Paper Info
Title
Probabilistic Learner Modeling in Scientific Inquiry Exploratory Learning Environment
Abstract
Research on learning has shown that although computer-based exploratory learning environments have been proven to be beneficial to learners, effectively inferring a learner's actions under a sound teaching and learning model that enhances exploratory behaviours remains uncertain. To address this problem, this article aims at discussing and highlighting the detail methodological approach for designing, and integrating the probabilistic learner modeling leveraging Bayesian networks into Scientific Inquiry Exploratory Learning Model. This integration mainly serves as a basis to support learners with adaptive instructions and facilitating the acquisition of both domain knowledge as well as scientific inquiry skills. To visualize the proposed methodological approach, a computer-based scientific inquiry exploratory learning environment named InQPro is developed. This article ends with presenting the preliminary investigation on employing Artificial Students technique to investigate the propagation of probabilities between subnetworks, and identifying threshold parameters in the probabilistic learner model.
Year
Venue
Keywords
2005
ICCE
computer-based exploratory learning environment,probabilistic learner modeling,probabilistic learner model,proposed methodological approach,scientific inquiry exploratory learning,artificial students technique,detail methodological approach,computer-based scientific inquiry,scientific inquiry skill,exploratory behaviour,exploratory learning environment
Field
DocType
Volume
Experiential learning,Data science,Domain knowledge,Computer science,Exploratory learning,Knowledge management,Bayesian network,Probabilistic logic
Conference
133
ISSN
ISBN
Citations 
0922-6389
1-58603-573-8
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Choo-Yee Ting19013.19
M. Reza. Beik Zadeh291.01