Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Factors influencing the performance of Dynamic Decision Network for INQPRO
Computers & Education
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts
User Modeling and User-Adapted Interaction
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Identifying the appropriate features for constructing a Bayesian student model is crucial to ensure that the model is always optimal. Feature sets can be identified via two types of feature selection algorithms: (i) algorithms that return a discrete set of features, and (ii) algorithms that rank features from the highest to the lowest importance with respect to a class label. To determine the optimal feature set from the second type of feature selection algorithm has always been a challenge, mainly because indifference in overall predictive accuracies between feature sets often occurs. In this light, this paper proposes evidence conflict analysis approach to tackle the challenges. This approach analyzes the conflicts in evidence when a Bayesian Network is employed as a student model. To demonstrate the proposed method, the experiments in this study had utilized two datasets that were transformed from 244 students' log data. The empirical findings suggested that evidence conflict analysis can differentiate the performance of feature sets having the same overall predictive accuracy.