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.
Philippe Rast
Professor, University of California Davis
Philippe is a Quantitative Psychologist who focuses on the development of statistical methods and software to capture longitudinal changes in human behavior. Specifically, he focuses on models that capture changes in the level and consistency of cognitive performance over time, both in individuals and in populations. His work in this area has been supported by major funding agencies (such as NIH) and has been adopted in a wide range of research areas ranging from Education, Neuroscience to Econometrics.
