Properties of Bayesian student model for INQPRO

Abstract

Employing a probabilistic student model in a scientific inquiry learning environment often presents two challenges. First, what constitute the appropriate variables for modeling scientific inquiry skills in such a learning environment, considering the fact that it practices exploratory learning approach? Following exploratory learning approach, students are granted the freedom to navigate from one GUI to another. Second, do causal dependencies exist between the identified variables, and if they do, how should they be defined? To tackle the challenges, this research work attempted the Bayesian Networks framework. Leveraging on the framework, two student models were constructed to predict the acquisition of scientific inquiry skills for INQPRO, a scientific inquiry learning environment developed in this research work. The student models can be differentiated by the variables they modeled and the causal dependencies they encoded. An on-field evaluation involving 101 students was performed to assess the most appropriate structure of the INQPRO's student model. To ensure fairness in model comparison, the same Dynamic Bayesian Network (DBN) construction approach was employed. Lastly, this paper highlights the properties of the student model that provide optimal results for modeling scientific inquiry skill acquisition in INQPRO.