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  • We introduce novel methods for analysis of logic proofs constructed by undergraduate students. The methods employ 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 generalization. We showed in [8-11] that these representations allow us to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent on behavioral patterns and we were able to find clusters of erroneous solutions. We also observed a significant dependence between time duration of solving the task and an appearance of the most serious error. This text brings a brief summary of four contributions [8-11] presented for presentation in the period from October 2013 until September 2014.
  • We introduce novel methods for analysis of logic proofs constructed by undergraduate students. The methods employ 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 generalization. We showed in [8-11] that these representations allow us to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent on behavioral patterns and we were able to find clusters of erroneous solutions. We also observed a significant dependence between time duration of solving the task and an appearance of the most serious error. This text brings a brief summary of four contributions [8-11] presented for presentation in the period from October 2013 until September 2014. (en)
Title
  • Educational data mining for analysis of students’ solutions
  • Educational data mining for analysis of students’ solutions (en)
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  • Educational data mining for analysis of students’ solutions
  • Educational data mining for analysis of students’ solutions (en)
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  • RIV/00216224:14330/14:00077051!RIV15-MSM-14330___
http://linked.open...avai/riv/aktivita
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  • S
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  • 13004
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  • RIV/00216224:14330/14:00077051
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  • educational data mining; logic proofs; clustering; outlier detection; sequence mining (en)
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http://linked.open...ontrolniKodProRIV
  • [0CF08C06A2FE]
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  • Jasná pod Chopkom, Nízké Tatry, Slovakia
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  • Praha
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  • Znalosti 2014
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  • Popelínský, Lubomír
  • Vaculík, Karel
  • Nezvalová, Leona
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number of pages
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  • Vysoká škola ekonomická v Praze
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  • 9788024520544
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  • 14330
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