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
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Students' reasoning during modeling in an inquiry learning environment
Computers in Human Behavior
Assessing Learner's Scientific Inquiry Skills Across Time: A Dynamic Bayesian Network Approach
UM '07 Proceedings of the 11th international conference on User Modeling
Factors influencing the performance of Dynamic Decision Network for INQPRO
Computers & Education
Intervening vs. non-intervening actions for INQPRO's dynamic learner model
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Optimal dynamic decision network model for scientific inquiry learning environment
Applied Intelligence
International Journal of Artificial Intelligence in Education
Data mining for adding adaptive interventions to exploratory and open-ended environments
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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Although existing computer-based scientific inquiry learning environments have proven to benefit learners, effectively inferring and intervening within these learning environments remain an open issue. To tackle this challenge, this article will firstly address the issue on learning model by proposing Scientific Inquiry Exploratory Learning Model. Secondly, aiming at effective modeling and intervening under uncertainty in modeling learner's exploratory behaviours, decision-theoretic approach is integrated into INQPRO. This approach allows INQPRO to compute a probabilistic assessment on learner's scientific inquiry skills (Hypothesis Generation and Variables Identification), domain knowledge, and subsequently provides tailored hints. This article ends with an investigation on the accuracy of proposed learner model by performing a model walk-through with human expert and field trial evaluation with a total number of 30 human students.