2022-2023 Winner


United States of America
Learning Science Research
Award Level:
Catalyst Award

Project Description

consistentlyBetter is a tool that identifies students who are unusually inconsistent in their academic achievement over the course of a school year or more and who might benefit from targeted interventions to enhance and stabilize their academic performance. Depending on the available data, the tool can also identify situations or conditions that are linked to periods of higher and lower consistency in academic achievement. consistentlyBetter is built around a statistical package that is accessible either as a standalone R library for scientific use, a user-friendly web-application, or an LMS integrated app that provides teachers with student-level predictions on the consistency of their academic achievement. The R-package is ideally suited for learning engineering and A/B testing and is geared toward scientists while the LMS integrated app is a meant for teachers who seek suggestions on students that might benefit from additional support.

Meet Our Team

Philippe Rast

Professor, University of California Davis

Josue Rodriguez

Graduate Student at University of California Davis and Data Scientist at McGraw Hill

Donald Williams


Meet the finalists for the Building an Adaptive & Competitive Workforce track here. Meet the finalists for all other tracks here.