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. |
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@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.
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