Educational data mining for analysis of students’ solutions

D 2014

Educational data mining for analysis of students’ solutions

VACULÍK, Karel, Leona NEZVALOVÁ and Lubomír POPELÍNSKÝ

Basic information

Original name

Educational data mining for analysis of students’ solutions

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, Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014, p. 150-161, 12 pp. 2014

Publisher

Springer

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

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/14:00076477

Organization unit

Faculty of Informatics

ISBN

978-3-319-10553-6

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-10554-3_14

Keywords in English

educational data mining; logic proofs; clustering; outlier detection; sequence mining

Abstract

V originále

We introduce a novel method for analysis of logic proofs constructed by undergraduate students that employs sequence mining for manipulation with temporal information about all actions that a student performed, and also graph mining for finding frequent subgraphs on different levels of generalisation. We show that this representation allows to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent on behavioural patterns and we are able to find clusters of erroneous solutions. We also observed significant dependence between time duration and an appearance of the most serious error.

Citovat

VACULÍK, Karel, Leona NEZVALOVÁ and Lubomír POPELÍNSKÝ. Educational data mining for analysis of students’ solutions. Online. In Gennady Agre, Pascal Hitzler, Adila A. Krisnadhi, Sergei O. Kuznetsov. Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014. London: Springer, 2014, p. 150-161. ISBN 978-3-319-10553-6. Available from: https://dx.doi.org/10.1007/978-3-319-10554-3_14.

@inproceedings{1198062,
   author = {Vaculík, Karel and Nezvalová, Leona and Popelínský, Lubomír},
   address = {London},
   booktitle = {Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014},
   doi = {http://dx.doi.org/10.1007/978-3-319-10554-3_14},
   editor = {Gennady Agre, Pascal Hitzler, Adila A. Krisnadhi, Sergei O. Kuznetsov},
   keywords = {educational data mining; logic proofs; clustering; outlier detection; sequence mining},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {London},
   isbn = {978-3-319-10553-6},
   pages = {150-161},
   publisher = {Springer},
   title = {Educational data mining for analysis of students’ solutions},
   year = {2014}
}

TY  - JOUR
ID  - 1198062
AU  - Vaculík, Karel - Nezvalová, Leona - Popelínský, Lubomír
PY  - 2014
TI  - Educational data mining for analysis of students’ solutions
PB  - Springer
CY  - London
SN  - 9783319105536
KW  - educational data mining
KW  - logic proofs
KW  - clustering
KW  - outlier detection
KW  - sequence mining
N2  - We introduce a novel method for analysis of logic proofs constructed by undergraduate students that employs sequence mining for manipulation with temporal information about all actions that a student performed, and also graph mining for finding frequent subgraphs on different levels of generalisation. We show that this representation allows to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent on behavioural patterns and we are able to find clusters of erroneous solutions. We also observed significant dependence between time duration and an appearance of the most serious error.
ER  -

VACULÍK, Karel, Leona NEZVALOVÁ and Lubomír POPELÍNSKÝ. Educational data mining for analysis of students’ solutions. Online. In Gennady Agre, Pascal Hitzler, Adila A. Krisnadhi, Sergei O. Kuznetsov. \textit{Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014}. London: Springer, 2014, p.~150-161. ISBN~978-3-319-10553-6. Available from: https://dx.doi.org/10.1007/978-3-319-10554-3\_{}14.

Detailed Information on Publication Record