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...

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Bibliographic Details
Published in:Computers and education 2008-12, Vol.51 (4), p.1430-1449
Main Authors: Kaburlasos, Vassilis G., Marinagi, Catherine C., Tsoukalas, Vassilis Th
Format: Article
Language:English
Subjects:
Adaptive Testing
Architectures for educational technology systems
Bayesian Statistics
College Instruction
Computer Assisted Instruction
Computer Assisted Testing
Computer Science
Computer Software
Cybernetics
Educational Technology
Feedback (Response)
Instructional Design
Intelligent Tutoring Systems
Interactive learning environments
Item Banks
Student Improvement
Teaching/learning strategies
Undergraduate Students
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Summary: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.
ISSN:0360-1315
1873-782X
DOI:10.1016/j.compedu.2008.01.004