From chatbots to dashboards to avatars, LEVI teams are using learning engineering to improve middle
school math outcomes for low-income students.

Our Affiliates

Rice University-OpenStax

The team will use machine learning to identify types of errors students make and provide just-in-time feedback and interventions. The project will categorize errors that arise due to challenges in reading comprehension, computation, and reduced engagement. The model will also trigger teacher-crafted supports based on the error type. Student errors are communicated with teachers in real-time and teachers are provided with professional development to effectively respond.

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A compelling way to close learning gaps is to support schools with a cohort of high-quality peer tutors. Schoolhouse world is already a free, online tutoring platform powered by 3k+ volunteer peer tutors that serve 25k+ learners. This project will utilize tutors in a school-centric model and leverage machine learning algorithms that improve tutor efficacy and learner assessments to double the rate of math progress for low-income middle school students.

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