IES Blog

Institute of Education Sciences

The ED/IES SBIR 2021 Year in Review and a Look Ahead to 2022

The Department of Education’s Small Business Innovation Research Program (SBIR), administered by IES, provides awards for the research and development of new, commercially viable education technology products. Known as ED/IES SBIR, the program’s goal is to grow a portfolio of scalable, research-based products that address pressing needs across topic areas in education and special education.

From an education technology perspective, 2021 will surely be remembered as the “year after” the onset of the global pandemic—where demand for effective education tools and platforms skyrocketed and developers pivoted to meet the needs of the return to in-person and hybrid learning environments. Dozens of ED/IES SBIR-developers contributed to these efforts, with millions of students and educators using their products to support remote and in-person learning in 2021. This blog shares some highlights from the ED/IES SBIR program in 2021 and provides a preview of its recently released 2022 solicitations.

The ED Games Expo

IES hosted the 8th annual ED Games Expo virtually in June 2021 to provide resources to the public in response to pandemic-related challenges. As part of the virtual Expo, 170 IES- and government-supported education technology products were available at no cost to educators and students around the country. The Expo also presented 35 virtual events for the public that have been viewed more than 10,000 times on YouTube, highlighted by a Kick Off Show introduced by Secretary of Education Miguel Cardona and including master classes for educators and behind-the-sciences “how to” events for students. Dates for the next ED Games Expo will be announced soon.

New ED/IES SBIR Awards

ED/IES SBIR announced 29 new 2021 awards, including 18 for prototype development and 11 for full-scale education technology product development. The awards continue trends from recent years.

One exciting trend is the employment of advanced technologies such as artificial intelligence, machine learning, natural language processing, or algorithms to personalize student learning. Examples include projects by Myriad Sensors (Pocket Lab) to develop an AI engine to assess and provide feedback to students while doing physical science experiments, Analytic Measures Inc. (AMI) to create an natural language processing engine to recommend personalized practice activities based on a student’s level of oral reading fluency, and by KooApps and Kings Peak Technology to use machine learning to provide immediate vocabulary support to English learners.

Another trend in 2021 is the development of new products to scale existing IES-funded research. Projects that build on prior IES research include: Nimble Hiring to develop a platform to improve school district hiring and educator retention, xSEL Labs to create a platform for social and behavioral learning innovations, and Emberex to create a user interface with reporting and recommendation features to meet modern standards for a reading assessment.

ED/IES SBIR also continues to support projects in new areas. For example, three new projects are developing music-based technologies to support learning (Muzology, Edify, and Lyrics to Learn).

Highlights From Individual Projects in the Portfolio

Many ED/IES SBIR-supported companies enjoyed newsworthy successes in 2021.

ED/IES SBIR Releases Two 2022 Program Solicitations

On December 1, 2021, ED/IES SBIR released two new solicitations. Phase I solicitation #91990022R0001 is a request for proposals for $250,000 awards for 8 months for the research, development, and evaluation of new prototypes of education and special education technology products. Direct to Phase II solicitation #91990022R0002 is a request for proposals for $1,000,000 for 2 years for R&D and evaluation to develop new technology to prepare existing researcher-developed evidence-based innovations (products, interventions, practices) for use at scale, and to plan for commercialization. The goal is to support the successful transfer of research to practice at scale in education and special education. Proposals for both solicitations are due February 1, 2022.

Stay tuned for updates in 2022 on Twitter and Facebook as IES continues to support innovative forms of technology.


Edward Metz is a research scientist and the program manager for the Small Business Innovation Research Program at the US Department of Education’s Institute of Education Sciences. Please contact Edward.Metz@ed.gov with questions or for more information.

 

AI-Augmented Learning for Individuals with Disabilities: New Funding Opportunity, Current Research, and the Potential for Improving Student Outcomes

This March, IES Director Mark Schneider released a blog in which he discussed exploring a partnership with the National Science Foundation (NSF) to encourage scientists with expertise in AI and related fields to address the important post-pandemic need for accelerating learning. IES is now excited to announce our resulting participation in NSF’s National Artificial Intelligence (AI) Research Institutes—Accelerating Research, Transforming Society, and Growing the American Workforce solicitation. In this blog, we describe this new funding opportunity, provide examples of existing NCSER-funded research in this area, and highlight the potential for such research to further improve outcomes for learners with disabilities.

 Artificial Intelligence Research Funding Opportunity

With funding from the American Rescue Plan, NCSER plans to support research under Theme 6, Track B: AI-Augmented Learning for Individuals with Disabilities. Proposals must discuss how the work will respond to the needs of learners with or at risk for a disability in an area where the COVID-19 pandemic has further widened existing gaps and/or resulted in decreased access and opportunities for students with disabilities to learn and receive support services. Please review the solicitation, the webinar (November 16), and the frequently asked questions for more information. Interested applicants should note the primary focus of this institute:

The primary focus of an institute in AI-Augmented Learning includes research and development of AI-driven innovations to radically improve human learning and education. Achievement and opportunity gaps, particularly for learners from disadvantaged or underserved communities, have always been present, but COVID-19 has exacerbated them. Institute plans for this theme should address and measure outcomes with direct education impact, in both the short- and long- term, that have practical significance to educators, parents, or other decision-makers. Plans must also directly address algorithmic bias, model transparency, security and data privacy in the support of learning.”

Current NCSER-Funded Grants Applying Artificial Intelligence and Machine Learning

Prior to the new collaboration between IES and NSF, NCSER funded several grants that apply artificial intelligence and machine learning approaches, including those described below.

With a 2018 NCSER grant, Dr. Maithilee Kunda and her team at Vanderbilt University are building on a technology-based intervention known as Betty’s Brain. This computer-based instructional program for middle school science, designed with the support of a 2006 NCER grant, allows students to teach a computer agent to understand certain concepts, increasing their own knowledge and understanding. Dr. Kunda and her team are developing a new game called Film Detective, which is designed to improve theory of mind (ToM) reasoning in adolescents with autism spectrum disorder (ASD). More information about this project can be found in this IES blog.

With a 2021 NCSER grant, Dr. Patrick Kennedy and his team at University of Oregon are using machine learning to validate a well-known assessment, Dynamic Indicators of Basic Early Literacy Skills, 8th Edition® (DIBELS 8) as a screener for dyslexia. As of 2020, 47 states require that students be screened for dyslexia in early elementary school and many states use DIBELS for this screening. However, it remains to be validated for this purpose. To address the validity of the DIBELS for screening, this research team is using machine learning approaches to predict and classify scores in relation to a pre-defined target. This will allow the research team to draw conclusions about the validity of the DIBELS 8 for dyslexia screening. These conclusions will be disseminated widely to state and local education agencies and other stakeholders.

The Potential of AI for Improving Outcomes for Learners with Disabilities

In addition to the work that IES is funding, AI has already demonstrated potential for improving outcomes for learners with disabilities in many other ways:

  • AI has been used to support children with ASD who have difficulties understanding people’s emotions, with AI-driven apps and robots helping students practice emotion recognition and other social skills.
  • AI has informed the development of algorithms that can help those involved in assessment identify disabilities in students, such as ASD, specific learning disabilities (dyslexia, dysgraphia, and dyscalculia), and attention-deficit/hyperactivity disorder (ADHD).
  • AI-embedded interventions have included error analysis to inform instruction and personalized feedback in spelling and math for students with disabilities.

Despite these advancements, there appear to be persistent gaps in AI research for students with disabilities, such as AI for students with intellectual and developmental disabilities. This is an especially important area of work because many of these learners have multiple disabilities and/or serious health conditions. For example, children with intellectual and developmental disabilities who also have hearing loss or visual impairment have compounded challenges. Some students with Down syndrome also have hearing loss and other health complications, such as cardiac issues. AI affords an opportunity to integrate health information across different applications to improve the quality of life for these students. These technological solutions can assist in managing information about the students and communicating health information between teachers, physicians, and caregivers.

AI has the potential to transform special education. We hope that this NCSER-NSF partnership will encourage researchers to be creative in planning projects that move the field of AI forward as well as provide innovative solutions to support learners with disabilities.

This blog was co-authored by Sarah Brasiel (Sarah.Brasiel@ed.gov), program officer at NCSER and Bennett Lunn (Bennett.lunn@ed.gov), Truman-Albright Fellow for NCSER and the National Center for Education Research (NCER). IES encourages special education researchers to partners with experts in Artificial Intelligence to submit to this NSF AI Institute solicitation 22-502 to increase the evidence base on use of AI for this population.

Film Detective: How an AI-powered Game Aims to Improve Outcomes for Students with ASD

Artificial intelligence (AI) is poised to revolutionize the way humans live, even in ways yet unseen, and education is no exception. IES funds research at the cutting edge of technology and education science, and, as Director Mark Schneider has recently pointed out, AI may eventually serve to help educators identify, assess, and support students with disabilities. In 2018, NCSER awarded funding to Dr. Maithilee Kunda of Vanderbilt University to do just that.

Dr. Kunda and her team are developing a new game called Film Detective to improve theory of mind (ToM) reasoning in adolescents with autism spectrum disorder (ASD). ToM reasoning is the ability to infer the mental state of others, allowing us to understand and predict behavior based on our perception of their beliefs, intentions, and desires. The game builds on a technology-based intervention known as Betty’s Brain. Developed with support from a NCER grant, Betty’s Brain is a computer-based instructional program for middle school science that allows students to teach a computer agent to understand certain concepts. By teaching the agent, students grew their own knowledge and understanding. Dr. Kunda and her team are building on this software by adapting the learning-by-teaching model to improve ToM reasoning in neurodiverse students. (For more on Dr. Kunda’s perspective on the importance of neurodiversity, see this blog.)

The Film Detective game takes students through an interactive storyline in which they must help a scientist from the year 3021 “decode” the way people in today’s world behave in a series of films. The stakes are high as students help a scientist unlock a time machine by retrieving codes hidden in films by an evil scientist—aptly named Von Klepto—who has stolen items from the Museum of Human History. By teaching the computer agent—the player’s robot sidekick (named T.O.M.)—how to identify modern behaviors, the student develops their own ToM reasoning. The Film Detective storyline is a product of the creative talents of several Vanderbilt creative writing students, and the game mechanics were designed with insights of college students with ASD themselves. With the help of post-doctoral student and project lead, Roxanne Rashedi, the project team has used participatory design and qualitative methods to better tailor the game to the community for which it is intended. By working closely with students with ASD and their families, the project team was able to refine the original Betty’s Brain structure with new reward structures and storylines that balance the challenge of the game with the frustration that students can feel playing the game.

Screen shot of the Film Detective’s theatre and time machine room
Film Detective’s Theatre and Time Machine Room (illustration by Kayla Stark)

Every part of the project draws on the diverse expertise of the team, and the inclusion of a variety of perspectives has been crucial to informing the project’s development. The team includes experts from Vanderbilt’s School of Engineering and the Vanderbilt Medical Center’s Treatment and Research Institute for Autism Spectrum Disorders (TRIAD), with Dr. Kunda and students in computer science and psychology providing insights in cognitive science and artificial intelligence. The joining of expertise in artificial intelligence, clinical psychology, and educational psychology has allowed the team to merge theoretical perspectives on ToM development with conceptions of knowledge representation and modeling in computational systems. This approach offers the team a unique framework for understanding the development of social reasoning skills in students with ASD. Beyond the theoretical, the team has also leveraged artificial intelligence to evaluate how students progress through the game, using advanced data mining techniques and eye-tracking-enabled user studies to better understand how students with ASD can develop greater ToM reasoning through learning-by-teaching.

Film Detective’s hallway to concessions
Film Detective’s Hallway to Concessions (illustration by Kayla Stark)

The work that has gone into Film Detective exemplifies the ways that novel research that combines technological advancement and diverse perspectives can lead to important innovations in the education sciences. While Film Detective is still under development (it is currently being user tested, and readers are encouraged to sign up to take part here), IES is eager to see what will come out of this exciting collaboration.

Dr. Maithilee Kunda is the director of the Laboratory for Artificial Intelligence and Visual Analogical Systems and a faculty investigator for the Frist Center for Autism and Innovation at Vanderbilt University. This blog was written and edited by Bennett Lunn, Truman-Albright Fellow for the National Center for Education Research and the National Center for Special Education Research.

Building a Community around Digital Learning Platforms

Last month, we were excited to announce grants within the Digital Learning Platforms Network, which includes five platform teams and a network lead. The purpose of this network is to leverage existing, widely used digital learning platforms for rigorous education research. This network is part of IES’s investments in innovation within education research and development and is funded through the Research Networks Focused on Critical Problems of Policy and Practice grant program. That program is designed to focus resources and attention on critical education issues faced by our nation as well as create infrastructure and process to bring together researchers who are working on similar issues. A major focus of the network—and why we chose a network approach—is bringing together educators, researchers, and platform developers to figure out how to leverage the potential of platforms for research insights. IES hopes that a major contribution from this network will be building that community of stakeholders and creating resources that reflect best practices for doing this kind of work. 

With that goal in mind, Digital Promise Global, the network lead, will host an event on October 22 at 3pm Eastern Time with introductory remarks from IES Director Mark Schneider. At the event, each of the five platform teams will briefly share the purpose of their project, and you can learn more about the network’s planned activities. You will also learn where you can go to find out more about the work that the network will pursue and to receive updates on their progress.

To join the event, please RSVP here: https://www.eventbrite.com/e/seernet-launch-webinar-tickets-186961746617 


For more information or questions about the Digital Learning Platforms Network, please contact Erin Higgins (Erin.Higgins@ed.gov), Program Officer at the National Center for Education Research.

Students’ Internet Access Before and During the Coronavirus Pandemic by Household Socioeconomic Status

The pandemic has focused attention on the resources needed for students to engage equitably in educational opportunities, particularly during remote learning. While access to computers and the internet were important to education prior to the pandemic—as tools for word processing, research, and communication after school hours, or even as the primary means of schooling—they became essential tools for students to remain engaged during the 2020–21 academic year. Reflecting this importance both before and during the pandemic, recent NCES blogs have highlighted data on virtual schools and geographic differences in digital access. This blog presents additional insight on these topics from the Condition of Education 2021. Specifically, it highlights patterns of inequity in access to educational technology by socioeconomic status, both before and during the coronavirus pandemic.

Before the Coronavirus Pandemic

According to the American Community Survey (ACS),1 the higher the level of parental educational attainment, the higher the percentage of 3- to 18-year-olds with home internet access in 2019. For instance, the percentage with home internet access was highest for those whose parents had attained a bachelor’s or higher degree (99 percent) and lowest for those whose parents had less than a high school credential (83 percent) (figure 1).

Similarly, the higher the level of family income, the higher the percentage of 3- to 18-year-olds with home internet access in 2019. Specifically, the percentage with home internet access was highest for those in families in the highest income quarter (99 percent) and lowest for those in families in the lowest income quarter (89 percent) (figure 1).2


Figure 1. Percentage of 3- to 18-year-olds with home internet access and home internet access only through a smartphone, by parental education and family income quarter: 2019

1 Includes those who completed high school through equivalency credentials, such as the GED.
NOTE: Includes only 3- to 18-year-olds living in households (respondents living in group quarters such as shelters, healthcare facilities, or correctional facilities were not asked about internet access). Includes 3- to 18-year-olds who had home internet access only through a smartphone but did not have any of the following types of computers: desktop or laptop, tablet or other portable wireless computer, or “some other type of computer.” Although rounded numbers are displayed, the figures are based on unrounded data.
SOURCE: U.S. Department of Commerce, Census Bureau, American Community Survey (ACS), 2019. See Digest of Education Statistics 2020, table 702.12.


While internet access is nearly universal in the United States (95 percent of all 3- to 18-year-olds had access in 2019), not all families access the internet the same way. Specifically, 88 percent had access through a computer,3 and 6 percent relied on a smartphone for their home internet access.4,5

In 2019, the higher the level of parental educational attainment, the lower the percentage of 3- to 18-year-olds who relied on a smartphone for their home internet access. Similarly, the higher the level of family income, the lower the percentage of 3- to 18-year-olds who relied on a smartphone for their home internet access. For instance, the percentage who relied on a smartphone for their home internet access was lowest for those in families in the highest income quarter (1 percent) and highest for those in families in the lowest income quarter (14 percent) (figure 1).

Taken together with the patterns for overall home internet access, these findings reveal that access only through a smartphone is generally more common for groups with lower rates of internet access overall. Importantly, although smartphones can be useful tools for staying connected, they offer more limited functionality for applications such as word processing or interactive learning platforms. In other words, overall levels of internet access mask further inequities in mode of access, which have implications for whether/how the internet can be used as an educational tool.

During the Coronavirus Pandemic

As students moved en masse to online learning during the pandemic, access to internet-connected devices became a requirement for students to participate effectively in their new learning environments. The pre-pandemic data described above suggest that not all students would have been in a position to take advantage of these remote classrooms, and that this would be true of a higher percentage of students whose parents had lower incomes or lower levels of educational attainment.  

Some schools and districts helped students meet these needs by providing computers or paying for home internet access. Data from the Household Pulse Survey (HPS) show that 59 percent of adults6 with children in the home enrolled in school7 reported that computers were provided by their school or district. This percentage was generally higher for those with lower 2019 household incomes, ranging from 68 percent for adults with household incomes below $25,000 to 50 percent for adults with household incomes over $150,000 (figure 2). A similar pattern was observed for internet access. Overall, 4 percent of adults said internet access was paid for by their students’ district or school, ranging from 8 percent for adults in the lowest household income range to about 1 percent for those in the highest household income range. These patterns are consistent with higher rates of assistance going to families with higher rates of expected need (as indicated in figure 1).


Figure 2. Among adults 18 years old and over who had children under 18 in the home enrolled in school, percentage reporting that computers and internet access were always or usually available and provided or paid for by schools or school districts, by income level: September 2 to 14, 2020

NOTE: Although rounded numbers are displayed, the figures are based on unrounded data. Data in this figure are considered experimental and do not meet NCES standards for response rates. The survey question refers to enrollment at any time during the 2020–21 school year.
SOURCE: U.S. Department of Commerce, Bureau of the Census, Household Pulse Survey, collection period of September 2 to 14, 2020. See Digest of Education Statistics 2020, tables 218.85 and 218.90.


Even with this assistance from schools and districts, however, socioeconomic inequalities in students’ access to computers and internet were not eliminated. In general, the percentage of adults who reported that these resources were always or usually available increased with household income. For example, in September 2020, the percentage of adults reporting that computers were always or usually available was highest for the two household income levels at or above $100,000 and lowest for the two household income levels below $50,000. Similarly, the percentage of adults reporting that internet access was always or usually available was higher for the three household income levels at or above $75,000 than for the three household income levels below $75,000.

Both before and during the pandemic, these data show that overall access to education technology in the United States is high. This access is bolstered by widespread access to mobile devices like smartphones and—at least during the 2020–21 academic year—by resources provided by students’ schools and districts, particularly for students from lower socioecnomic backgrounds. Nevertheless, inequalities persist. As the prevalence of technology in education grows, it will be important to continue to track equity not only in access but also in quality of access and frequency and competency of use.

Explore the following resources to learn more about students’ access to, use of, and competency with education technology.

General

Access

  • Condition of Education 2021

Use

Competency

 

By Véronique Irwin, NCES


[1] The American Community Survey (ACS) provides a large monthly sample of demographic, socioeconomic, and housing data comparable in content to the Long Forms of the Decennial Census. Aggregated over time, these data serve as a replacement for the Long Form of the Decennial Census. This section of the blog post uses data from ACS to describe the percentage of 3- to 18-year-olds with home internet access and the percentage with home internet access only through a smartphone in 2019.

[2] The highest quarter refers to the top 25 percent of all family incomes; the middle-high quarter refers to the 51st through the 75th percentile of all family incomes; the middle-low quarter refers to the 26th through the 50th percentile of all family incomes; and the lowest quarter refers to the bottom 25 percent of all family incomes.

[3] Refers to the percentage of 3- to 18-year-olds with home internet access through one or more of the following types of computers: desktop or laptop, tablet or other portable wireless computer, or “some other type of computer.” Includes homes having both smartphones and any of these types of computers.

[4] Refers to the percentage of 3- to 18-year-olds who had home internet access only through a smartphone but did not have any of the types of computers listed in endnote 3.

[5] Detail does not sum to totals because of rounding.

[6] The Household Pulse Survey, conducted by the Census Bureau and other agencies including NCES, gathers information from adults about household educational activities (as well as other topics). Because the data focus on adults, findings from HPS are not directly comparable to those from ACS mentioned above.

[7] According to HPS data, 52 million adults had children under age 18 in the home enrolled in school in September 2020. Overall, two-thirds (67 percent) of these adults reported that classes for their children had moved to a distance learning format using online resources.