The usefulness of log based clustering in a complex simulation environment

Data mining techniques have been successfully employed on user interaction data in exploratory learning environments. In this paper we investigate using data mining techniques for analyzing student behaviors in an especially-complex exploratory environment, with over one hundred possible actions at any given point. Furthermore, the outcomes of these actions depend on their context. We propose a multi-layer action-events structure to deal with the complexity of the data and employ clustering and rule mining to examine student behaviors in terms of learning performance and effects of different degrees of scaffolding. Our findings show that using the proposed multi-layer structure for describing action-events enables the clustering algorithm to effectively identify the successful and unsuccessful students in terms of learning performance across activities in the presence or absence of external scaffolding. We also report and discuss the prominent behavior patterns of each group and investigate short term effects of scaffolding.