Modeling and Intervening Across Time in Scientific Inquiry Exploratory Learning Environment

This article aims at discussing how Dynamic Decision Network (DDN) can be employed to tackle the challenges in modeling temporally variable scientific inquiry skills and provision of adaptive pedagogical interventions in INQPRO, a scientific inquiry exploratory learning environment for learning O’level Physics. We begin with an overview of INQPRO and a highlight of the computer algorithm as well as the design of our proposed DDN model. We then present an instance of interactions with INQPRO to describe how the proposed model can be generated dynamically by aggregating different INQPRO Graphical User Interfaces (GUIs) in real-time basis to perform probabilistic assessments of the two scientific inquiry skills (Hypothesis Formulation Η and Variable Identification ς). In this study, we carried out a two-phase empirical evaluation to investigate the performance of the proposed DDN model in categorizing different groups of learners. The performance of the proposed DDN model is identified by its matching accuracies elicited from a total of 6 domain experts and 77 learners who participated in both evaluation phases. Based on the empirical results, we summarized that the proposed DDN model is practically sound as it has demonstrated acceptable estimation accuracies with reference to the classification results obtained from the pretest, posttest, and from domain experts.