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Personalized multi-student improvement based on Bayesian cybernetics

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Computers & Education Volume 51, Number 4, ISSN 0360-1315 Publisher: Elsevier Ltd


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.


Kaburlasos, V.G., Marinagi, C.C. & Tsoukalas, V.T. (2008). Personalized multi-student improvement based on Bayesian cybernetics. Computers & Education, 51(4), 1430-1449. Elsevier Ltd. Retrieved June 26, 2022 from .

This record was imported from Computers & Education on February 1, 2019. Computers & Education is a publication of Elsevier.

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