HarvardX has partnered with TutorGen to pilot on a MOOC an adaptive learning and assessment algorithm in order examine the effects on learning outcomes, engagement and course drop-out rates.
The pilot study has been applied on the redesigned Super-Earths and Life course. Specifically, this adaptivity functionality has been implemented in four out of 16 graded subsections. “The order is determined by a personalized learning progression, using learners’ real-time performance and statistical inferences on sub-topics they have mastered. The inferences are continuously updated based on each learner’s’ performance,” explains HarvardX.
TutorGen’s adaptive engine called SCALE (Student Centered Adaptive Learning Engine, providing a variety of the Bayesian Knowledge Tracing algorithm) decides which problem to serve next based on the list of learning objectives covered by the homework and course material and the student’s current mastery of those learning objectives.
HarvardX developed an LTI tool to integrate TutorGen’s SCALE into the edX platform (see the user interface above). Additionally, HarvardX tripled the existing content in the four adaptive subsections, investing around 200 hours.