Analyzing student inquiry data using process discovery and sequence classification

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Proceedings titleProceedings of the 8th International Conference on Data Mining
Conference8th International Conference on Data Mining, June 26-29, 2015, Madrid, Spain
Pages412415; # of pages: 4
AbstractThis paper reports on results of applying process discovery mining and sequence classifcation mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and social networks. As an example of our current research efforts, we applied temporal data mining analysis techniques to a PSLC DataShop data set [17, 18, 19, 20]. First, we show that process mining techniques allow for discovery of learning processes from student behaviours. Second, sequential pattern mining is used to classify students according to skill. Our results show that considering sequences of activities as opposed to single events improved classifcation by up to 230%.
Publication date
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21275888
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Record identifierc1eee199-2ec7-4bb2-846f-22e67c370523
Record created2015-07-23
Record modified2016-05-09
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