SBIR-STTR Award

Personalized Reading Instruction
Award last edited on: 8/12/2016

Sponsored Program
SBIR
Awarding Agency
NSF
Total Award Amount
$1,160,000
Award Phase
2
Solicitation Topic Code
-----

Principal Investigator
Jay Goyal

Company Information

Actively Learn Inc

220 2nd Avenue S
Seattle, WA 98104
   (857) 540-6670
   info@activelylearn.com
   www.activelylearn.com
Location: Single
Congr. District: 07
County: King

Phase I

Contract Number: ----------
Start Date: ----    Completed: ----
Phase I year
2014
Phase I Amount
$150,000
This SBIR Phase I 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 for students working with any text. The broader impact of this Phase I project will be to improve how students read, and therefore how they learn. 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. Difficulty reading extends to all subjects; poor readers have only a 14% chance of success in math and 1% chance of success in science. Reading is the heart of education. It is the most important skill we learn in school. People who read critically have more success in school, obtain high quality jobs, and are able to contribute more to expand social resources. Thirty million students in the U.S. struggle to read, so reducing that number by even 10 or 20 percent would mean a huge improvement in the educational and financial opportunities for millions of Americans. Globally the problem is even larger, and digital devices give us the opportunity to reach over a billion people worldwide. Researchers and educators have been trying to solve the "reading gap" for decades, but only now does the technology exist to make this possible.

Phase II

Contract Number: ----------
Start Date: ----    Completed: ----
Phase II year
2015
(last award dollars: 2017)
Phase II Amount
$1,010,000

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.