Over the past several months, our foundation’s grantees have generated cutting-edge research and evidence across our investment areas of early childhood, K-9, and out-of-school STEM. These research projects are aligned with our vision for research, falling into three categories:
- validation of program models
- research aligned to each portfolio’s logic model and priority outcomes, and
- research about cross-cutting topics that have implications for all portfolios
Each area of research adds valuable insights to grantees’ respective fields while bolstering our foundation’s understanding of the education landscape and best practices that can support improvement in key academic and socioemotional outcomes for all children.
Aligned with our core value of “learn better, together,” Overdeck Family Foundation is committed to promoting transparent research practices by lifting up timely findings. We hope that other organizations and funders can then use this data to inform strategic decision making and investment for the future.
Below are the results of seven grantee research studies that concluded at the end of this year.
Can Khan Academy’s MAP Accelerator improve math scores?
Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and out of the classroom. Their web-based tool MAP Accelerator, developed with NWEA, uses MAP Growth scores to automatically generate a personalized, mastery-based math learning plan for each student.
To better understand the relationship between the time students spend on MAP Accelerator and their MAP Growth Assessment scores, Khan Academy partnered with NWEA to conduct a correlational study. The study looked at math scores for students in third through eighth grades across 99 school districts during the COVID-disrupted 2020-21 school year.
Results of the study indicate that students who used MAP Accelerator for 30 or more minutes a week throughout the school year showed greater-than-expected growth, on average, on their MAP tests compared to normative pre-pandemic growth. Key findings include:
- On average, students in classrooms that engaged with MAP Accelerator at the recommended dosage of 30 minutes per week exceeded growth projections by nine to 43 percent, depending on grade level.
- Trends were consistent across grades and student demographic subgroups, such as race/ethnicity, gender, and the proportion of students in a school eligible for free/reduced-price lunch.
Looking ahead, Khan Academy and NWEA will explore options to build on these findings with further analyses to evaluate whether the MAP Growth patterns observed in the first year of implementation persist in subsequent years.
Does use of TalkingPoints impact student academic outcomes and attendance?
TalkingPoints is an education technology nonprofit that supports school districts in connecting families and teachers. Through their multilingual platform, TalkingPoints leverages two-way translated communication and personalized content to facilitate meaningful family-school partnerships.
To better understand the impact of the platform, the organization conducted an evaluation to measure if TalkingPoints usage impacted student academic outcomes and attendance, and whether the impact varied across different student groups. The study looked at data from a large U.S. urban school district over a multi-year period, drawing from a sample of more than 30,000 students and 2,000 teachers, comparing schools that had adopted TalkingPoints to schools that had not.
Researchers found that students in schools that had adopted TalkingPoints experienced improvements in academic achievement, and that the effects were more pronounced for traditionally underserved students, specifically Black and Latino students, students with disabilities, and English language learners. District schools that had adopted TalkingPoints also experienced an overall decrease of 15 percent in student absenteeism compared to their counterparts. Similarly, the effects on absenteeism were larger for Black and Latino students, students with disabilities, and English language learners.
These findings suggest that supporting families and teachers in building effective partnerships through TalkingPoints can have a positive impact on student outcomes, narrowing educational gaps for all students.
Logic model & priority outcomes research
Learning from educators’ experiences implementing edtech
The EdTech Evidence Exchange (the Exchange) is dedicated to improving edtech decision-making in districts by elevating educator experiences with implementing specific technologies in the context of their district. Collecting and sharing this data provides edtech purchasers and school leaders with more information on their district context and how teachers in the district perceive the success of edtech in their classrooms. In 2021, the EdTech Evidence Exchange officially launched its inaugural evidence exchange cohort for the 2021-22 school year, in partnership with the Alabama State Department of Education (ALSDE), the Nevada Department of Education (NDE), and the Utah STEM Action Center. The Exchange assembled an advisory board in each state to help reach Pre-K-12 math educators and decision makers and to use collected evidence to inform edtech selection and implementation in each state.
Over the span of 14 months, the cohort worked with the Exchange research team to collect data from 1,220 educators representing 35 school districts and 26 charter schools. Together, they analyzed the data and generated state- and district- level reports ahead of a one-day virtual convening where representatives from the three states gathered to:
- Review state insights on edtech implementation contexts and experiences.
- Build relationships to support math edtech implementation within their states and across the other participating states.
- Identify how evidence can best support their edtech decision-making processes.
As a result of this collaborative work, the Exchange summarized their findings in a report. Among the key takeaways, they identified that an important ingredient to edtech implementation success is state, district, and school level buy-in and commitment to participation. “It is critical to ensure that leaders understand the value of the time investment to participate in the EdTech Evidence Exchange and are willing to share this message with educators across the district. This buy-in needs to be established in initial meetings and fostered throughout the partnership. It is time-intensive but worth it.”
Using the lessons learned from the first cohort, the Exchange will welcome a second cohort during the 2022-23 school year to continue to engage district and state leaders in deep conversations about the decision-making process for edtech selection and implementation, as well as to understand how evidence from the Exchange Platform supports more evidence-based edtech purchasing.
Understanding the role of social interaction in language development
University of Washington’s Institute for Learning & Brain Sciences (I-LABS) is a leading research center that adds to the fields of early childhood brain, language, and cognitive development by highlighting new evidence-based practices and potential avenues for family intervention. One of the I-LABS studies funded by Overdeck Family Foundation focuses on understanding how social interaction affects learning. From previous research, I-LABS found that infants require social interaction to learn the sounds and words of a foreign language, which led researchers to hypothesize that social interaction “gated” early language learning.
In a new, first-of-its-kind experiment, I-LABS conducted a study to understand “inter-brain synchrony” (the rate at which neurons fire in two interacting brains) between mothers and their five-year-old children using a first-in-the world laboratory setup with two magnetoencephalography (MEG) brain-imaging devices. Through this process, researchers found increased synchronization in specific areas of the brain during socially interactive verbal exchanges compared to the nonsocial control condition. It is possible that these areas in the brain are “cortical hubs” (areas that have a high number of interbrain connections) and may have the potential to serve as a neural marker of social verbal interaction.
These findings bring new insights in understanding how the brain reacts to interactive behaviors in mother-child dyads, highlighting the essential role of social interaction in language learning. I-LABS will further build on these findings by exploring “inter-brain synchrony” during face-to-face social interactions between both mother and child.
Unlocking acceleration for literacy through grade-level work
TNTP, a national nonprofit, aims to advance policies and practices that ensure effective teaching in every classroom. The organization partnered with ReadWorks, a free digital English Language Arts resource, to analyze trends in teachers’ use of grade-level assignments for over three million students who frequently used the ReadWorks platform during the 2018-19 through 2020-21 school years. TNTP’s analysis of the data found that:
- Students are spending even more time on below grade-level work than they were before the pandemic.
- Students were just as successful on grade-level work as they were on below grade-level work.
- Students in schools serving more historically marginalized communities—particularly students experiencing poverty—were assigned the most below grade-level work.
- In schools serving more students in poverty, students received less access to grade-level work, even when they’d already shown they can master it.
These trends confirm the findings in TNTP’s 2021 study, Accelerate, Don’t Remediate, which revealed that inequitable access to grade-level work has only deepened during the pandemic. The new results also reinforce the finding that students at all academic levels are better served through grade-level work, and that schools should focus on learning acceleration versus remediation to better support all students in the wake of the pandemic. While this study looked at acceleration practices for reading, TNTP’s previous study found similar trends for math.
Accelerating math learning for all students
Zearn, a top-rated online math learning platform, is used by one in four elementary school students nationwide. In a recent study, Zearn sought to understand how teachers helped students catch up and move forward in math during the pandemic. Were they going back and redoing the months of disrupted work, or were they moving ahead with the next grade’s work with some built-in review? The Zearn team examined data from 600,000 elementary- and middle-school students across all 50 states from the 2020-21 and 2021-22 school years to see which method was most effective at helping students get to grade-level.
They found promising evidence that supports acceleration versus remediation tactics. Notably:
- When a student was consistently accelerated by their teacher, they completed twice the amount of grade-level lessons and struggled 17 percent less in their math learning.
- A student enrolled in a majority Black, Latino, or low-income school was more likely to be remediated when compared with their peers in a majority White or high-income school.
- The positive impact of learning acceleration was consistent across student subgroups.
As the education sector works collectively to help all students get back on track, this research study provides encouraging evidence that grade-level learning and acceleration can help all students catch up and move forward.
Cross-cutting topic research
Quantifying the role of social capital in income mobility
Based out of Harvard University, Opportunity Insights is a team of researchers and policy analysts that conduct scientific research on how to improve upward mobility and then work collaboratively with local stakeholders to translate research findings into policy change. In August 2022, Opportunity Insights published results of a new study focused on the role of social capital in economic mobility. The team analyzed a privacy-protected data set—the largest of its kind—of the social networks of 70.3 million Facebook users between the ages of 25-44 years old, specifically looking at 81 billion friendship connections. The team measured:
- Economic connectedness: The degree of interaction between low- and high-income people
- Cohesiveness: The degree to which social networks are fragmented into cliques
- Civic engagement: Rates of volunteering and participation in community organizations
Through these connection measurements, they found that, for children who grow up in low-income families, living in an area where people have more friendships that cut across class lines (economic connectedness) significantly increases how much they earn in adulthood. Additional key findings include:
- Social networks are highly stratified by socioeconomic class, with higher income people tending to have higher income friends.
- Other forms of social capital, for instance how tight-knit a community or friendship group is, are not strongly associated with economic mobility.
- Differences in economic connectedness can explain the relationship between upward mobility and other factors such as high poverty rates.
- The social disconnection by class is due in equal part to segregation by income across social settings and “friending bias,” the tendency for people to befriend people who are similar to them.
- Both exposure and friending bias are shaped by the structure of institutions and policies, such as college admissions policies and zoning laws.
These findings illustrate how critical it is to provide opportunities for children to build wide-ranging social relationships at an early age. Using this data, available in a digital Social Capital Atlas, researchers and policy makers can learn from areas that display high social capital to target future interventions to communities with lower levels. This new data can also serve as a starting point for researchers to explore what types of social capital matters most for other key outcomes that shape people’s lives.
Thank you to Irene Chen, Pete Lavorini, Katie Lim, Paula Longoria, and Jon Sotsky for your contributions to this blog post.
Header image courtesy of Zearn