VACULÍK, Karel and Lubomír POPELÍNSKÝ. Graph Mining for Automatic Classification of Logical Proofs. In Susan Zvacek, Maria Teresa Restivo, James Uhomoibhi and Markus Helfert. 6th International Conference on Computer Supported Education - CSEDU 2014. Portugal: 2014 SCITEPRESS – Science and Technology Publications, 2014, p. 268-275. ISBN 978-989-758-020-8.
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Basic information
Original name Graph Mining for Automatic Classification of Logical Proofs
Authors VACULÍK, Karel (203 Czech Republic, belonging to the institution) and Lubomír POPELÍNSKÝ (203 Czech Republic, guarantor, belonging to the institution).
Edition Portugal, 6th International Conference on Computer Supported Education - CSEDU 2014, p. 268-275, 8 pp. 2014.
Publisher 2014 SCITEPRESS – Science and Technology Publications
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
Publication form storage medium (CD, DVD, flash disk)
RIV identification code RIV/00216224:14330/14:00076476
Organization unit Faculty of Informatics
ISBN 978-989-758-020-8
Keywords in English graph mining; frequent subgraphs; logic proofs; resolution; classification; educational data mining
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Karel Vaculík, Ph.D., učo 256512. Changed: 16/9/2014 16:39.
Abstract
We introduce graph mining for evaluation of logical proofs constructed by undergraduate students in the introductory course of logic. We start with description of the source data and their transformation into GraphML. As particular tasks may differ---students solve different tasks---we introduce a method for unification of resolution steps that enables to generate generalized frequent subgraphs. We then introduce a new system for graph mining that uses generalized frequent patterns as new attributes. We show that both overall accuracy and precision for incorrect resolution proofs overcome 97%. We also discuss a use of emergent patterns and three-class classification (correct/incorrect/unrecognised).
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