Students need to integrate multiple basic concepts to become competent in the activities that require the knowledge of the composite concept. Traditionally, we rely on experts? judgments to build models for this integration process. In this paper, we explore computational methods for unveiling how students learn composite concepts, and compare effects of applying mutual information-based and hierarchical search-based techniques for guessing the unobservable processes, which were simulated by Bayesian networks. Experimental results show that computational methods can be useful in assisting this student modelling task.