IES Blog

Institute of Education Sciences

What We are Learning from NAEP Data About Use of Extended Time Accommodations

For students with learning disabilities, many of whom may take more time to read and process information than non-disabled peers, an extended time accommodation (ETA) is often used on standardized assessments. In 2021, IES awarded a grant for researchers to explore the test-taking behavior, including use of accommodations such as ETA, of students with disabilities in middle school using response process data from the NAEP mathematics assessment. In this blog, we interview Dr. Xin Wei from Digital Promise to see what she and Dr. Susu Zhang from University of Illinois at Urbana-Champaign are learning from their study.

The researchers have delved into the performance, process, and survey data of the eighth graders who took the digital NAEP mathematics test in 2017. Their recent article presents a quasi-experimental study examining the differences in these data across three distinct profiles of students with learning disabilities (LDs)—students with LD who received and utilized ETAs, students with LD who were granted ETAs but did not use them, and students with LD who did not receive ETAs.

The key findings from their study are as follows:

  • Students with LDs who used their ETAs performed statistically significantly better than their peers with LDs who were not granted ETA and those who received ETA but did not use it. They also engaged more with the test, as demonstrated by more frequent actions, revisits to items, and greater use of universal design features like drawing tool and text-to-speech functionalities on most of the math items compared to students who were not granted extended time.
  • Students with LDs who had ETAs but chose not to use them performed significantly worse than their peers with LDs who were not granted extended time.
  • Students with LDs who were granted ETAs saw the best performance with an additional 50% time (45 minutes compared to the usual 30 minutes provided to students without ETA).
  • Students who were given extra time, regardless of whether they used it, reported feeling less time pressure, higher math interest, and enjoying math more.
  • There were certain item types for which students who used ETAs performed more favorably.

We recently discussed the results of the study with Dr. Wei to learn more.


Which types of items on the test favored students who used extended time and why do you think they benefited?

Headshot of Xin Wei

The assessment items that particularly benefited from ETAs were not only complex but also inherently time-consuming. For example, students need to complete four sub-questions for item 5, drag six numbers to the correct places for item 6, type answers into four places to complete an equation for item 9, type in a constructive response answer for item 11, and complete a multiple-choice question and type answers in eight places to complete item 13.

For students with LDs, who often have slower processing speeds, these tasks become even more time-intensive. The additional time allows students to engage with each element of the question thoroughly, ensuring they have the opportunity to fully understand and respond to each part. This extended time is not just about accommodating different processing speeds; it's about providing the necessary space for these students to engage with and complete tasks that are intricate and time-consuming by design.

Why did you decide to look at the additional survey data NAEP collects on math interest and enjoyment in your study of extended time?

These affective factors are pivotal to academic success, particularly in STEM fields. Students who enjoy the subject matter tend to perform better, pursue related fields, and continue learning throughout their lives. This is especially relevant for students with LDs, who often face heightened test anxiety and lower interest in math, which can be exacerbated by the pressure of timed assessments. Our study's focus on these affective components revealed that students granted extra time reported a higher level of math interest and enjoyment even if they did not use the extra time. ETAs appear to alleviate the stress tied to time limits, offering dual advantages by not only aiding in academic achievement but also by improving attitudes toward math. ETAs could be a low-cost, high-impact accommodation that not only addresses academic needs but also contributes to emotional health.

What recommendations do you have based on your findings for classroom instruction?

First, it is crucial to prioritize extra time for students with LDs to enhance their academic performance and engagement. This involves offering flexible timing for assignments and assessments to reduce anxiety and foster a greater interest in learning. Teachers should be encouraged to integrate Universal Design for Learning principles into their instructional methods, emphasizing the effective use of technology, such as text-to-speech tools and embedded digital highlighters and pencils for doing scratchwork. Professional development for educators is essential to deepen their proficiency in using digital learning tools. Additionally, teachers should motivate students to use the extra time for thorough problem-solving and to revisit math tasks for accuracy. Regularly adjusting accommodations to meet the evolving needs of students with LDs is vital in creating an inclusive learning environment where every student can achieve success.

What is the implication of the study findings on education equity? 

Our study demonstrates that ETAs offer more than just a performance boost: they provide psychological benefits, reducing stress and enhancing interest and enjoyment with the subject matter. This is vital for students with LDs, who often face heightened anxiety and performance pressure. To make the system more equitable, we need a standardized policy for accommodations that ensures all students who require ETAs receive them. We must consider the variable needs of all students and question the current practices and policies that create inconsistencies in granting accommodations. If the true aim of assessments is to gauge student abilities, time is a factor that should not become a barrier.


U.S. Department of Education Resources

Learn more about the Department’s resources to support schools, educators, and families in making curriculum, instruction, and assessment accessible for students with disabilities.

Learn more about conducting research using response process data from the 2017 NAEP Mathematics Assessment.

 

This  interview blog was produced by Sarah Brasiel (Sarah.Brasiel@ed.gov), a program officer in the National Center for Special Education Research.

How Often Do High School Students Meet With Counselors About College? Differences by Parental Education and Counselor Caseload

There are many factors that can affect students’ decisions to apply to college, such as income, school engagement, and coursework.1 Similarly, previous research has reported that students whose parents did not hold a college degree (i.e., first-generation college students) enrolled in college at a lower rate than did peers whose parents held a college degree.2 However, high school counselors may help students choose colleges and apply to them, meaning that students who meet with a counselor about college could be more likely to attend college.3 Counselors may help potential first-generation college students plan for college by providing information that continuing-generation students already have access to via their parents who had attained college degrees themselves. Despite the potential benefits of meeting with a counselor, a school's counselor caseloads may affect its students' counseling opportunities.4

What percentage of high school students met with a counselor about college? How did this percentage vary by parental education and counselor caseload?

Around 47 percent of 2009 ninth-graders were potential first-generation college students whose parents did not hold a college degree (table U1). These students met with a counselor at a lower rate than did students whose parents held a college degree. Figure 1 shows that 72 percent of students whose parents did not hold a college degree met with a counselor, compared with 76 and 82 percent of students whose parents held an associate’s degree and a bachelor’s degree or higher, respectively.


Figure 1. Percentage of students who met with a counselor about college in 2012–13, by average counselor caseload level at the school and parents' highest education level

NOTE: Caseload is a continuous variable based on counselor reports of the average number of students per counselor at the school. Each caseload category accounts for roughly one-third of the sample in the unweighted data. Low caseload refers to counselors responsible for 40 to 299 students, medium caseload refers to counselors responsible for 300 to 399 students, and high caseload refers to counselors responsible for 400 or more students. The category high school degree or less incudes high school diploma or GED and those who started college but did not complete a degree. Respondents who did not know whether they met with a counselor are excluded from the analyses. These represent approximately 8 percent of weighted cases. 
SOURCE: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09) Base year, First Follow-up, and 2013 update.


During the senior year of most of the cohort of 2009 ninth-graders, the average counselor caseload at schools attended by these students5 was 375 students per counselor. The average caseload at public schools was 388, and the average caseload at private schools was 202.

Students attending schools with low counselor caseloads met with a counselor about college at a higher rate than did students at schools with high counselor caseloads, when comparing students whose parents had similar attainment levels. For example, at schools with low caseloads, 79 percent of students whose parents held a high school degree or less met with a counselor about college, compared with 70 percent of these students at schools with high caseloads. This pattern is also true for students at schools with low caseloads compared with medium caseloads (i.e., 86 vs. 76 percent of students whose parents held an associate’s degree and 89 vs. 81 percent of students whose parents held a bachelor’s degree), except among students whose parents held a high school degree or less (79 percent was not statistically different from 74 percent). Finally, students whose parents held a high school degree or less met with a counselor at a lower rate than did students whose parents held a bachelor’s degree or higher in each caseload category (i.e., 79 vs. 89 percent for low caseload schools, 74 vs. 81 percent for medium caseload schools, and 70 vs. 77 percent for high caseload schools).

For more information about counselor meetings and college enrollment, check out this Data Point: High School Counselor Meetings About College, College Attendance, and Parental Education.

This blog post uses data from the High School Longitudinal Study of 2009 (HSLS:09), a national study of more than 23,000 ninth-graders and their school counselors in fall 2009. Student sample members answered surveys between 2009 and 2016. Sample members or their parents reported on whether the student met with a counselor about college during the 2012–13 school year (most students’ 12th-grade year).

While data presented here are the most recent data available on the topic, NCES will have new data on high schoolers’ experiences in the 2020s coming soon. In particular, data from the High School and Beyond Longitudinal Study of 2022 (HS&B:22), which also includes information about students’ visits to school counselors, is forthcoming.

Until those data are released, we recommend you access HSLS:09 student and counselor data to conduct your own analyses via NCES’s DataLab.

 

By Catharine Warner-Griffin, AnLar, and Elise Christopher, NCES


[1] See, for example, Fraysier, K., Reschly, A., and Appleton, J. (2020). Predicting Postsecondary Enrollment With Secondary Student Engagement Data. Journal of Psychoeducational Assessment, 38(7), 882–899.

[2] Cataldi, E. F., Bennett, C. T., and Chen, X. (2018). First-Generation Students: College Access, Persistence, and Postbachelor’s Outcomes (2018-421). U.S. Department of Education. Washington, DC: National Center for Education Statistics.

[3] Tang, A. K., and Ng, K. M. (2019). High School Counselor Contacts as Predictors of College Enrollment. Professional Counselor, 9(4), 347–357.

[4] Woods, C. S., and Domina, T. (2014). The School Counselor Caseload and the High School-to-College Pipeline. Teachers College Record, 116(10), 1–30.

[5] These schools are only those sampled in the base year (i.e., students’ 2009 schools).

The Impact of Parent-Mediated Early Intervention on Social Communication for Children with Autism

A key challenge for children with autism is the need to strengthen social communication, something that can be supported early in a child’s development. Dr. Hannah Schertz, professor at Indiana University Bloomington’s School of Education, has conducted a series of IES-funded projects to develop and evaluate the impact of early intervention, mediated through parents, for improving social communication in toddlers with or at risk for autism. We recently interviewed Dr. Schertz to learn more about the importance of guiding parents in the use of mediated learning practices to promote social communication, how her current research connects with her prior research, and what she hopes to accomplish.

Why is parental mediation in early intervention important for very young children with autism? How does it work and why do you focus this approach on improving children’s social communication development?

Headshot of Hannah Schertz

The intervention targets social communication because it is the core autism challenge and it’s important to address concerns early, as signs of autism emerge. Research has found that preverbal social communication is related to later language competency. Our premise is that this foundation will give toddlers a reason to communicate and set the stage for verbal communication. More specifically, joint attention—one preverbal form of social communication—is the key intervention target in our research. It is distinct from requesting/directing or following requests, which are instrumental communications used to accomplish one’s own ends. Joint attention, which takes the partner’s interests and perspectives into account, is an autism-specific challenge whereas more instrumental communication skills are not.

Our research team incorporates a mediated learning approach at two levels—early intervention providers supporting parents and then parents supporting their toddlers. The approach is designed to promote active engagement in the learning process and leverage the parent’s privileged relationship with the child as the venue for social learning. Early intervention providers help parents understand both the targeted social communication outcomes for their children (intervention content) and the mediated learning practices (intervention process) used to promote these child outcomes. As parents master these concepts, they can translate them flexibly into a variety of daily parent-child interactions. This understanding allows parents to naturally integrate learning opportunities with child interests and family cultural/language priorities and preferences. Over time, their accrued knowledge, experience, and increased self-efficacy should prepare them to continually support the child’s social learning even after their participation in the project ends.

How does your more recent work, developing and testing Building Interactive Social Communication (BISC), extend your prior research examining Joint Attention Mediated Learning (JAML)?

Both JAML and BISC address the same goal—supporting social communication as early signs of autism emerge. In JAML, researchers guided parent learning directly while parents incorporated social communication into interaction with their toddlers. BISC extends the intervention by supporting community-based practitioners in facilitating parent learning rather than parents learning directly from the research team. BISC also added a component to address cases in which parents identify child behaviors that substantially interfere with the child’s social engagement.

You recently completed a pilot study to test a new professional development framework for supporting early intervention providers in implementing BISC. Please tell us about the findings of this study. What were the impacts on the early intervention providers, parents, and toddlers?

We tested an early version of BISC to study its preliminary effects on early intervention provider, parent, and child outcomes for 12 provider/parent/child triads. In effect size estimates derived from single-case design data, we found large effects for early intervention provider fidelity (for example, mediating parent learning, guiding parents’ reflection on video-recorded interaction with their toddlers, and supporting active parent engagement) and parent application of mediated learning practices to promote toddler social communication. We also found large effects on child outcomes (social reciprocity, child behavior, and social play) and a small effect on joint attention.

As you begin your larger-scale trial to examine the efficacy of BISC on provider, parent, and child outcomes, what impact do you hope your work will have on the field of early intervention generally and the development of social communication in children with autism more specifically? 

Approximately 165 community-based early intervention practitioners will have learned to support parent learning through direct participation or as control group participants who receive self-study materials. These providers will be equipped to bring this knowledge to their future work. We anticipate that practitioners will experience their implementation role as feasible and effective. Ultimately, toddlers with early signs of autism will have greater access to early, developmentally appropriate, and family-empowering early intervention that directly addresses the core social difficulty of autism. Forthcoming published materials will extend access to other providers, offering an intervention that is more specifically tailored to the needs of very young children with social communication challenges than other approaches.

Is there anything else you would like to share/add regarding your projects? 

I would like to thank my colleagues and project co-principal investigators (Co-PIs) for their expertise and contributions to this work. For our current BISC efficacy project, Co-PI Dr. Patricia Muller (Director of the Center for Evaluation, Policy, and Research) is leading the randomized controlled trial and cost-effectiveness study, and Co-PI Dr. Jessica Lester (professor of Counseling and Educational Psychology) is overseeing the qualitative investigation of parent-child interactions using conversation analysis to explore potential influences on child outcomes. Kathryn Horn coordinates intervention activities, Lucia Zook oversees operational and assessment activities, and Addison McGeary supports recruitment and logistical activities.

This blog was authored by Skyler Fesagaiga, a Virtual Student Federal Service intern for NCSER and graduate student at the University of California, San Diego. The grants in this connected line of research have been managed by Amy Sussman (PO for NCSER’s early intervention portfolio) and Emily Weaver (PO for NCSER’s autism research portfolio).

ED/IES SBIR Special Education Technology is Showcased at the White House Demo Day

On Tuesday, November 7, 2023, the White House’s Office of Science and Technology Policy hosted a Demo Day of American Possibilities at the Showroom in Washington, DC.  The event featured 45 emerging technologies created by innovators through federal research and development programs across areas such as health, national security, AI, robotics, climate, microelectronics, and education. President Biden attended the event and met with several developers to learn about and see demonstrations of the innovations.

An IES-supported project by a Michigan-based Alchemie, the KASI Learning System (KASI), was invited to represent the U.S. Department of Education and its Small Business Innovation Research program, which IES administers.

KASI is an inclusive assistive technology that employs computer vision and multi-sensory augmented reality to support blind and low vision learners in using hand-held physical manipulatives to practice chemistry. A machine learning engine in KASI generates audio feedback and prompts to personalize the experience as learners progress. At the event, the project’s principal investigator and former high school chemistry educator, Julia Winter, demonstrated KASI to leaders in government and to attendees from the assistive technology field.

ED/IES SBIR supported the initial development for KASI through three awards. Based on these awards, Alchemie received funding from angel investors in Michigan, won a commercialization grant from the Michigan Emerging Technology Fund, and is establishing partnerships with publishers in K-12 and higher education. To extend KASI to more topics, Alchemie has won additional SBIR awards from the National Science Foundation, the National Institutes of Health, and the National Institute of Disability, Independent Living, and Rehabilitation Research, and is currently a finalist in the 2024 Vital Prize Challenge competition. KASI has also recently been highlighted in Forbes and Crain’s Detroit Business.

 

 

Stay tuned for updates on KASI and other education technology projects through the ED/IES SBIR program on Twitter, Facebook, and LinkedIn.


About ED/IES SBIR: The Department of Education’s (ED) Small Business Innovation Research (SBIR) program, administered by the Institute of Education Sciences (IES), funds entrepreneurial developers to create the next generation of technology products for learners, educators, and administrators. The program, known as ED/IES SBIR, emphasizes an iterative design and development process and pilot research to test the feasibility, usability, and promise of new products to improve outcomes. The program also focuses on planning for commercialization so that the products can reach schools and end-users and be sustained over time. Millions of students in thousands of schools around the country use technologies developed through ED/IES SBIR.

Edward Metz (Edward.Metz@ed.gov) is the Program Manager of the ED/IES SBIR program.

Laurie Hobbs (Laurie.Hobbs@ed.gov) is the Program Analyst of the ED/IES SBIR program.

NCES Provides New Data Table on School District Structures

The National Center for Education Statistics (NCES) has released a new data table (Excel) on local education agencies (LEAs)1 that serve multiple counties. This new data table can help researchers understand how many LEAs exist and break down enrollment by LEA and county.

Variation in School District Structures

The organizing structures for LEAs vary across the United States. In many areas of the country, LEAs share boundaries with counties or cities. In other areas, there are multiple LEAs within a single county. LEAs also can span multiple counties.

The organizing structures for LEAs or school districts reflect the policies and practices of local and state governments and historical trends across many states. For example, there was a large consolidation in LEAs in the last century as the number of regular school districts decreased from 117,100 in 1939–40 to fewer than 14,900 in 2000–01. In contrast to these declines, the numbers of charter schools and charter school agencies operating outside of regular school district and county frameworks have increased over the past 2 decades.2

Impact of Structural Differences in School Districts

These structural differences can make it challenging for researchers to estimate student enrollment by county and drill down into other data. This is important because the structure of LEAs and their relationships to county boundaries can impact the capability of researchers and policy analysts to align existing county and district data in ways that could better inform education policies.3 In addition, these structures can affect the designs of new surveys and research activities. For example, research or data collections on career and technical education (CTE) activities at the district level would need to accommodate structural differences in where CTE activities are typically provided—that is, in general education districts (as is the case in most states) or through separate CTE-focused LEAs.

New Data Table on LEAs Serving Multiple Counties

NCES has taken valuable steps to increase the amount of information available to the research community about funding crossing district lines. In fiscal year 2018, a data item was added to the School District Finance Survey (F-33) that includes current expenditures made by regional education service agencies (RESAs) and other specialized service agencies (e.g., supervisory unions) that benefit the reporting LEA.4

Our recently released data table (Excel)—which shows the prevalence and enrollment size of LEAs that serve multiple counties—will facilitate a better understanding of how RESA expenditures are included in the district-level total current expenditures and current expenditure per pupil amounts displayed in the annual Revenues and Expenditures for Public Elementary and Secondary School Districts finance tables.

Understanding the New Data Table

The data table uses data from the Common Core of Data (CCD) and Demographic and Geographic Estimates (EDGE) to provide county and student enrollment information on each LEA in the United States (i.e., in the 50 states and the District of Columbia) with a separate row for each county in which the agency has a school presence. The table includes all LEA types, such as regular school districts, independent charter school districts, supervisory union administrative centers, service agencies, state agencies, federal agencies, specialized public school districts, and other types of agencies.

LEA presence within a county is determined by whether it had at least one operating school in the county. School presence within a county is determined by whether there is at least one operating school in the county identified in the CCD school-level membership file. For example, an LEA that is coterminous with a county has one record (row) in the listing. A charter school LEA that serves a region of a state and has a presence in five counties has five records. LEA administrative units, which do not operate schools, are listed in the county in which the agency is located.

In the 2021–22_LEA_List tab, column D shows the “multicnty” (i.e., multicounty) variable. LEAs are assigned one of the following codes:

1 = School district (LEA) is in single county and has reported enrollment.

2 = School district (LEA) is in more than one county and has reported enrollment.

8 = School district (LEA) reports no schools and no enrollment, and the county reflects county location of the administrative unit. 

9 = School district (LEA) reports schools but no enrollment, and the county reflects county location of the schools.

In the Values tab, the “Distribution of local education agencies, by enrollment and school status: 2021–22” table shows the frequency of each of the codes (1, 2, 8, and 9) (i.e., the number of records that are marked with each of the codes in the 2021–22_LEA_List tab):

  • 17,073 LEAs had schools in only one county.
  • 1,962 LEAs had schools located in more than one county and reported enrollment for these schools.
  • 1,110 LEAs had no schools of their own and were assigned to a single county based on the location of the LEA address. (Typically, supervisory union administrative centers are examples of these LEAs.)
  • 416 LEAs had schools located in one county but did not report enrollment for these schools.

 

By Tom Snyder, AIR


[1] Find the official definition of an LEA.

[4] The annual School District Finance Survey (F-33) is collected by NCES from state education agencies and the District of Columbia. See Documentation for the NCES Common Core of Data School District Finance Survey (F-33) for more information.