DocumentCode
2896452
Title
BAUT: A Bayesian Driven Tutoring System
Author
Tan, Song ; Qian, Kai ; Fu, Xiang ; Bhattacharya, Prabir
fYear
2010
fDate
12-14 April 2010
Firstpage
476
Lastpage
481
Abstract
This paper presents the design of BAUT, a tutoring system that explores statistical approach for providing instant project failure analysis. Driven by a Bayesian Network (BN) inference engine, BAUT analyzes the test cases failed by a student project submission and provides instant diagnosis that guides students in identifying and removing software bugs. Using a parameter learning process, BAUT is able to improve the quality of its analysis. The initial case study with the prototype demonstrates the potential of the system.
Keywords
belief networks; computer viruses; fault diagnosis; inference mechanisms; intelligent tutoring systems; system recovery; BAUT; Bayesian network inference engine; fault diagnosis; instant project failure analysis; parameter learning process; software bugs; student project submission; tutoring system; Application software; Automatic testing; Bayesian methods; Computer bugs; Computer science; Databases; Engines; Failure analysis; Prototypes; Software prototyping; Automated Tutoring; Network; Testing; Verification; Web Application;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technology: New Generations (ITNG), 2010 Seventh International Conference on
Conference_Location
Las Vegas, NV
Print_ISBN
978-1-4244-6270-4
Type
conf
DOI
10.1109/ITNG.2010.28
Filename
5501731
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