Emma Brunskill
Associated IES Content
Grant
Use of Machine Learning to Adaptively Select Activity Types and Enhance Student Learning with an Intelligent Tutoring System
When preparing instructional materials and lesson plans, teachers and instructional designers choose from an almost overwhelming set of student activity types. A fundamental problem in education is determining what combinations and sequences of activity types are most effective in supporting student learning. This research team hypothesizes that significant gains in robust learning are possible by careful selecting among a diverse set of activity types. To address this issue, the researchers...
Federal funding program:
Award number:
R305A130215