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Research Spending & Results

Award Detail

Doing Business As Name:University of Memphis
  • Vasile Rus
  • (901) 678-5259
  • Philip I Pavlik Jr.
  • Stephen E Fancsali
  • Dale Bowman
  • Deepak Venugopal
Award Date:09/15/2019
Estimated Total Award Amount: $ 2,584,309
Funds Obligated to Date: $ 1,050,000
  • FY 2019=$1,050,000
Start Date:09/01/2019
End Date:08/31/2021
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:The Learner Data Institute: Harnessing The Data Revolution To Make The Learning Ecosystem More Effective, Efficient, and Engaging
Federal Award ID Number:1934745
DUNS ID:055688857
Parent DUNS ID:878135631
Program:HDR-Harnessing the Data Revolu
Program Officer:
  • Finbarr Sloane
  • (703) 292-8465

Awardee Location

Street:Administration 315
Awardee Cong. District:09

Primary Place of Performance

Organization Name:The University of Memphis
Cong. District:09

Abstract at Time of Award

The project will lay the foundation for a Learner Data Institute, to further understanding of how people learn, how to improve adaptive instructional systems, and how to create learning systems that are more effective, efficient, engaging, and affordable. An interdisciplinary team of people from academia, industry, and government will work together to improve the effectiveness of the learning ecosystem for the benefit of its hundreds of thousands of students, prepare teachers for the future learning ecosystem, and contribute to accelerating discovery and transforming the education ecosystem through the use of big data and cloud computing. The project will impact a number of communities including learning sciences, data science, artificial intelligence in education, assessment, educational data mining, and machine learning. The outcomes will be widely disseminated through training materials, workshops, tutorials, and a course for researchers and practitioners. The two-year conceptualization phase of the Learner Data Institute will focus on building a strong community of researchers, define research priorities, and develop interdisciplinary prototype solutions that address critical student learning, cyber-learning, and learning engineering challenges. Based on modern theories of learning and recent advances in educational technologies, artificial intelligence, sensing technologies, signal processing, and data science, the team will explore new frontiers in multi-faceted (cognitive, motivational, emotional, etc.) learner data collection, analysis, and visualization in order to understand and possibly transform how learners learn with technology. The multi-disciplinary team will address core research questions such as: (1) how to transform a widely distributed group of interdisciplinary researchers, developers, and practitioners into a community of practice that can fully exploit the data revolution for the benefit of the learners and educational stakeholders; (2) how adaptive instructional systems (AISs) and data science can be used as a research vehicle to further understanding of how learners learn; (3) how the human-technology partnership with data and data science can be used to improve learners? and teachers? ability to employ technology in ways that facilitate learning and improve the effectiveness, scalability, and affordability of AISs in order to maximize the potential of learning ecologies of the future; and (5) more generally, how to extend the frontiers of data science to include: new methods of data collection and design; more interpretable, knowledge-rich machine learning methods (e.g., by combining Deep Learning with Markov Logic); scalable new inference and learning algorithms symmetries and joint dependencies in the data; and methods for identifying causal mechanisms from unstructured, semi-structured, and structured data. While the project will address core educational tasks in the context of online and blended learning environments, the proposed data science methods and models are generally applicable to other instructional contexts as well as other science and engineering areas. All models, software, processes, and data used in the project will be documented and disseminated for everyone to use and build on the outcomes of the project. This project is part of the National Science Foundation's Harnessing the Data Revolution Big Idea activity. The effort is jointly funded by the Directorate for Education and Human Resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Publications Produced as a Result of this Research

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Al-Farabi, K.M. and Sarkhel, S. and Dey, S. and Venugopal, D. "Fine-Grained Explanations Using Markov Logic" Fine-Grained Explanations Using Markov LogicMachine Learning and Knowledge Discovery in Databases, v.11907, 2019, p.. Citation details  

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