Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Automated Advice-Giving Strategies for Scientific Inquiry
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Conceptual and Meta Learning During Coached Problem Solving
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Properties of Bayesian student model for INQPRO
Applied Intelligence
Coaching within a domain independent inquiry environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty stemmed mainly because conceptual change is an implicit mental model restructuring process, which occurs as a result of confrontation with new knowledge; (ii) to model the process of conceptual change across time by merely observing student interactions could be misleading. This is largely because within the computer-based learning environment, students are granted the freedom to explore and evaluate their ideas. To ease these challenges, this study began with proposing variables of conceptual change and subsequently employing a Bayesian Network model to capture the causal dependencies between the proposed variables as well as the evolving patterns of conceptual change. To obtain the optimal model, two conceptual change Bayesian Network models were proposed and integrated into INQPRO before they can be empirically evaluated via a field study. In this study, interaction logs from 96 were collected and preprocessed before feeding into the proposed models. The accuracy of each Bayesian Network model was measured by matching the results of posttest and interview with the predicted conceptual change outcomes of each model. The empirical findings supported the notion that the conceptual change Bayesian Network model with self-regulation node is a better solution to predict a student's conceptual change in INQPRO.