VACULÍK, Karel, Leona NEZVALOVÁ and Lubomír POPELÍNSKÝ. Graph Mining and Outlier Detection Meet Logic Proof Tutoring. Online. In Collin F. Lynch, Tiffany Barnes. Proceedings of EDM 2014 Ws Graph-based Educational Data Mining (G-EDM). London: CEUR-WS.org, 2014, p. 43-50. ISSN 1613-0073.
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Basic information
Original name Graph Mining and Outlier Detection Meet Logic Proof Tutoring
Authors VACULÍK, Karel (203 Czech Republic, belonging to the institution), Leona NEZVALOVÁ (203 Czech Republic, belonging to the institution) and Lubomír POPELÍNSKÝ (203 Czech Republic, guarantor, belonging to the institution).
Edition London, Proceedings of EDM 2014 Ws Graph-based Educational Data Mining (G-EDM), p. 43-50, 8 pp. 2014.
Publisher CEUR-WS.org
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/14:00076475
Organization unit Faculty of Informatics
ISSN 1613-0073
Keywords in English logic proofs; resolution; educational data mining; graph mining; outlier detection
Tags International impact, Reviewed
Changed by Changed by: RNDr. Karel Vaculík, Ph.D., učo 256512. Changed: 16/9/2014 16:30.
Abstract
We introduce a new method for analysis and evaluation of logic proofs constructed by undergraduate students, e.g. resolution or tableaux proofs. This method employs graph mining and outlier detection. The data has been obtained from a web-based system for input of logic proofs built at FI MU. The data contains a tree structure of the proof and also temporal information about all actions that a student performed, e.g. a node insertion into a proof, or its deletion, drawing or deletion of an edge, or text manipulations. We introduce a new method for multi-level generalization of subgraphs that is useful for characterization of logic proofs. We use this method for feature construction and perform class-based outlier detection on logic proofs represented by these new features. We show that this method helps to find unusual students' solutions and to improve semi-automatic evaluation of the solutions.
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