Personalized multi-student improvement based on Bayesian cybernetics

Authors:
Vassilis G. Kaburlasos;Catherine C. Marinagi;Vassilis Th. Tsoukalas
Affiliations:
Technological Educational Institution of Kavala, Department of Industrial Informatics, GR-65404 Kavala, Greece;Technological Educational Institution of Halkida, Department of Logistics, GR-32200 Thiva, Greece;Technological Educational Institution of Kavala, Department of Industrial Informatics, GR-65404 Kavala, Greece
Venue:
Computers & Education
Year:
2008

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Abstract

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 4years 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.