Educational data mining for analysis of students’ solutions

D 2014

Educational data mining for analysis of students’ solutions

VACULÍK, Karel; Leona NEZVALOVÁ a Lubomír POPELÍNSKÝ

Základní údaje

Originální název

Educational data mining for analysis of students’ solutions

Autoři

VACULÍK, Karel; Leona NEZVALOVÁ a Lubomír POPELÍNSKÝ

Vydání

London, Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014, od s. 150-161, 12 s. 2014

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Impakt faktor

Impact factor: 0.402 v roce 2005

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/14:00076477

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-10553-6

ISSN

DOI

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

EID Scopus

2-s2.0-84921734322

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 27. 4. 2015 05:56, RNDr. Pavel Šmerk, Ph.D.

Anotace

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 a 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, s. 150-161. ISBN 978-3-319-10553-6. Dostupné z: https://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 = {https://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  - CONF
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 a 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, s.~150-161. ISBN~978-3-319-10553-6. Dostupné z: https://doi.org/10.1007/978-3-319-10554-3\_{}14.

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