Adaptive Learning Open Source Initiative for MOOC Experimentation

Abstract

In personalized adaptive systems, the learner’s progress toward clearly defined goals is continually assessed, the assessment occurs when a student is ready to demonstrate competency, and supporting materials are tailored to the needs of each learner. Despite the promise of adaptive personalized learning, there is a lack of evidence-based instructional design, transparency in many of the models and algorithms used to provide adaptive technology or a framework for rapid experimentation with different models. ALOSI (Adaptive Learning Open Source Initiative) provides open source adaptive learning technology and a common framework to measure learning gains and learner behavior. This paper provides an overview of adaptive learning functionality developed by Harvard and Microsoft in collaboration with edX and other partners, and shared results the recent deployment in Microsoft MOOC on edX. The study explored the effects of two different strategies for adaptive problems (i.e., assessment items) on knowledge and skills development. We found that the implemented adaptivity in assessment, with emphasis on remediation is associated with a substantial increase in learning gains, while producing no big effect on the drop-out. Further research is needed to confirm these findings and explore additional possible effects and implications to course design.

R. Rubin—Independent.