Learning is a complex cognitive process that depends not only on an individual capabilty of knowledge  absorbtion but it can be also influenced by various group interactions.  After a few years of collecting students’ records, the database at Faculty of Physics, WUT has grown enough to become an object of statistical analysis.  We have applied various data mining methods (such as PCA, clustering, Pearson product-moment correlation coefficient, mutual information, transfer entropy) for an  anonymized version of academic records in order to determine whether there are  key courses or distinctive groups of students. Moreover we have studied  whether hidden relationships and interactions among students affect the dynamics of students’ scores and if so then how strong  the effect is.

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