Graph Mining and Outlier Detection Meet Logic Proof Tutoring

D 2014

Graph Mining and Outlier Detection Meet Logic Proof Tutoring

VACULÍK, Karel, Leona NEZVALOVÁ and Lubomír POPELÍNSKÝ

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/14:00076475

Organization unit

Faculty of Informatics

ISSN

Keywords in English

logic proofs; resolution; educational data mining; graph mining; outlier detection

Abstract

V originále

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.

Citovat

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.

@inproceedings{1198056,
   author = {Vaculík, Karel and Nezvalová, Leona and Popelínský, Lubomír},
   address = {London},
   booktitle = {Proceedings of EDM 2014 Ws Graph-based Educational Data Mining (G-EDM)},
   editor = {Collin F. Lynch, Tiffany Barnes},
   keywords = {logic proofs; resolution; educational data mining; graph mining; outlier detection},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {London},
   pages = {43-50},
   publisher = {CEUR-WS.org},
   title = {Graph Mining and Outlier Detection Meet Logic Proof Tutoring},
   year = {2014}
}

TY  - JOUR
ID  - 1198056
AU  - Vaculík, Karel - Nezvalová, Leona - Popelínský, Lubomír
PY  - 2014
TI  - Graph Mining and Outlier Detection Meet Logic Proof Tutoring
PB  - CEUR-WS.org
CY  - London
KW  - logic proofs
KW  - resolution
KW  - educational data mining
KW  - graph mining
KW  - outlier detection
N2  - 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.
ER  -

VACULÍK, Karel, Leona NEZVALOVÁ and Lubomír POPELÍNSKÝ. Graph Mining and Outlier Detection Meet Logic Proof Tutoring. Online. In Collin F. Lynch, Tiffany Barnes. \textit{Proceedings of EDM 2014 Ws Graph-based Educational Data Mining (G-EDM)}. London: CEUR-WS.org, 2014, p.~43-50. ISSN~1613-0073.

Detailed Information on Publication Record