Summer Learning Isn’t One Size Fits All
Data enables schools to identify areas of opportunity and tailor summer learning programs likely to have the greatest impact on students.
Schools can use data when determining eligibility for summer learning programs to make sure students with the most need for summer learning are enrolled.
By: Dr. Joy Smithson
After three years of COVID-impacted classrooms, many school districts are expanding summer programs to provide remediation and enrichment opportunities for students. Summer school, once reserved for relatively few students needing to catch up in certain subjects, is now expanding in many schools as more students and parents are concerned about disrupted learning. The U.S. Department of Education is supporting this effort with funding to meet the demand.
As districts work to spend this money judiciously, many will tailor summer programs to address their most vulnerable students. All students are impacted to some degree when school is out for the summer, but opportunity gaps for historically marginalized students tend to get exacerbated during this break. According to NWEA’s research, some groups of students such as English language learners, students with disabilities, and rural students can experience greater loss when school is out for the summer. In other words, these students can enter school in the fall further behind peers with whom they were more academically aligned just the spring prior. Researchers recommend that districts use data to determine the best summer learning interventions.
Many educators are using district, school, and student-level data to get a clearer picture of how their specific students are impacted and which students would benefit most from summer learning programs.
As a data scientist, I work with K-12 schools to help them use data to inform efforts aimed at helping students. Here are three takeaways from my work with schools that can help educators use data to inform and implement summer learning plans.
1. Data enables schools to identify specific focus areas for learning programs
Aggregated student data can help districts spot overall areas in schools, grade levels, subpopulations, and subjects where students are thriving and where students need additional support. Longitudinal data informs whether changes are occurring over time in the areas that need attention. Multiple types of longitudinal data enable further analysis such as measuring the relationship between variables. Although district administrators might have access to such data, it’s often stored in multiple silos, making it difficult and time-consuming to merge and analyze.
Districts need actionable data that is updated, visual, and accessible. Instead of having data in isolated departments or in systems that are not user-friendly, many schools are adopting systems that allow educators to create interactive, custom data dashboards that include aggregated and disaggregated data. The data itself doesn’t provide a resolution, but specific knowledge about the percent of students failing, missing an inordinate amount of classes, and/or struggling behaviorally and academically removes subjective bias about the state of the district. Administrators might assume they don’t have an attendance problem, for instance, until data paints a more informative picture.
When planning summer learning programs, schools should look at the data specific to the areas they hope to target for improvement. It’s important to look at differences between groups and also within groups. For instance, a district-level report might indicate that a high percentage of high school students have behavioral problems, but further analysis might reveal that one high school or grade level accounts for a disproportionate amount of the total infractions. Interactive dashboards enable this analysis and comparison directly.
2. Schools can use data to set criteria to identify students with the most need for summer learning and ensure those students are enrolled
Summer learning is most valuable for students when learning plans are tailored to address specific needs. Some data-informed districts are hand-selecting students to recommend for summer school using rubrics they have created after examining data trends in the district. For instance, administrators might create a rubric to identify students in need of reading assistance over the summer. Perhaps the rubric assigns 0 – 5 points to students based on their reading benchmark performance and course grades, where higher values indicate the need for a reading intervention. Students get one point for each benchmark (taken fall, winter, and spring) in which they perform below grade level. Additionally, one point is assigned if a student is earning a 70 or below in any course and two points if earning a 70 or below in multiple courses. Students with a score of 5 aren’t doing well on their benchmarks or in their courses. If too few or too many students are identified using these criteria, adjustments can be made to the rubric.
This intentional approach is made possible by technology that ensures teachers have access to relevant data in a timely manner. Using the reading assistance rubric, aggregate information such as the total number and percentage of students with a maximum score of 5 or knowing the average reading rubric score across all students in the class would indicate how the overall class is doing in a quick snapshot. Student-level information, indicating each student’s reading rubric score, would help teachers identify immediately where an individual falls on the rubric. This is just one example of how districts can use data to inform which students are most suited for summer learning programs and target those programs where they are most needed.
3. Data-informed communication can help educators and parents enhance each other’s contributions to supporting students’ learning
Engaging parents and students’ support systems at home to support summer learning plans is key to their success. Having data available as part of conversations with parents and caregivers offers insight to parents regarding the decision-making process at the school level. Likewise, caregivers have the opportunity to share the learning practices the family engages with at home, and co-create the learning environment to which their child is exposed.
Evidence suggests that efforts to engage parents in conversations regarding age-appropriate learning help build trust between parents and educators. Communication is most effective when it is established early, regularly, and when it prioritizes home-based parent engagement in students’ learning. When communication is established early and regularly, both educators and parents report more confidence in their communication together and students report greater feelings of belonging at school. All of these factors contribute to students’ success.
Using a variety of data points to help students is the best approach for creating successful summer learning plans. Data that is multi-faceted, timely, and interactive enables educators and parents to have informed conversations about students’ learning paths. Students’ needs over the course of a year may change; open lines of communication with parents and access to dynamic data are critical tools for ensuring summer learning plans are tailored to meet students’ and schools’ most pressing needs.
Dr. Joy Smithson is a data scientist with SchoolStatus.
I agree that summer learning is important. I feel that especially since students are very behind we need to cater the learning to meet the needs of the students. My question is, How do we get young students to buy-in to summer learning?
I agree with the article that summer school is not one size fits all. The problem I see is that a lot of teachers do not know how to analyze data. They also do not know what to do with the data. There should be time given for teachers to take training on how to analyze data.
Leave a Comment
Your email address will not be published. All fields are required.