Practical neural network recipes in C++
Practical neural network recipes in C++
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Personalized hypermedia and international privacy
Communications of the ACM - The Adaptive Web
User Modeling in Human–Computer Interaction
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Adaptivity for Conceptual and Narrative Flow in Hyperbooks: The MetaLinks System
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Applying multi-intelligent adaptive hypermedia to online learning
Applying multi-intelligent adaptive hypermedia to online learning
Assessing metacognitive knowledge in web-based CALL: a neural network approach
Computers & Education
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Automatic detection of users' skill levels using high-frequency user interface events
User Modeling and User-Adapted Interaction
Learners' navigation behavior identification based on trace analysis
User Modeling and User-Adapted Interaction
Enhancing student learning through hypermedia courseware andincorporation of student learning styles
IEEE Transactions on Education
Evaluating the integration of fuzzy logic into the student model of a web-based learning environment
Expert Systems with Applications: An International Journal
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF). The MLFF was adopted because of its ability on imprecise or incompletely understood data, ability to generalize and learn from specific examples, ability to be quickly updated with extra parameters, and speed in execution making them ideal for real time applications. The system then adaptively recommended learning content presented with a variety of content and interactive components through the adaptation model based on the student cognitive style identified in the student model. The adaptive web interfaces were designed by investigating the relationships between students' cognitive styles and browsing patterns of content and interactive components. Training of the MLFF and an experiment were conducted to examine the accuracy of identifying students' cognitive styles during browsing with the proposed MLFF and the impact of the proposed adaptive web-based system on students' engagement in learning. The training results of the MLFF showed that the proposed system could identify students' cognitive styles with high accuracy and the temporal effects should be considered while identifying students' cognitive styles during browsing. Two factors, the acknowledgment of students' cognitive styles while browsing and the existence of adaptive web interfaces, were used to assign three classes of college freshmen into three groups. The experimental results revealed that the proposed system could have significant impacts on temporal effects on students' engagement in learning, not only for students with cognitive styles known before browsing, but also for students with cognitive styles identified during browsing. The results provide evidence of the effectiveness of the adaptive web-based learning system with students' cognitive styles dynamically identified during browsing, thus validating the research purposes of this study.