Dynamic Time Warping in Analysis of Student Behavioral Patterns

E-learning systems store large amount of data based on the history of users’ interactions with the system. These pieces of information are usually used for further course optimization, finding e-tutors in collaboration learning, analysis of students’ behavior, or for other purposes. The paper deals with an analysis of students’ behavior in learning management system. The main goal of the paper is to find, how selected methods can influence finding of behavioral patterns in learning management system and how we can reduce the amount of extracted sequences. The methods of process mining and sequential pattern mining were used for extraction of behavioral patterns. The authors present the comparison of selected methods for the definition of students’ behavior with the focus to influence of dynamic time warping. Obtained patterns and relations between them are presented using complex networks; the visualization and pattern clusters extraction is optimized by spectral graph partitioning.