Factors influencing the performance of Dynamic Decision Network for INQPRO

There has been an increasing interest in employing decision-theoretic framework for learner modeling and provision of pedagogical support in Intelligent Tutoring Systems (ITSs). Much of the existing learner modeling research work focuses on identifying appropriate learner properties. Little attention, however, has been given to leverage Dynamic Decision Network (DDN) as a dynamic learner model to reason and intervene across time. Employing a DDN-based learner model in a scientific inquiry learning environment, however, remains at infant stage because there are factors contributed to the performance the learner model. Three factors have been identified to influence the matching accuracy of INQPRO’s learner model. These factors are thestructureof DDN model, thevariable instantiationapproach, and theweightsassignmentmethodfortwoconsecutiveDecisionNetworks (DNs). In this research work, a two-phase empirical study involving 107 learners and six domain experts was conducted to determine the optimal conditions for the INQPRO’s dynamic learner model. The empirical results suggested each time-slice of the INQPRO’s DDN should consist of a DN, and that DN should correspond to the Graphical User Interface (GUI) accessed. In light of evidence, observable variables should be instantiated to their observedstates; leaving the remaining observable nodes uninstantiated. The empirical results also indicated that varying weights between two consecutive DNs could optimize the matching accuracy of INQPRO’s dynamic learner model.