Optimal dynamic decision network model for scientific inquiry learning environment

Abstract

While recent studies employ heuristic to support learners in scientific inquiry learning environments, this study examined the theoretical and practical aspects of decision-theoretic approach to simultaneous reason about learners' scientific inquiry skills and provision of adaptive pedagogical interventions across time. In this study, the dynamic learner model, represented by three different Dynamic Decision Network (DDN) models, were employed and evaluated through a three-phase empirical study. This paper discusses how insights gained and lessons learned from the evaluations of a preceding model had led to the improvements of subsequent model; before finalizing the optimal design of DDN model. The empirical studies involved six domain experts, 101 first-year university learners, and dataset from our previous research. Each learner participated in a series of activities including a pretest, a session with INQPRO learning environment, a posttest, and an interview session. For each DDN model, the predictive accuracies were computed by comparing the classifications given by the model with (a) the results obtained from the pretest, posttest, and learner self-rating scores, and (b) classifications elicited by domain experts based on the learner interaction logs and the graphs exhibited by each model.