Personalized multi-student improvement based on Bayesian cybernetics

This work presents innovative cybernetics (feedback) techniques based on Bayesian statistics for drawing questions from an Item Bank towards personalized multi-student improvement. A novel software tool, namely Module for Adaptive Assessment of Students (or, MAAS for short), implements the proposed (feedback) techniques. In conclusion, a pilot application to two Computer Science courses during a period of 4 years demonstrates the effectiveness of the proposed techniques. Statistical evidence strongly suggests that the proposed techniques can improve student performance. The benefits of automating a quicker delivery of University quality education to a large body of students can be substantial as discussed here.