
NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
Recipient: |
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Initial Amendment Date: | August 4, 2016 |
Latest Amendment Date: | August 21, 2017 |
Award Number: | 1636782 |
Award Instrument: | Standard Grant |
Program Manager: |
Martin Halbert
OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2016 |
End Date: | August 31, 2020 (Estimated) |
Total Intended Award Amount: | $429,110.00 |
Total Awarded Amount to Date: | $514,931.00 |
Funds Obligated to Date: |
FY 2017 = $85,821.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
100 INSTITUTE RD WORCESTER MA US 01609-2280 (508)831-5000 |
Sponsor Congressional District: |
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Primary Place of Performance: |
100 Institute Rd Worcester MA US 01609-2247 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
BD Spokes -Big Data Regional I, Information Technology Researc, ECR-EDU Core Research |
Primary Program Source: |
04001617DB NSF Education & Human Resource |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
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).
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The Big Data for Education Spoke of the Big Data Northeast Innovation Hub developed the community of researchers and practitioners working in this area in the Northeastern United States, and nationwide. We conducted competitions around large-scale educational data sets, one data set involving student use of educational software in middle school and their eventual career outcomes, and the other involving standardized test data. Hundreds of researchers participated in these competitions, building the field while yielding scientific findings published in workshops and a journal special issue. For example, competitors developed new findings on how disengagement in middle school predicts career choice over a decade later, and how interaction patterns early in test-taking are associated with eventually working too quickly later in the test.
We conducted workshops and tutorials in several cities (New York City, Boston, Philadelphia, Pittsburgh, Buffalo, Worcester, Amherst), attended by hundreds of teachers, educational software developers, and scientific researchers. The spoke also supported a massive online open course, Big Data and Education. Over 10,000 learners participated in this course during the life of the spoke, and it continues to be available for free going forward.
The data-driven approaches to education this hub supported pair deep knowledge of human learning and cognition with adaptive curricula and responses, enabling instructional resources to extend far beyond traditional, “one-size-fits-all” methods of teaching. Mastering tools for educational data science will, in the long term, help increase productivity, develop new insights into human behavioral patterns, transform existing educational practices, and spawn new industries.
Last Modified: 10/20/2020
Modified by: Neil T Heffernan
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