article

Multistrategy Discovery and Detection of Novice Programmer Errors

Authors:

Raymund C. Sison,

Masayuki Numao,

Masamichi ShimuraAuthors Info & Claims

Machine Learning, Volume 38, Issue 1-2

Pages 157 - 180

https://doi.org/10.1023/A:1007690108308

Published: 01 January 2000 Publication History

Abstract

Detecting and diagnosing errors in novice behavior is an important student modeling task. In this paper, we describe MEDD, an unsupervised incremental multistrategy system for the discovery of classes of errors from, and their detection in, novice programs. Experimental results show that MEDD can effectively detect and discover misconceptions and other knowledge-level errors that underlie novice Prolog programs, even when multiple errors are enmeshed together in a single program, and when the programs are presented to MEDD in a different order.

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Tsiriga VVirvou M(2019)A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring SystemsUser Modeling and User-Adapted Interaction10.1023/B:USER.0000043396.14788.cc14:4(289-316)Online publication date: 1-Jun-2019
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Elgamal AAbas HBaladoh E(2018)An interactive e-learning system for improving web programming skillsEducation and Information Technologies10.1007/s10639-011-9175-718:1(29-46)Online publication date: 24-Dec-2018
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Guzmán EConejo RGálvez J(2010)A data-driven technique for misconception elicitationProceedings of the 18th international conference on User Modeling, Adaptation, and Personalization10.1007/978-3-642-13470-8_23(243-254)Online publication date: 20-Jun-2010
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Multistrategy Discovery and Detection of Novice Programmer Errors

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Copyright © Copyright © 2000 Kluwer Academic Publishers.

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Published: 01 January 2000

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Cited By

Tsiriga VVirvou M(2019)A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring SystemsUser Modeling and User-Adapted Interaction10.1023/B:USER.0000043396.14788.cc14:4(289-316)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1023/B%3AUSER.0000043396.14788.cc
Elgamal AAbas HBaladoh E(2018)An interactive e-learning system for improving web programming skillsEducation and Information Technologies10.1007/s10639-011-9175-718:1(29-46)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s10639-011-9175-7
Guzmán EConejo RGálvez J(2010)A data-driven technique for misconception elicitationProceedings of the 18th international conference on User Modeling, Adaptation, and Personalization10.1007/978-3-642-13470-8_23(243-254)Online publication date: 20-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13470-8_23
Iglesias AMartínez PAler RFernández F(2009)Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement LearningApplied Intelligence10.1007/s10489-008-0115-131:1(89-106)Online publication date: 1-Aug-2009
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Amershi SConati CChin DZhou MLau TPuerta A(2007)Unsupervised and supervised machine learning in user modeling for intelligent learning environmentsProceedings of the 12th international conference on Intelligent user interfaces10.1145/1216295.1216315(72-81)Online publication date: 28-Jan-2007
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Neri F(2000)Multi Level Knowledge in Modeling Qualitative PhysicsLearningMachine Language10.1023/A:100764222514638:1-2(181-211)Online publication date: 1-Jan-2000
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