IES Blog

Institute of Education Sciences

Google Acquires Intellectual Property for IES-Supported Education Technology Products Moby.Read and SkillCheck

On April 1, 2022, Google acquired the intellectual property (IP) rights for Moby.Read and SkillCheck, education technology products developed through IES programs by California-based Analytics Measures, Inc. (AMI). AMI will continue as a small business and is honoring school contracts that use Moby.Read and SkillCheck until 2024.

Moby.Read is a technology-delivered, fully automated, oral reading fluency (ORF) assessment that is self-administered by grade school students. As students read a passage aloud into a tablet, the speech-recognition software generates an assessment of ORF in real time through natural language processing software that analyzes text passages of the read-aloud performances. SkillCheck is a component of Moby.Read that employs natural language processing software to analyze recordings and produce interactive report pages that rate and illustrate the student's basic reading skills.

 

 

The technologies were developed over two decades with IES funding. Beginning in 2002, AMI designed several early prototypes to be used for ORFs as a part of the National Assessment of Educational Progress and other national assessments administered by IES’s National Center for Education Statistics. In 2016 and 2017, the IES Small Business Innovation Research program (ED/IES SBIR) funded AMI to develop and test Moby.Read to be used in schools at scale. With 2020 and 2021 ED/IES SBIR awards, AMI developed the SkillCheck as an additional component of Moby.Read to provide educators activities to inform instruction. AMI conducted research at key points over 20 years to validate the results of the assessment.

Since commercial launch in 2019, the Moby.Read and SkillCheck have been used for more than 30,000 student assessments in 30 states. Google acquired the Moby.Read and SkillCheck IP with plans to incorporate these tools into Google suite of products for education.

For additional information on the research, development, and commercialization of these technologies, see this Success Story on the ED/IES SBIR website.


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.

 

Calculating the Costs of School Internet Access

This blog is part of a guest series by the Cost Analysis in Practice (CAP) project team to discuss practical details regarding cost studies.

Internet access has become an indispensable element of many education and social programs. However, researchers conducting cost analyses of education programs often don’t capture these costs due to lack of publicly available information on what school districts pay for internet service. EducationSuperHighway, a nonprofit organization, now collects information about the internet bandwidth and monthly internet costs for each school district in the United States. The information is published on the Connect K-12 website. While Connect K-12 provides a median cost per Mbps in schools nationwide, its applicability in cost analyses is limited. This is because the per student cost varies vastly depending on the school district size.

As customers, we often save money by buying groceries in bulk. One of the reasons that larger sizes offer better value is that the ingredient we consume is sometimes only a small part of the total cost of the whole product; the rest of the cost goes into the process that makes the product accessible, such as packaging, transportation, and rent.

Same thing with internet. To make internet available in schools, necessary facilities and equipment include, but are not limited to web servers, ethernet cables, and Wi-Fi routers. Large school districts, which are often in urban locations, usually pay much less per student than small districts, which are often in rural areas. Costs of infrastructural adaptations need to be considered when new equipment and facilities are required for high-speed internet delivery. Fiber-optic and satellite internet services have high infrastructural costs. While old-fashioned DSL internet uses existing phone lines and thus has less overhead cost, it's much slower, often making it difficult to meet the current Federal Communications Commission recommended bandwidth of 1 Mbps per student.

In short, there is no one-price-for-all when it comes to costs of school internet access. To tackle this challenge, we used the data available on Connect K-12 for districts in each of the 50 U.S. states to calculate some useful metrics for cost analyses. First, we categorized the districts with internet access according to MDR’s definition of small, medium, and large school districts (Small: 0-2,499 students; Medium: 2,500-9,999 students; Large: 10,000+ students). For each category, we calculated the following metrics which are shown in Table 1:

  1. median cost per student per year
  2. median cost per student per hour

 

Table 1: Internet Access Costs

District size

(# of students)

Median mbps per student per month

Median cost per mbps per month

Median cost per student per month

Cost per student per year

Cost per student per hour

Small (0-2,499)

1.40

$1.75

$2.45

$29.40

$0.02

Medium (2,500-9,999)

0.89

$0.95

$0.85

$10.15

$0.007

Large (10,000+)

0.83

$0.61

$0.50

$6.03

$0.004

National median

1.23

$1.36

$1.67

$20.07

$0.014

 

Note: Cost per student per hour is computed based on the assumption that schools open for 1,440 hours (36 weeks) per annum, e.g., for a small district the cost per student per hour is $29.40/1,440 = $0.02). See methods here.

 

Here’s an example of how you might determine an appropriate portion of the costs to attribute to a specific program or practice:  

Sunnyvale School is in a school district of 4,000 students. It offers an afterschool program in the library in which 25 students work online with remote math tutors. The program runs for 1.5 hours per day on 4 days per week for 36 weeks. Internet costs would be:

 

1.5 hours x 4 days x 36 weeks x 25 students x $0.007 = $37.80.

 

The cost per student per hour might seem tiny. Take New York City Public Schools, for example, the cost per Mbps per month is $0.13, and yet the district pays $26,000 each month for internet. For one education program or intervention, internet costs may sometimes represent only a small fraction of the overall costs and may hardly seem worth estimating in comparison to personnel salaries and fringe benefits. However, it is critical for a rigorous cost analysis study to identify all the resources needed to implement a program.


Yuan Chang is a research assistant in the Department of Education Policy & Social Analysis at Teachers College, Columbia University and a researcher on the CAP Project.

 Anna Kushner is a doctoral student in the Department of Education Policy & Social Analysis at Teachers College, Columbia University and a researcher for the CAP Project.

A Conversation About Educational Inequality With Outstanding Predoctoral Fellow Marissa Thompson

Each year, the Institute of Education Sciences (IES) recognizes an outstanding fellow from its Predoctoral Interdisciplinary Research Training Programs in the Education Sciences for academic accomplishments and contributions to education research. The 2021 awardee, Marissa Thompson, completed her PhD at Stanford University and worked as a postdoctoral fellow with the Education Policy Initiative at University of Michigan’s Ford School of Public Policy. This summer, she joins Columbia University as an assistant professor of sociology. Her work focuses on the relationship between education and socioeconomic and racial inequality over the course of life.

Recently, we caught up with Dr. Thompson and asked her to discuss her research on educational inequality and her experiences as a scholar.

How did you become interested in a career in education research?

For a long time, I thought that I wanted to become an engineering professor. I majored in chemical and biomolecular engineering in college and planned to pursue a doctoral degree in engineering after I graduated. Though I was excited about my undergraduate research projects, I was also passionate about diversity and inclusion in science, technology, engineering, and mathematics (STEM) fields. This led me to spend my free time in college working on programs within the School of Engineering that promoted more equitable access to these majors. At the same time, I began taking some courses outside of the engineering program, which led me to a series of introductory sociology electives and inspired me to think about a career in the social sciences.

My interests in educational inequality stemmed in part from my own experiences and challenges as a Black woman in the sciences, but also from the experiences of my classmates who had to overcome barriers to access these fields. I wanted to have a more direct impact on the policies and programs that help to mitigate racial and socioeconomic inequality in education, which led me to apply for graduate programs in sociology of education.

What inspired you to focus your research on understanding the role of education in shaping inequality?

I began my graduate studies with the goal of focusing more narrowly on access and persistence in STEM fields, but this quickly developed into a broader interest in educational inequality. I was fortunate to work on several projects with advisors and mentors that motivated my interests in educational inequality over the life course—from studying racial and socioeconomic achievement gaps in public school districts across the country to studying how processes of major choice can lead to increased gender segregation across fields. My work seeks to understand how a variety of sources—including structural inequality, policy changes, and individual preferences—are related to disparities in access to quality educational experiences. My goal as a researcher is to understand how patterns of inequality emerge as well as to research the efficacy of policies that might mitigate social inequality. In doing so, I hope to have an impact on reducing educational disparities for future generations.

What do you see as the greatest research needs or recommendations to improve the relevance of education research for diverse communities of students and families?

I think one of the most important ways that we can improve the relevance of education research for diverse communities of students and families is to involve a more diverse group of voices in the research process. This includes creating more opportunities for researchers from different backgrounds who may ask questions that are uniquely informed by their own experiences or the experiences of their communities. In addition, I also believe that, as researchers, we have a responsibility to speak to the communities that are affected by the policies and patterns that we influence.  

What advice would you give to emerging scholars that are pursuing a career in education research?

My first piece of advice would be to find mentors and peers in graduate school who can support you. I have benefitted tremendously from the encouragement of my support system, and I have learned so much from my mentors and peers along the way. I would also encourage students from outside of the traditional social sciences to consider research in education. As an undergraduate engineering major, I was initially afraid to take a leap and change disciplines for graduate school, but in retrospect, I’m so glad that I did. At the time, I worried that my skillset and training in a different discipline would be a disadvantage, but I believe that my interdisciplinary background and unique perspective have helped me to grow my research agenda in ways that would not have been possible otherwise. 


This blog was produced by Bennett Lunn (Bennett.Lunn@ed.gov), Truman-Albright Fellow. It is part of an Inside IES Research blog series showcasing a diverse group of IES-funded education researchers and fellows that are making significant contributions to education research, policy, and practice.

Measuring Student Safety: New Data on Bullying Rates at School

Bullying remains a serious issue for students and their families, as well as policy makers, administrators, and educators. NCES is committed to providing reliable and timely data on bullying to measure the extent of the problem and track any progress toward reducing its prevalence. As such, a new set of web tables focusing on bullying rates at school was just released. These tables use data from the School Crime Supplement to the National Crime Victimization Survey, which collects data on bullying by asking a nationally representative sample of students ages 12–18 if they had been bullied at school. This blog post highlights data from these newly released web tables.

In 2019, about 22 percent of students reported being bullied at school during the school year (figure 1). This percentage was lower compared with a decade ago (2009), when 28 percent of students reported being bullied at school.

Students’ reports of being bullied varied based on student and school characteristics in 2019. For instance, a higher percentage of female students than of male students reported being bullied at school during the school year (25 vs. 19 percent). The percentage of students who reported being bullied at school was higher for students of Two or more races (37 percent) than for White students (25 percent) and Black students (22 percent), which were in turn higher than the percentage of Asian students (13 percent). Higher percentages of 6th-, 7th-, and 8th-graders reported being bullied at school (ranging from 27 to 28 percent), compared with 9th-, 10th-, and 12th-graders (ranging from 16 to 19 percent). A higher percentage of students enrolled in schools in rural areas (28 percent) than in schools in other locales (ranging from 21 to 22 percent) reported being bullied at school.


Figure 1. Percentage of students ages 12–18 who reported being bullied at school during the school year, by selected student and school characteristics: 2019

Horizontal bar chart showing the percentage of students ages 12–18 who reported being bullied at school during the school year in 2019, by selected student characteristics (sex, race/ethnicity, and grade) and school characteristics (locale and control of school)

1 Total includes race categories not separately shown.
2 Race categories exclude persons of Hispanic ethnicity. Data for Pacific Islander and American Indian/Alaska Native students did not meet reporting standards in 2019; therefore, data for these two groups are not shown.
3 Excludes students with missing information about the school characteristic.
NOTE: “At school” includes in the school building, on school property, on a school bus, and going to and from school. Although rounded numbers are displayed, the figures are based on unrounded data.
SOURCE: U.S. Department of Justice, Bureau of Justice Statistics, School Crime Supplement (SCS) to the National Crime Victimization Survey, 2019. See Digest of Education Statistics 2020, table 230.40.


Not all students chose to report the bullying to adults at school. Among students ages 12–18 who reported being bullied at school during the school year in 2019, about 46 percent reported notifying an adult at school about the incident. This percentage was higher for Black students than for White students (61 vs. 47 percent), and both percentages were higher than the percentage for Hispanic students (35 percent).

For more details on these data, see the web tables from “Student Reports of Bullying: Results from the 2019 School Crime Supplement to the National Crime Victimization Survey.” For additional information on this topic, see the Condition of Education indicator Bullying at School and Electronic Bullying. For indicators on other topics related to school crime and safety, select “School Crime and Safety” on the Explore by Indicator Topics page.

 

By Ke Wang, AIR

Congratulations Dr. Roddy Theobald on Winning the 2022 AEFP Early Career Award!

Headshot of Roddy TheobaldEach year, the Association for Education Finance and Policy (AEFP) recognizes one outstanding early career scholar whose research makes a significant contribution to the field of education finance and policy. In 2022, Dr. Roddy Theobald was the recipient of the Early Career award from AEFP. Congratulations to Dr. Theobald!

Dr. Theobald is a principal researcher in the Center for Analysis of Longitudinal Data in Education Research (CALDER) at the American Institutes for Research (AIR). CALDER, a collaboration among researchers at AIR and several universities around the United States, uses longitudinal data to explore a wide range of policy-relevant topics in education. Dr. Theobald’s research focuses on the teacher pipeline and its implications for student outcomes. Over the years, he has been involved in multiple IES-funded projects. These projects reflect a clear commitment to improving the teacher workforce and promoting positive outcomes for students. Dr. Theobald became interested in education policy research and studying the teacher workforce as a result of his experience as a 7th grade math teacher in the Oakland Unified School District. He is particularly interested in better understanding teacher shortage areas and what schools and districts can do to address them. 

As principal investigator (PI) on a recently completed researcher-practitioner partnership project, Dr. Theobald and his team worked in partnership with the Massachusetts Department of Elementary and Secondary Education to investigate the predictive validity of the state’s pre-service teacher evaluation systems and later in-service teaching outcomes and student outcomes. Key findings showed that teacher candidate performance on the Massachusetts Candidate Assessment of Performance, a practice-based assessment of student teaching, was predictive of their in-service summative performance ratings a year later. In examining the predictive validity of the Massachusetts Tests for Educator Licensure, results indicated that pre-service teacher scores were positively and significantly related to in-service performance ratings and value-added modeling of student test scores.

Dr. Theobald is currently the PI of a research grant that examines associations between pre-service teacher experiences (coursework, student teaching placements, and the match between student teaching experiences and early career experiences), special education teacher workforce entry and retention, and student academic outcomes. Using data on graduates of special education teacher education programs in Washington state, he found that the rate of special educator attrition is between 20-30%, which includes teachers that left public schools as well as those who moved to general education classrooms. Interestingly, the research team found that while dual endorsement in special and general education is positively associated with retention in the teaching workforce, it is negatively associated with retention in special education classrooms specifically. In terms of factors that promote retention, the research team found that better coherence between teacher preparation and early career experiences is associated with greater retention and that being supervised by a cooperating teacher endorsed in special education as part of student teaching is associated with a higher likelihood of becoming a special education teacher. The research team also found a link between preservice teacher experiences and student outcomes: students demonstrate larger reading gains when their district and the program from which their teacher graduated emphasized evidence-based literacy decoding practices and when a more experienced cooperating teacher supervised their teacher’s student teaching placement.

When we asked Dr. Theobald about the direction in which this line of research is heading, he explained, “immediate next steps in this line of work include looking at the employment outcomes of individuals trained to be special education teachers who never enter public school teaching or leave the teacher workforce, as well as better understanding the paraeducator workforce in public schools. It is also essential to understand how the special educator workforce has changed in response to the COVID pandemic, and we hope to study these changes in the years to come!”

This blog was authored by Kaitlynn Fraze, doctoral student at George Mason University and IES intern, and Katie Taylor (Katherine.Taylor@ed.gov), program officer at the National Center for Special Education Research.