Award Abstract # 1443068
CIF21 DIBBs: Building a Scalable Infrastructure for Data-Driven Discovery and Innovation in Education

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: CARNEGIE MELLON UNIVERSITY
Initial Amendment Date: August 21, 2014
Latest Amendment Date: June 2, 2020
Award Number: 1443068
Award Instrument: Standard Grant
Program Manager: Amy Walton
awalton@nsf.gov
 (703)292-4538
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: January 1, 2015
End Date: December 31, 2020 (Estimated)
Total Intended Award Amount: $4,830,819.00
Total Awarded Amount to Date: $5,736,819.00
Funds Obligated to Date: FY 2014 = $4,830,819.00
FY 2016 = $450,000.00

FY 2017 = $8,000.00

FY 2018 = $16,000.00

FY 2019 = $416,000.00

FY 2020 = $16,000.00
History of Investigator:
  • Ken Koedinger (Principal Investigator)
    Koedinger@cmu.edu
  • Carolyn Rose (Co-Principal Investigator)
  • John Stamper (Co-Principal Investigator)
Recipient Sponsored Research Office: Carnegie-Mellon University
5000 FORBES AVE
PITTSBURGH
PA  US  15213-3890
(412)268-8746
Sponsor Congressional District: 12
Primary Place of Performance: Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh
PA  US  15213-3890
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): U3NKNFLNQ613
Parent UEI: U3NKNFLNQ613
NSF Program(s): STEM + Computing (STEM+C) Part,
Project & Program Evaluation,
REAL,
Data Cyberinfrastructure
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
01001819DB NSF RESEARCH & RELATED ACTIVIT

04001415DB NSF Education & Human Resource

01001718DB NSF RESEARCH & RELATED ACTIVIT

01002021DB NSF RESEARCH & RELATED ACTIVIT

04001617DB NSF Education & Human Resource

01001415DB NSF RESEARCH & RELATED ACTIVIT

01001920DB NSF RESEARCH & RELATED ACTIVIT

04001920DB NSF Education & Human Resource
Program Reference Code(s): 9251, 7433, 8048, 7726
Program Element Code(s): 005Y00, 726100, 762500, 772600
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project is creating a community software infrastructure, called LearnSphere, that supports sharing, analysis, and collaboration across the wide variety of educational data. LearnSphere supports researchers as they improve their understanding of human learning. It also helps course developers and instructors improve teaching and learning through data-driven course redesign. The goal is to transform learning science and engineering through a large, distributed data infrastructure, and develop the capacity for course developers, instructors, and learning engineers to make use of it.

LearnSphere maintains a central store of metadata about what datasets exist, but also has distributed features allowing contributors control over access to their own data. It provides a hub to link many communities of educational researchers, provides a repository for researchers to store their data, and provides an open analytic method library and workflow-authoring environment for researchers to build models and run them across datasets.

The research team has extensive experience not only in using educational data mining to make discoveries and improve student outcomes, but also in the creation of educational data infrastructures. They have developed the DataShop infrastructure, which is currently the largest open repository of educational technology data including over 550 datasets. A newer data infrastructure, MOOCdb, is being developed to store and analyze Massively Open Online Course (MOOC) data. The Open Learning Initiative has produced data stored in DataShop for many years and is expanding into the MOOC space. Dialogue-based tutoring systems and student affect sensors are producing new kinds of data that are being added to LearnSphere. The researchers are further improving data collection infrastructure in MOOCs especially by adding platform components for massive multi-factor online experiments. The project is also creating new methods for data integration, discourse data storage and analytics, and new algorithms for automated discovery, as well as new learning science discoveries that result from these algorithms.

By integrating these building blocks in LearnSphere, the project will facilitate cross-modality and cross-domain educational data analysis that is not possible today.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Hu, X., Cai, Z., Hampton, A.J., Cockroft, J.L., Graesser, A.C., Copland, C., Folsom-Kovarik, J.T. "Capturing AIS Behavior using xAPI-like Statements" In Proceedings of Lecture Notes in Computer Science (HCII 2019) , 2019 , p.204
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Shi, G., Pavlik Jr., P. I., & Graesser, A. "Using an Additive Factor Model and Performance Factor Analysis to Assess Learning Gains in a Tutoring System to Help Adults with Reading Difficulties" Proceedings for the 10th International Conference on Educational Data Mining , 2017
Shiyan Jiang, Kexin Yang, Chandrakumari Suvarna, Pooja Casula, Mingtong Zhang and Carolyn Rose "Applying Rhetorical Structure Theory to Student Essays for Providing Automated Writing Feedback" Proceedings of Discourse Relation Parsing and Treebanking (DISRPT) , 2019
Sinatra, A., Graesser, A.C., Hu,X., Brawner. K., & Rus, V. "Design Recommendations for Intelligent Tutoring Systems:" Self-improving systems ( , v.7 , 2019
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