Backed by Learning Science Research and Powered by Data-Driven Intelligence.
edapt platform was built from the ground up based on proven research-backed strategies and learning techniques coupled with data-driven intelligence to deliver an unparalleled personalized mastery-learning experience as effective as one-to-one tutoring.
Learning Science RESEARCH
a.k.a. “Learning for mastery”, students are required to master each concept before moving to dependent concepts. Mastery learning focuses on the role of feedback in learning and encourages students to learn at their own pace as they master skills and progress toward learning goals.
Through painstaking implementation and refinement, edapt platform incorporated the most impactful mastery learning techniques into its core pedagogy including 1:1 instruction, reinforcement, feedback-corrective mastery learning, cues and explanations, initial cognitive prerequisites and independent intervention.
"The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring." - Bloom, B. S. (1984).
Learning science RESEARCH
Identifying knowledge gaps and delivering instructional scaffolds is critical and educationally consequential. Scaffolding is a seamless and timely process that provides students with support until they can apply new skills and strategies independently. As they begin to demonstrate mastery, the assistance or support is gradually decreased to shift the responsibility for learning to the students.
edapt platform's pedagogy uses our proprietary "Recursive Bi-Directional Scaffolding" mechanism to deliver highly effective ways of identifying and closing knowledge gaps through seamless scaffolding and just-in-time remediation.
"Using Scaffolded Instruction To Optimize Learning." - Larkin, Martha (2002).
Our Research Advisors.
The pedagogy powering our platform was developed in close collaboration with our research advisors from Stanford University.
Dr. Emma Brunskill
Dr. Brunskill is a computer science professor at Stanford where she is part of the AI lab and Statistical Machine Learning group. Her research focuses on reinforcement learning in high stakes scenarios.
Dr. Karin Forssell
Dr. Forssell is the program director for the Learning, Design and Technology program at Stanford. Her research focuses on the choices people make in learning about and using new digital tools.
Dr. Rob Tibshirani
Dr. Tibshirani is a professor of statistics at Stanford where he leads research in data science and statistics. His research focuses on dealing with large amounts of data and separating out consistent patterns from the noise.