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: 1661987
• 负责人: Ryan Baker
• 金额: $ 22.5万
• 依托单位: University of Pennsylvania
• 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=1661987&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。它将提高东北部教育大数据参与者分析学校，学生和管理人员数据的能力，并改善教学和学习。然而，随着更精确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中的几个重大挑战：1）预测未来的学生事件，例如，大学出勤率，大学专业，从现有的大规模纵向教育数据集，涉及同样的数千名学生。2)帮助教师理解密集的在线数据，以影响他们的教学，例如，他们应该说什么或做什么来回应学生的活动。3)利用代表学生技能和行为的大数据，推断学生在学习中的认知、动机和元认知因素，为每个学生提供个性化指导。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：为课堂教师创建可操作的建议;对学生进行有效且成功的预测;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类型的数据，以更好地了解学生、团体和他们学习的环境。计算机已经在教室里使用了几十年，但教育工作者还没有找到最有效的使用方法。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然使用50年前可用的测量方法。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和学习的特征。这个项目有可能接触到数百万学生（在学习过程中），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）和十几个其他组织（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（Baker，Hongnan）的研究人员;自适应教育技术和计算机科学（Woolf，Arroyo）;以及学习科学（Arroyo，Hongnan）。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的人（Baker），数据科学教材的开发人员（Arroyo，Baker），以及其他开发在线辅导系统的人，这些系统在学习中取得了显着的学生成功（例如，Hongnan，Arroyo，Woolf）.

项目成果

Analysis of click-stream data to predict stem careers from student usage of an intelligent tutoring system

分析点击流数据以预测学生使用智能辅导系统的职业生涯

• DOI: 10.5281/zenodo.4008050
• 发表时间: 2020
• 期刊: Journal of educational data mining
• 作者: Makhlouf, J.;Mine, T.
• 通讯作者: Mine, T.

ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface.

ASSISTments 纵向数据挖掘竞赛特刊：前言。

• DOI: 10.5281/zenodo.4008048
• 发表时间: 2020
• 期刊: Journal of educational data mining
• 作者: Patikorn, T.;Baker, R. S.;& Heffernan, N. T.
• 通讯作者: & Heffernan, N. T.

ASSISTments Longitudinal Data Mining Competition 2017: A Preface

2017 年 ASSISTments 纵向数据挖掘竞赛：前言

• 发表时间: 2018
• 期刊: International Conference on Educational Data Mining
• 作者: Patikorn, T.;Heffernan, N.T.;Baker, R.S.
• 通讯作者: Baker, R.S.

Towards Interpretable Automated Machine Learning for STEM Career Prediction

迈向 STEM 职业预测的可解释自动化机器学习

• DOI: 10.5281/zenodo.4008073
• 发表时间: 2020
• 期刊: Journal of educational data mining
• 作者: Liu, R.;Tan, A.
• 通讯作者: Tan, A.