Abstract
We introduce a novel method for analysis of logical 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 one to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent of 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.
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Vaculík, K., Nezvalová, L., Popelínský, L. (2014). Educational Data Mining for Analysis of Students’ Solutions. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_14
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DOI: https://doi.org/10.1007/978-3-319-10554-3_14
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