Features and Bayesian Network Model of Conceptual Change for INQPRO

Predicting conceptual change in scientific inquiry learning environment is not trivial due to the challenges that stemmed when eliciting a student´s implicit properties. The challenges could be more complicated when such learning environment employs exploratory learning approach. One plausible approach to tackle the challenges is by employing data mining approach. In this study, 129 interaction logs were firstly preprocessed and subsequently transformed into structured dataset fits for mining purpose. Feature selection algorithms were performed considering that fact the dataset consists of large number of attributes. The dimension of feature set was reduced via two feature selection algorithms and elicitation of domain expert, resulting in F_ORA, F_RFE, and F_DOM, respectively. The feature sets were compared using Naive Bayesian Networks (M_{NB_DOM}, M_{NB_RFE}, M_{NB_ORA}). The second phase of empirical study aimed to investigated the optimal BN model for capturing knowledge about conceptual change. To do that, a machine-learned Bayesian Network (M_LBN) was constructed and its performance was compared to M_{NB_DOM}. Findings from empirical studies suggested that (i) classifiers constructed using F_DOM outperformed F_ORA and F_RFE and (ii) the classifier M_{NB_DOM} outperformed .Mnb dom in predicting conceptual change, suggesting that M_LBN is a better classifier than M_{NB_DOM} in capturing knowledge about conceptual change in INQPRO, a scientific inquiry learning environment developed in this research work.