Reducing inequality in academic outcomes through personalized learning

Does high dosage tutoring at scale reduce academic inequalities by race, ethnicity, and economic standing?

Extant literature has documented the many ways in which the pandemic exacerbated inequalities in academic outcomes, particularly for students of color and students from low-income families. High-dosage tutoring (HDT) has been shown to be a highly effective strategy to improve academic outcomes but is often resource-prohibitive for many school districts. A promising strategy for scaling this model at cost is sustainable high-dosage tutoring (SHDT), which alternates in-person tutoring with high-quality computer-assisted learning. Bhatt and colleagues will examine data from a large-scale randomized controlled trial to test whether HDT and SHDT have similar effects in reducing academic inequalities for Black, Latinx, and Native American students and students from low-income families. The team will examine dosage data in each site, as well as surveys with tutors and site coordinators about intervention content, program delivery, and facilitators and barriers. They will also estimate intent-to-treat and treatment-on-the treated effects on pooled data across school district sites, grades, and subject. To examine whether and how HDT and SHDT reduce inequalities, the team will separate impact estimates for: free-and-reduced price lunch eligible students versus those who are not eligible, and students by race and ethnic background. Findings will provide evidence on scaling a promising intervention to address academic inequalities.

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