Community-Sourced Data Driven Open Adaptive Courseware

“Community Sourced, Data-Driven Improvements to Open, Adaptive Courseware” will improve outcomes for STEM learners in targeted courses by deploying and improving open, adaptive courseware. Funded as part of the California Learning Lab effort, this project partners with faculty from Santa Ana College, CSU Fullerton, and UC Berkley to implement and analyze use of open, adaptive courseware. Open courseware such as CMU’s Open Learning Initiative(OLI) and Lumen Learning courseware have proven effective in closing gaps for underrepresented STEM Learners in part by combining multiple approaches (such as course-redesign, active learning, frequent practice, targeted hints and feedback in problem-solving context, careful attention to measurable, student-centered learning objectives, and close alignment between practice and assessment) into their platforms. The course improvements in this project will be driven by three sources – students, faculty and crowd-sourcing – informed by past data on learner use and success. Our project will engage students in the act of learning activity development and improvement involving learners in describing challenges in the learning model and in the creation of new resources.

Co-PIs : Crystal Jenkins (SAC), Nina Robson (CSU Fullerton), Zachary Pardos (UC Berkeley), Lauren Herckis (Carnegie Mellon University)