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: 1636782
• 负责人: Ivon Arroyo
• 金额: $ 42.91万
• 依托单位: Worcester Polytechnic Institute
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636782&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参与者分析学校、学生和管理人员数据并改善教学的能力。然而，随着更精细的数据来自在线教学系统和数据挖掘技术的使用，参与者将学会搜索模式和关联，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领域的几个重大挑战：1）根据涉及数千名学生的现有大规模纵向教育数据集来预测未来的学生事件，例如大学出勤率、大学专业。 2) 帮助教师理解密集的在线数据以影响他们的教学，例如，他们应该对学生的活动说什么或做什么。 3）利用代表学生技能和行为的大数据为每个学生提供个性化指导，并推断学生学习中的认知、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举办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，以：为课堂教师提出可行的建议；对学生做出有效且成功的预测；开发新的人工智能教育方法；并创建新的数据科学工具集。主要成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，并利用数字教育提供的独特数据类型来更好地了解学生、群体和他们学习的环境。计算机已经在课堂上使用了几十年，但教育工作者尚未找到使用它们的最有效方法。尽管衡量人类学习的评估方法取得了进步，但大多数研究人员仍在使用 50 年前可用的衡量标准。该项目将利用和扩展最先进的大数据库和技术来衡量在线学习，特别是与提高学生成绩相关的学生参与度和学习特征。该项目有可能惠及数百万学生（学习时）、数百名研究人员（同时测量人类学习（来自教育、认知科学、学习科学、心理学和计算机科学））以及十几个其他组织（出版商、测试组织、非营利组织、教师、家长和利益相关者）。该团队汇集了来自数据科学领域的独特研究人员（Baker、Heffernan）；适应性教育技术和计算机科学（伍尔夫、阿罗约）；和学习科学（Arroyo，Heffernan）。其中包括女性和少数族裔（伍尔夫、阿罗约）、帮助开发世界上最大的教育数据库的人（贝克）、数据科学教材的开发者（阿罗约、贝克）以及其他开发了在线辅导系统、使学生在学习上取得重大成功的人（例如赫弗南、阿罗约、伍尔夫）。

项目成果

The automated grading of student open responses in mathematics

学生数学开放式回答的自动评分

• DOI: 10.1145/3375462.3375523
• 发表时间: 2020
• 期刊: Tenth International Conference on Learning Analytics & Knowledge
• 作者: Erickson, John A.;Botelho, Anthony F.;McAteer, Steven;Varatharaj, Ashvini;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.

Effectiveness of Crowd-Sourcing On-Demand Assistance from Teachers in Online Learning Platforms

在线学习平台中教师众包按需协助的有效性

• DOI: 10.1145/3386527.3405912
• 发表时间: 2020
• 期刊: Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S
• 作者: Patikorn, Thanaporn;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.