Pattern Mining and Process Modelling of Collaborative Interaction Data in an Online Multi-Tabletop Learning Environment

Abstract

This research builds on the intersection of a web-based (online) multi-interactive multi-tabletop collaborative environment (so-called M-ITCL) and process mining process discovery algorithms applied on the collaborative interaction data (event logs) previously collected from an authentic learning classroom. The main focus of the study was to investigate which process mining algorithm could lead to generation of process models that differentiate (replay) the events correctly with 100% level of fitness, precision, generalisation and simplicity. The results showed that alpha algorithm resulted in the generation of process models with good simplicity but with poor precision and generalisation. Heuristic algorithm resulted in the generation of process models with good precision but with poor generalisation and simplicity. Fuzzy algorithm resulted in generation of rather simple process models with good precision and generalisation. Moreover, the models/graphs generated through fuzzy algorithm could differentiate all of the cases correctly with 100% level of fitness as a validation measure.

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