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Description
  • 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.
  • 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. (en)
Title
  • Educational data mining for analysis of students’ solutions
  • Educational data mining for analysis of students’ solutions (en)
skos:prefLabel
  • Educational data mining for analysis of students’ solutions
  • Educational data mining for analysis of students’ solutions (en)
skos:notation
  • RIV/00216224:14330/14:00076477!RIV15-MSM-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
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  • 13003
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/14:00076477
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • educational data mining; logic proofs; clustering; outlier detection; sequence mining (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EF16466F4DA4]
http://linked.open...v/mistoKonaniAkce
  • Varna, Bulgaria
http://linked.open...i/riv/mistoVydani
  • London
http://linked.open...i/riv/nazevZdroje
  • Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
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  • Popelínský, Lubomír
  • Vaculík, Karel
  • Nezvalová, Leona
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-10554-3_14
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 9783319105536
http://localhost/t...ganizacniJednotka
  • 14330
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