Inside IES Research

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

A few months ago, a team of researchers conducting a large, IES-funded randomized controlled trial (RCT) on the intervention Promoting Accelerated Reading Comprehension of Text-Local (PACT-L) met with the Cost Analysis in Practice (CAP) Project team in search of planning support. The PACT-L team had just received funding for a 5-year systematic replication evaluation and were consumed with planning its execution. During an initial call, Iliana Brodziak, who is leading the cost analysis for the evaluation study, shared, “This is a large RCT with 150 schools across multiple districts each year. There is a lot to consider when thinking about all of the moving pieces and when they need to happen. I think I know what needs to happen, but it would help to have the key events on a timeline.”

The comments and feeling of overload are very common even for experienced cost analysts like Iliana because conducting a large RCT requires extensive thought and planning. Ideally, planning for a cost analysis at this scale is integrated with the overall evaluation planning at the outset of the study. For example, the PACT-L research team developed a design plan that specified the overall evaluation approach along with the cost analysis. Those who save the cost analysis for the end, or even for the last year of the evaluation, may find they have incomplete data, insufficient time or budget for analysis, and other avoidable challenges. Iliana understood this and her remark set off a spark for the CAP Project team—developing a timeline that aligns the steps for planning a cost analysis with RCT planning.

As the PACT-L and CAP Project teams continued to collaborate, it became clear that the PACT-L evaluation would be a great case study for crafting a full cost analysis timeline for rigorous evaluations. The CAP Project team, with input from the PACT-L evaluation team, created a detailed timeline for each year of the evaluation. It captures the key steps of a cost analysis and integrates the challenges and considerations that Iliana and her team anticipated for the PACT-L evaluation and similar large RCTs.

In addition, the timeline provides guidance on the data collection process for each year of the evaluation.

• Year 1:  The team designs the cost analysis data collection instruments. This process includes collaborating with the broader evaluation team to ensure the cost analysis is integrated in the IRB application, setting up regular meetings with the team, and creating and populating spreadsheets or some other data entry tool.
• Year 2: Researchers plan to document the ingredients or resources needed to implement the intervention on an ongoing basis. The timeline recommends collecting data, reviewing the data, and revising the data collection instruments in Year 2.
• Year 3 (and maybe Year 4): The iteration of collecting data and revising instruments continue in Year 3 and, if needed, in Year 4.
• Year 5: Data collection should be complete, allowing for the majority of the analysis. 

This is just one example of the year-by-year guidance included in the timeline. The latest version of the Timeline of Activities for Cost Analysis is available to help provide guidance to other researchers as they plan and execute their economic evaluations. As a planning tool, the timeline gathers all the moving pieces in one place. It includes detailed descriptions and notes for consideration for each year of the study and provides tips to help researchers.

The PACT-L evaluation team is still in the first year of the evaluation, leaving time for additional meetings and collective brainstorming. The CAP Project and PACT-L teams hope to continue collaborating over the next few years, using the shared expertise among the teams and PACT-L’s experience carrying out the cost analysis to refine the timeline.

Visit the CAP Project website to find other free cost analysis resources or to submit a help request for customized technical assistance on your own project.

Jaunelle Pratt-Williams is an Education Researcher at SRI International.

Iliana Brodziak is a senior research analyst at the American Institutes for Research.

Katie Drummond, a Senior Research Scientist at WestEd. 

Lauren Artzi is a senior researcher at the American Institutes for Research.

English learners (ELs) are the fastest growing group of students in U.S. public schools. They are disproportionately at risk for poor academic outcomes and are more likely than non-ELs to be classified as having specific learning disabilities and speech/language impairment. Data collected by the U.S. Department of Education in school year 2018-2019 (Common Core of Data, Individuals with Disabilities Education Act (IDEA) data) indicate that approximately 14.1% of students in classrooms across the country received services through IDEA Part B. Nationally, 11.3% of students with disabilities were ELs, a little higher than the percentage of total student enrollment who were ELs (10.2%). However, it is important to distinguish between language and literacy struggles that are due to learning English as a second language and those due to a language or reading disability. For those who have or are at risk for a disability and in need of intervention, it is also important that the interventions are linguistically and culturally appropriate for these children.

Since the first round of competitions in 2006, the National Center for Special Education Research (NCSER) has funded research on ELs with or at risk for disabilities. The projects are in broad topic areas, including early childhood; reading, writing, and language development; cognition and learning; and social and behavioral skill development. They vary with respect to the types of research conducted (such as exploration, development, efficacy, measurement) as well as the extent to which they focus on ELs, from ELs as the exclusive or primary population of interest to a secondary focus as a student group within the general population.

As an example, David Francis (University of Houston) explored factors related to the identification and classification of reading and language disabilities among Spanish-speaking ELs. The aim was to provide schools with clearer criteria and considerations for identifying learning disabilities among these students in kindergarten through grade 2. Analyzing data from previous studies, the team found that narrative measures (measures in which narrative responses were elicited, transcribed, and scored) were more sensitive to identifying EL students with disabilities than standardized measures that did not include a narrative component. They also found that the differences in student language growth depended on the language used in the instruction and the language used to measure outcomes. Specifically, language growth was greatest for Spanish-instructed students on Spanish reading and language outcomes, followed by English outcomes for English-instructed students, English outcomes for Spanish-instructed students, and with the lowest growth, Spanish outcomes for English-instructed students.

A number of these projects are currently in progress. For example, Ann Kaiser (Vanderbilt University) and her team are using a randomized controlled trial to test the efficacy of a cultural and linguistic adaptation of Enhanced Milieu Teaching (EMT). EMT en Español aims to improve the language and related school readiness skills of Spanish-speaking toddlers with receptive and expressive language delays who may be at risk for language impairment. In another study, Nicole Schatz (Florida International University) and her team will be using a randomized controlled trial to compare the efficacy of a language-only, behavior-only, or combination language and behavior intervention for students in early elementary school who are English language learners with or at risk for ADHD.

Overall, NCSER has funded 12 research grants that focus specifically on English learners, dual-language learners, and/or Spanish-speaking children with or at risk for disabilities, including the following:

• Interventions for English Language Learners At-Risk for ADHD 
  Principal Investigator: Nicole Schatz
  FY 2021
• Computer Adaptive Storybook Assessments (CASA) 
  Principal Investigator: Alisha Wackerle-Hollman
  FY 2021
• Video- and App-Based Naturalistic Language Instruction (VALI) for Spanish-Speaking Caregivers to Support Bilingual Language Development in Children with or At Risk for Language Delays 
  Principal Investigator: Anne Larson
  FY 2020
• EMT en Español: Comprehensive Early Intervention to Support School Readiness Skills for Spanish-Speaking Toddlers with Language Delays 
  Principal Investigator: Ann Kaiser
  FY 2019
• Cognitive and Linguistic Mediators of Response to Intensive Interventions in Reading for English Learners At-Risk for Learning Disabilities 
  Principal Investigator: Jeremy Miciak
  FY 2017
• Identification of Reading and Language Disabilities in Spanish-Speaking English Learners 
  Principal Investigator: David Francis
  FY 2016
• Testing an Integrated Preschool Curriculum for English Language Learners 
  Principal Investigator: Susan H. Landry
  FY 2011
• Developing a Narrative Intervention 
  Principal Investigator: Sandra Laing Gillam
  FY 2010
• Growth in Literacy, Language, and Cognition in Children with Reading Disabilities who are English Language Learners 
  Principal Investigator: H. Lee Swanson
  FY 2009
• Spanish Screener for Language Impairment in Children (SSLIC) 
  Principal Investigator: Maria Adelaida Restrepo 
  FY 2008
• Precision in Response to Intervention Models: Variations of Measurement, Instruction, Student Language, and Age 
  Principal Investigator: Rollanda O’Connor
  FY 2007
• Vocabulary, Oral Language, and Academic Readiness (VOLAR): A Language Intervention for Latino Preschool English Language Learners with Language Disorders 
  Principal Investigator: Vera Gutierrez-Clellen
  FY 2006

In addition to the research focused specifically on English learners, many other projects include ELs as a large portion of their sample and/or focus some of their analyses specifically on the student group of ELs with or at risk for disabilities. A few recently completed studies show encouraging results with little differences between ELs and non-ELs. For example, Nathan Clemens (University of Texas, Austin) investigated the adequacy of six early literacy measures and validated their use for monitoring the reading progress for kindergarten students at risk for reading disabilities. As part of this project, the research team conducted subgroup analyses that indicated ELs do not necessarily demonstrate lower initial scores and rates of growth over time than non-ELs and that there are few differences between ELs and non-ELs in the extent to which the initial performance or rate of growth differentially predict later reading skills. As another example, Jeanne Wanzek (Vanderbilt University) examined the efficacy of an intensive multicomponent reading intervention for fourth graders with severe reading difficulties. The team found that those in the intervention group outperformed their peers in word reading and word fluency, but not reading fluency or comprehension; importantly, there was no variation in outcomes based on English learner status.

NCSER continues to value and support research projects that focus on English learners with or at risk for disabilities throughout its various programs of research funding.

This blog was written by Amy Sussman, NCSER Program Officer

Under an IES grant, Michigan State University, in collaboration with the Michigan Department of Education, the Michigan Center for Educational Performance and Information, and the University of Michigan, is assessing the implementation, impact, and cost of the Michigan “Read by Grade 3” law intended to increase early literacy outcomes for Michigan students. In this guest blog, Dr. Tanya Wright and Lori Bruner discuss how they were able to quickly pivot to a remote data collection plan when COVID-19 disrupted their initial research plan.  

The COVID-19 pandemic began while we were planning a study of early literacy coaching for the 2020-2021 academic year. It soon became abundantly clear that restrictions to in-person research would pose a major hurdle for our research team. We had planned to enter classrooms and record videos of literacy instruction in the fall. As such, we found ourselves faced with a difficult choice: we could pause our study until it became safer to visit classrooms and miss the opportunity to learn about literacy coaching and in-person classroom instruction during the pandemic, or we could quickly pivot to a remote data collection plan.

Our team chose the second option. We found that there are multiple technologies available to carry out remote data collection. We chose one of them (a device known as the Swivl) that included a robotic mount, where a tablet or smartphone can be placed to take the video, with a 360-degree rotating platform that works in tandem with a handheld or wearable tracker and an app that allows videos to be instantly uploaded to a cloud-based storage system for easy access.

Over the course of the school year, we captured over 100 hours of elementary literacy instruction in 26 classrooms throughout our state. While remote data collection looks and feels very different from visiting a classroom to record video, we learned that it offers many benefits to both researchers and educators alike. We also learned a few important lessons along the way.

First, we learned remote data collection provides greater flexibility for both researchers and educators. In our original study design, we planned to hire data collectors to visit classrooms, which restricted our recruitment of schools to a reasonable driving distance from Michigan State University (MSU). However, recording devices allow us to capture video anywhere, including rural areas of our state that are often excluded from classroom research due to their remote location. Furthermore, we found that the cost of purchasing and shipping equipment to schools is significantly less than paying for travel and people’s time to visit classrooms. In addition, using devices in place of data collectors allowed us to easily adapt to last-minute schedule changes and offer teachers the option to record video over multiple days to accommodate shifts in instruction due to COVID-19.

Second, we discovered that we could capture more classroom talk than when using a typical video camera. After some trial and error, we settled on a device with three external wireless microphones: one for the teacher and two additional microphones to place around the classroom. Not only did the extra microphones record audio beyond what the teacher was saying, but we learned that we can also isolate each microphone during data analysis to hear what is happening in specific areas of the classroom (even when the teacher and children were wearing masks). We also purchased an additional wide-angle lens, which clipped over the camera on our tablet and allowed us to capture a wider video angle.  

Third, we found remote data collection to be less intrusive than sending a research team into schools. The device is compact and can be placed on any flat surface in the classroom or be mounted on a basic tripod. The teacher has the option to wear the microphone on a lanyard to serve as a hands-free tracker that signals the device to rotate to follow the teacher’s movements automatically. At the end of the lesson, the video uploads to a password-protected storage cloud with one touch of a button, making it easy for teachers to share videos with our research team. We then download the videos to the MSU server and delete them from our cloud account. This set-up allowed us to collect data with minimal disruption, especially when compared to sending a person with a video camera to spend time in the classroom.

As with most remote work this year, we ran into a few unexpected hurdles during our first round of data collection. After gathering feedback from teachers and members of our research team, we were able to make adjustments that led to a better experience during the second round of data collection this spring. We hope the following suggestions might help others who are considering such a device to collect classroom data in the future:

1. Consider providing teachers with a brief informational video or offering after-school training sessions to help answer questions and address concerns ahead of your data collection period. We initially provided teachers with a detailed user guide, but we found that the extra support was key to ensuring teachers had a positive experience with the device. You might also consider appointing a member of your research team to serve as a contact person to answer questions about the remote data collection during data collection periods.
2. As a research team, it is important to remember that team members will not be collecting the data, so it is critical to provide teachers with clear directions ahead of time: what exactly do you want them to record? Our team found it helpful to send teachers a brief two-minute video outlining our goals and then follow up with a printable checklist they could use on the day they recorded instruction. 
3. Finally, we found it beneficial to scan the videos for content at the end of each day. By doing so, we were able to spot a few problems, such as missing audio or a device that stopped rotating during a lesson. While these instances were rare, it was helpful to catch them right away, while teachers still had the device in their schools so that they could record missing parts the next day.

Although restrictions to in-person research are beginning to lift, we plan to continue using remote data collection for the remaining three years of our project. Conducting classroom research during the COVID-19 pandemic has proven challenging at every turn, but as we adapted to remote video data collection, we were pleased to find unanticipated benefits for our research team and for our study participants.

This blog is part of a series focusing on conducting education research during COVID-19. For other blog posts related to this topic, please see here.

Tanya S. Wright is an Associate Professor of Language and Literacy in the Department of Teacher Education at Michigan State University.

Lori Bruner is a doctoral candidate in the Curriculum, Instruction, and Teacher Education program at Michigan State University.

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

Educational interventions come at a cost—and no, it is not just the price tag, but the personnel time and other resources needed to implement them effectively. Having both efficacy and cost information is essential for educators to make wise investments. However, including cost analysis in an efficacy study comes with its own costs.

Experts from the Cost Analysis in Practice (CAP) Project recently connected with the IES-funded team studying Promoting Accelerated Reading Comprehension of Text - Local (PACT-L) to discuss the challenges of conducting cost analysis and cost-effectiveness analysis as part of an efficacy trial. PACT-L is a social studies and reading comprehension intervention with a train-the-trainer professional development model. Here, we share some of the challenges we discussed and the solutions that surfaced.

Challenge 1: Not understanding the value of a cost analysis for educational programs

Some people may not understand the value of a cost analysis and focus only on needing to know whether they have the budget to cover program expenses. For those who may be reluctant to invest in a cost analysis, ask them to consider how a thorough look at implementation in practice (as opposed to “as intended”) might help support planning for scale-up of a local program or adoption at different sites.

For example, take Tennessee’s Student/Teacher Achievement Ratio (STAR) project, a class size reduction experiment, which was implemented successfully with a few thousand students. California tried to scale up the approach for several million students but failed to anticipate the difficulty of finding enough qualified teachers and building more classrooms to accommodate smaller classes. A cost analysis would have supplied key details to support decision-makers in California in preparing for such a massive scale-up, including an inventory of the type and quantity of resources needed. For decision-makers seeking to replicate an effective intervention even on a small scale, success is much more likely if they can anticipate whether they have the requisite time, staff, facilities, materials, and equipment to implement the intervention with fidelity.

Challenge 2: Inconsistent implementation across cohorts

Efficacy studies often involve two or three cohorts of participants, and the intervention may be adapted from one to the next, leading to varying costs across cohorts. This issue has been particularly acute for studies running prior to the COVID-19 pandemic, then during COVID-19, and into post-COVID-19 times. You may have in-person, online, and hybrid versions of the intervention delivered, all in the course of one study. While such variation in implementation may be necessary in response to real-world circumstances, it poses problems for the effectiveness analysis because it’s hard to draw conclusions about exactly what was or wasn’t effective.

The variation in implementation also poses problems for the cost analysis because substantially different types and amounts of resources might be used across cohorts. At worst, this leads to the need for three cost analyses funded by the study budget intended for one! In the case of PACT-L, the study team modified part of the intervention to be delivered online due to COVID-19 but plans to keep this change consistent through all three cohorts.

For other interventions, if the differences in implementation among cohorts are substantial, perhaps they should not be combined and analyzed as if all participants are receiving a single intervention. Cost analysts may need to focus their efforts on the cohort for which implementation reflects how the intervention is most likely to be used in the future. For less substantial variations, cost analysts should stay close to the implementation team to document differences in resource use across cohorts, so they can present a range of costs as well as an average across all cohorts.

Challenge 3: Balancing accuracy of data against burden on participants and researchers

Data collection for an efficacy trial can be burdensome—add a cost analysis and researchers worry about balancing the accuracy of the data against the burden on participants and researchers. This is something that the PACT-L research team grappled with when designing the evaluation plan. If you plan in advance and integrate the data collection for cost analysis with that for fidelity of implementation, it is possible to lower the additional burden on participants. For example, include questions related to time use in interviews and surveys that are primarily designed to document the quality of the implementation (as the PACT-L team plans to do), and ask observers to note the kinds of facilities, materials, and equipment used to implement the intervention. However, it may be necessary to conduct interviews dedicated solely to the cost analysis and to ask key implementers to keep time logs. We’ll have more advice on collecting cost data in a future blog.

Challenge 4: Determining whether to use national and/or local prices

Like many other RCTs, the PACT-L team’s study will span multiple districts and geographical locations, so the question arises about which prices to use. When deciding whether to use national or local prices—or both—analysts should consider the audience for the results, availability of relevant prices from national or local sources, the number of different sets of local prices that would need to be collected, and their research budget. Salaries and facilities prices may vary significantly from location to location. Local audiences may be most interested in costs estimated using local prices, but it would be a lot of work to collect local price information from each district or region. The cost analysis research budget would need to reflect the work involved. Furthermore, for cost-effectiveness analysis, prices must be standardized across geographical locations which means applying regional price parities to adjust prices to a single location or to a national average equivalent.

It may be more feasible to use national average prices from publicly available sources for all sites. However, that comes with a catch too: national surveys of personnel salaries don't include a wide variety of school or district personnel positions. Consequently, the analyst must look for a similar-enough position or make some assumptions about how to adjust a published salary for a different position.

If the research budget allows, analysts could present costs using national prices and local prices. This might be especially helpful for an intervention targeting schools in a rural area or an urban area which, respectively, are likely to have lower and higher costs than the national average. The CAP Project’s cost analysis Excel template is set up to allow for both national prices and local prices. You can find the template and other cost analysis tools here: https://capproject.org/resources.

The CAP Project team is interested in learning about new challenges and figuring out how to help. If you are encountering similar or other challenges and would like free technical assistance from the IES-funded CAP Project, submit a request here. You can also email us at helpdesk@capproject.org or tweet us @The_CAP_Project. 

Fiona Hollands is a Senior Researcher at Teachers College, Columbia University who focuses on the effectiveness and costs of educational programs, and how education practitioners and policymakers can optimize the use of resources in education to promote better student outcomes.

Iliana Brodziak is a senior research analyst at the American Institutes for Research who focuses on statistical analysis of achievement data, resource allocation data and survey data with special focus on English Learners and early childhood.

Jaunelle Pratt-Williams is an Education Researcher at SRI who uses mixed methods approaches to address resource allocation, social and emotional learning and supports, school finance policy, and educational opportunities for disadvantaged student populations.

Robert D. Shand is Assistant Professor in the School of Education at American University with expertise in teacher improvement through collaboration and professional development and how schools and teachers use data from economic evaluation and accountability systems to make decisions and improve over time.

Katie Drummond, a Senior Research Scientist at WestEd, has designed and directed research and evaluation projects related to literacy, early childhood, and professional development for over 20 years. 

Lauren Artzi is a senior researcher with expertise in second language education PK-12, intervention research, and multi-tiered systems of support. 

For almost two decades, IES/NCSER has funded Brian Bottge and his teams at the University of Kentucky and University of Wisconsin-Madison to develop and test the efficacy of a teaching method called Enhanced Anchored Instruction (EAI), which helps low-achieving middle school students with math disabilities develop their problem-solving skills by solving meaningful problems related to a real-world problem. The research findings support the efficacy of EAI, especially for students with math disabilities. Most recently, Bottge and his team have been researching innovative forms of assessment that more adequately capture what students with disabilities know both conceptually and procedurally in solving math problems. With supplemental funding, IES/NCSER extended Dr. Bottge’s latest grant to test the use of oral assessment to measure student knowledge and compare that with the knowledge demonstrated on a pencil and paper test. The COVID-19 pandemic introduced added challenges to this work when schools closed and students shifted to online education.

Below we share a recent conversation with Dr. Bottge about the experience of conducting research during a pandemic and what he and his team were still able to learn about the value of oral assessment in mathematics for students with disabilities.

What changes did you observe in the intervention implementation by teachers due to the COVID-related shift to online learning?

The shift to online learning created changes in class size and structure. For 38 days (22 days in classroom, 16 days online through a virtual meeting platform), the middle school special education teacher first taught concepts through a widely used video-based anchored problem, the Kim’s Komet episode of the Jasper Project, in which characters compete in a “Grand Pentathlon.” The teacher then engaged the students in a hands-on application of the concepts by running a live Grand Pentathlon. In the Grand Pentathlon, students make their own cars, race them on a full-size ramp, time them at various release points on the ramp, and graph the information to estimate the speed of the cars. The purpose of both units was to help students develop their informal understanding of pre-algebraic concepts such as linear function, line of best fit, variables, rate of change (slope), reliability, and measurement error. Midway through the study, in-person instruction was suspended and moved online. Instead of working with groups of three to four students in the resource room throughout the day, the teacher provided online instruction to 14 students at one time and scheduled one-on-one sessions with students who needed extra help.

What challenges did you observe in the students interacting with the activities and their learning once they shifted to online learning?

All students had access to a computer at home and they were able to use the online platform without much confusion because they had used it in other classes. The screen share feature enabled students to interact with much of the curriculum by viewing the activities, listening to the teacher, and responding to questions, although they could not fully participate in the hands-on part of the lessons. Class attendance and student behavior were unexpectedly positive during the days when students were online. For example, one student had displayed frequent behavioral outbursts in school but became a positive and contributing member of the online class. The ability to mute mics in the platform gave the teacher the option of allowing only one student to talk at a time.

Were students still able to participate in the hands-on activities that are part of the intervention?

For the hands-on activities related to the Grand Pentathlon competition, the teacher taught online and a research staff member manipulated the cars, track, and electronic timers from campus. Students watched their computer screens waiting for their turn to time their cars over the length of the straightaway. The staff member handled each student’s cars and one by one released them from the height on the ramp as indicated by each student. After students had recorded the times, the teacher asked students to calculate and share the speeds of their cars for each time trial height.

Do you have any other observations about the impact of COVID-19 on your intervention implementation?

One of the most interesting observations was parent participation in the lessons. Several parents went beyond simply monitoring how their child was doing during the units to actively working out the problems. Some were surprised by the difficulty level of the math problems. One mother jokingly remarked: I thought the math they were going to do was as easy as 5 + 5 = 10. The next time my son might have to be the parent and I might have to be the student. You all make the kids think and I like that.

When COVID-19 shut down your participating schools, how were you able to adjust your data collection to continue with your research?

We used the same problem-solving test that we have administered in several previous studies (Figure 1 shows two of the items). On Day 1 of the study (pre-COVID), students took the math pretest in their resource rooms with pencil and paper. Due to COVID-19 school closures, we mailed the posttest and test administration instructions to student homes. On the scheduled testing day during an online class session, students removed the test from the envelope and followed directions for answering the test questions while we observed remotely. On Days 2 and 3 of the study (pre-COVID), an oral examiner (OE) pretested individual students in person. The OE asked the student questions, prompting the student to describe the overall problem, identify the information needed for solving the problem, indicate how the information related to their problem-solving plan, and provide an answer. Due to COVID-19, students took the oral posttests online. The teacher set up a breakout room in the platform where the OE conducted the oral assessments and a second member of the research team took notes.

Figure 1. Sample Items from the Problem-Solving Test

During the testing sessions, the OE projected each item on the students’ computer screens. Then she asked the student to read the problem aloud and describe how to solve it. The OE used the same problem-solving prompts as was used on the pretests. For problems that involved graphs or charts, the OE used the editing tools to make notations on the screen as the students directed. One challenge is that oral testing online made it more difficult to monitor behavior and keep students on task. For example, sometimes students became distracted and talked to other people in their house.

What were the results of this study of oral assessment in mathematics for students with disabilities?

Our results suggest that allowing students to describe their understanding of problems in multiple ways yielded depth and detail to their answers. We learned from the oral assessment that most students knew how to transfer the data from the table to an approximate location on the graph; however, there was a lack of precision due to a weak understanding of decimals. For item 4 in Figure 1, the use of decimals confused students who did not have much exposure to decimals prior to or during the study. We also found that graphics that were meant to help students understand the text-based items were in some cases misleading. The representation in item 4 was different than the actual ramp and model car activity students experienced virtually. We have used this math test several times in our research and regrettably had no idea that elements of the graphics contributed to misunderstanding.

Unfortunately, our findings suggest that the changes made in response to COVID-19 may have depressed student understanding. Performances on two items (including item 4 in Figure 1) that assessed the main points of the intervention were disappointing compared to results from prior studies. The increase in class size from 3–4 to 14 after COVID and switching to online learning may have reduced the opportunity for repetition and practice. There were reduced opportunities for students to participate in the hands-on activities and participate in conversations about their thinking with other students.

We acknowledge the limitations of this small pilot study to compare knowledge of students when assessed in a pencil and paper format to an oral assessment. We are optimistic about the potential of oral assessments to reveal problem-solving insights of students with math disabilities. The information gained from oral assessment is of value if teachers use it to individualize their instruction. As we learned, oral assessment can also point to areas where graphics or other information are misleading. More research is needed to understand the value of oral assessment despite the increase in time it might add to data collection efforts for students with math disabilities. This experience highlights some of the positive experiences of students learning during COVID-19 virtually at home as well as some of the challenges and risks of reduced outcomes from these virtual learning experiences, especially for students with disabilities.

This blog was written by Sarah Brasiel, program officer for NCSER’s Science, Technology, Engineering, and Math program.