Feature extraction and model construction for predicting scientific inquiry skills acquisition

Assessing scientific inquiry skills in INQPRO, a scientific inquiry learning environment developed in this research work, presents two major challenges: (i) identifying a set of important features from a series of student interactions for assessment of scientific inquiry skills is difficult. Such difficulty stemmed not only because there exists ways a student interacts with the scientific inquiry learning environment, but more challengingly defining the causal dependencies between the extracted features is not a trivial task; (ii) constructing a classification model from large number of features and can handle uncertainty in assessing scientific inquiry skills is not a trivial task. To overcome these challenges, feature selection approach was firstly employed, using the preprocessed dataset from interaction logs of 130 students. A Bayesian Network was subsequently constructed to handle the uncertainty inherent in assessing scientific inquiry skills. Both quantitative and qualitative portions of the Bayesian Network were elicited from a domain expert. Empirical study concluded that (i) expert elicited features outperformed features selected by feature selection algorithms; (ii) Machine-learned Bayesian Network can better encode knowledge about patterns of scientific inquiry skills acquisition as compared to Naïve Bayesian Network.