Patterns Analysis in the Online Assessment Processes Using Hierarchical Models

Student: Anastasiia Alekseeva

Educational Programme: System and Software Engineering (Master)

Year of Graduation: 2024

This master's thesis is devoted to the analysis of student behavior patterns within the framework of online assessment. The results of the programming contest at the Higher School of Economics are used as initial data. These data are used to construct hierarchical Petri nets that reflect the behavior of students in completing tasks in general and obtaining different results within one task. As a result, three behavior patterns were identified, for each of which an algorithm for identifying them was developed. The tools of the PM4PY library of the Python language are used as the main tool for data processing, building models and implementing algorithms. Key words: online assessment; workflow nets; process mining; hierarchical process model; learning behaviour patterns; learnflow.

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