机科学（Arroyo Woolf）；和学习科学（Arroyo，Heffernan）。它包括妇女和少数民族（Woolf，Arroyo），帮助开发世界上最大的教育数据库（Baker），数据科学教学材料的开发商（Arroyo，Baker）以及其他开发在线辅导系统，从而取得了巨大的学生学习成功（例如Heffernan，Heffernan，Arroyo，Woolf）。