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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
Parent DUNS ID:110261711
Program:SBIR Phase II
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.

Project Outcomes Report


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.

Reading is the heart of education, yet two out of three students have trouble understanding 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 a 1% chance of success in science. In grades 4-12, 30 million students struggle to read; reducing that number by even 10 or 20 percent would significantly impact millions of people.

Students can struggle with reading for a myriad of reasons. They often don’t get access to help when they need it and teachers struggle to provide one-on-one support given large class sizes and the time burden of grading assignments. Actively Learn’s SBIR research focused on solving both of these problems.

Using sophisticated technology, Actively Learn pioneered the use of adaptive interventions in digital text to help readers better understand what they read. When students are not comprehending the text, they are provided additional explanations or information to assist their understanding, like having a guide alongside them helping at critical points. These explanations appear within the digital text and are accessible to students as they read. These interventions are not visible to proficient readers to provide an appropriate level of challenge and to differentiate instruction. The adaptive interventions have proven effective, with a statistically significant increase in performance for students who would have otherwise struggled with comprehension.

We know students always benefit from more teacher support and guidance. However, much of teacher time goes into grading, leaving limited time for instructional support. Additionally, student writing is a great way to increase comprehension and make thinking visible, but the grading burden makes it hard to do consistently. Actively Learn developed algorithms to assist with grading by providing teachers recommended grades for short answer responses. These algorithms are demonstrating unique promise, as they have a high degree of accuracy, require a smaller corpus of responses for training compared to other solutions, and can be used with short answers instead of only longer-form writing.

Students and teachers are already benefiting from the technology created with the SBIR grant. Actively Learn will continue its efforts to measure its impact and improve its technologies to provide more support to students and teachers. As we learn more, we hope to add to educators’ knowledge base for how to best assist students with their reading.


Last Modified: 03/27/2019
Modified by: Jay Goyal
