For students from families that have been hardest hit by the economic impact of the coronavirus pandemic, the loss of resources and opportunities to learn go well beyond a traditional summer break and only serve to widen the achievement gap.
New data for the new normal
When dealing with the COVID slide, it’s not enough to anticipate that students will be behind one grade level on average. It will be necessary to capture the extent of each student’s learning loss and how it manifests across different groups.
That will require integrating data that has never been analyzed together before, such as:
● School opening and closing dates
● Available and implemented online and blended learning programs, including interventions
● Student access to online programs, devices, broadband internet, and other connectivity data
● Student and parent surveys
● Formative and summative assessment data
Once collected, data can be parsed, compared, and analyzed to help make better, more informed decisions that benefit systems, schools, and individual students alike. Valuable diagnostic information about past practices and predicted success probabilities for students at various academic milestones will help leaders to make proactive, sound choices for the future.
Educators can use these data sources to explore COVID learning loss among student groups in various ways, including:
● Identifying whether specific student groups experienced more COVID learning loss than other student groups, to target interventions and relief funding.
● Identifying any differences in patterns of COVID learning loss across different grades and subjects, which could inform strategies about how to help students regain ground.
● Incorporating data based on access to online learning environments, online learning usage, hybrid learning opportunities, and data about school closures. Program evaluation results could inform resource allocation to mitigate the impact of the pandemic and support strategic decisions about what instruction might look like during future interruptions.
● Identifying successful exemplars among schools that may have implemented strategies that limited COVID learning loss or limited inequities in COVID learning loss.
● Identify whether differences between expectations and actual results align with past trends. For example, if certain student groups were more likely to fall short of expected performance from 2018-19 to 2020-21, were these same patterns present in comparisons from previous years, such as from 2016-17 to 2018-19? These results could help to separate out the potential impacts of the pandemic and could identify additional patterns in student achievement data that could inform instructional approaches and allocation of resources.
The future of education
Right now, we are not only seeing the transformation of our educational system, we are actively making the decisions that will shape the future of education for children across the country. In this new and rapidly evolving environment, it is imperative that schools, districts, and states uncover and address unbalanced educational opportunities. While data analysis itself may not guarantee equitable outcomes, using data to make proactive decisions and more strategically distribute resources can illuminate a clear path forward in the face of this pandemic.
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