Using Semantic Lifting for Improving Educational Process Models Discovery and Analysis

Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with discovering, analyzing, and improving educational processes based on information hidden in datasets and logs. These data are recorded by educational systems in different forms and at different levels of granularity. Often, process discovery and analysis techniques applied in the educational field have relied exclusively on the syntax of labels in databases. Such techniques are very sensitive to data heterogeneity, labelname variation and their frequent changes. Consequently, large educational process models are discovered without any hierarchy or structuring. In this paper we show how by linking labels in event logs to their underlying semantics, we can bring educational processes discovery to the conceptual level. In this way, more accurate and compact educational processes can be mined and analyzed at different levels of abstraction. We have tested this approach using the process mining Framework ProM 5.2.