Designing Adaptive Assessments in MOOCs

Designing Adaptive Assessments in MOOCs

Yigal Rosen, Ilia Rushkin, Andrew Ang, Colin Fredericks, Glenn Lopez, Mary Jean Blink, and Dustin Tingley

There is an indisputable need for evidence-based instructional designs that create the optimal conditions for learners with different knowledge, skills and motivations to succeed in MOOCs. The study explores the technological feasibility and implications of adaptive functionality to course (re)design in the edX platform. Additionally, the study aims to establish the foundation for future study of adaptive functionality in MOOCs on learning outcomes, engagement and course drop-out rates. Preliminary findings suggest that the adaptivity of this kind leads to a higher efficiency of learning: students go through the course faster and attempt fewer problems, since the problems are served to them in a targeted way. And yet there is no evidence that the students’ overall performance in the course suffers. Further research is needed to explore additional facets of adaptive assessment in different contexts of MOOCs and the effects on learning outcomes.