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Research Spending & Results

Award Detail

Doing Business As Name:Actively Learn Inc.
  • Jay Goyal
  • (857) 540-6670
Award Date:08/21/2015
Estimated Total Award Amount: $ 750,000
Funds Obligated to Date: $ 1,010,000
  • FY 2015=$750,000
  • FY 2017=$260,000
Start Date:09/01/2015
End Date:02/28/2019
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase II: Personalized Reading Instruction
Federal Award ID Number:1534790
DUNS ID:078850803
Program Officer:
  • Rajesh Mehta
  • (703) 292-2174

Awardee Location

Street:220 2nd Ave S
Awardee Cong. District:07

Primary Place of Performance

Organization Name:Actively Learn Inc.
Cong. District:07

Abstract at Time of Award

This SBIR Phase II project proposes to discover digital methods to personalize reading instruction such that students understand more when they read, retain knowledge, and build lasting skills. The academic research on reading supports the claim that active reading strategies that incorporate quality instruction can benefit students. However, instruction is usually not personalized to meet the needs of specific students, and even when an educator works 1:1 with a student they can only interpret a limited number of signals from a student to help guide instruction. The objective of the project is to take in several inputs when students read digitally and investigate whether personalized reading instruction can effectively be created and delivered such that students get extra help when they struggle and are challenged when they can succeed on their own. Two-thirds of students in the U.S. are struggling readers; they cannot understand the main idea when they read. These students are four times more likely to drop out of school. People who read critically have more success in school, obtain high quality jobs, and are able to contribute more to expand social resources. Researchers and educators have been trying to solve the "reading gap" for decades, but only now does the technology exist to make this possible. This SBIR Phase II project proposes to use unique machine learning techniques to personalize reading instruction. The algorithms to personalize instruction will ensure that extra help, or scaffolding, is allocated to the students who need it, and removed when they no longer need it or when it threatens to become a crutch. This approach is different than other machine learning algorithms, which are built to minimize the overall error or maximize the overall reward. However, what is required for personalized reading instruction is different. The algorithm must learn how much to help a student not so they perform better with help, but so they perform better without it because the goal is for students to become better readers in the long term, not become reliant on scaffolding to read. The objective of the research is to fully develop and commercialize this personalized reading system and will involve data science, application development, and content authoring.

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