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Conference Paper: Evaluating a general model of adaptive tutorial dialogues

TitleEvaluating a general model of adaptive tutorial dialogues
Authors
KeywordsAdaptive Tutorial Dialogues
Constraint-Based Tutors
Ill-Defined Tasks
Well-Defined Tasks
Issue Date2011
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2011, v. 6738 LNAI, p. 395-402 How to Cite?
AbstractTutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial dialogues in both an ill-defined and a well-defined task. The first study involved dialogues in database design, an ill-defined task. The control group participants received non-adaptive dialogues regardless of their knowledge level and explanation skills. The experimental group participants received adaptive dialogues that were customised based on their student models. The performance on pre- and post-tests indicate that the experimental group participants learned significantly more than their peers. The second study involved dialogues in data normalization, a well-defined task. The performance of the experimental group increased significantly between pre- and post-test, while the improvement of the control group was not significant. The studies show that the model is applicable to both ill- and well-defined tasks, and that they support learning effectively. © 2011 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/179607
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorWeerasinghe, Aen_US
dc.contributor.authorMitrovic, Aen_US
dc.contributor.authorThomson, Den_US
dc.contributor.authorMogin, Pen_US
dc.contributor.authorMartin, Ben_US
dc.date.accessioned2012-12-19T10:00:10Z-
dc.date.available2012-12-19T10:00:10Z-
dc.date.issued2011en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2011, v. 6738 LNAI, p. 395-402en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/179607-
dc.description.abstractTutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial dialogues in both an ill-defined and a well-defined task. The first study involved dialogues in database design, an ill-defined task. The control group participants received non-adaptive dialogues regardless of their knowledge level and explanation skills. The experimental group participants received adaptive dialogues that were customised based on their student models. The performance on pre- and post-tests indicate that the experimental group participants learned significantly more than their peers. The second study involved dialogues in data normalization, a well-defined task. The performance of the experimental group increased significantly between pre- and post-test, while the improvement of the control group was not significant. The studies show that the model is applicable to both ill- and well-defined tasks, and that they support learning effectively. © 2011 Springer-Verlag Berlin Heidelberg.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.subjectAdaptive Tutorial Dialoguesen_US
dc.subjectConstraint-Based Tutorsen_US
dc.subjectIll-Defined Tasksen_US
dc.subjectWell-Defined Tasksen_US
dc.titleEvaluating a general model of adaptive tutorial dialoguesen_US
dc.typeConference_Paperen_US
dc.identifier.emailThomson, D: dthomson@hku.hken_US
dc.identifier.authorityThomson, D=rp00788en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/978-3-642-21869-9_51en_US
dc.identifier.scopuseid_2-s2.0-79959302774en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79959302774&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume6738 LNAIen_US
dc.identifier.spage395en_US
dc.identifier.epage402en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridWeerasinghe, A=14021787000en_US
dc.identifier.scopusauthoridMitrovic, A=7003631144en_US
dc.identifier.scopusauthoridThomson, D=7202586830en_US
dc.identifier.scopusauthoridMogin, P=6508011064en_US
dc.identifier.scopusauthoridMartin, B=7402931502en_US
dc.identifier.citeulike9989804-

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