Smart Remote Classroom: Creating a Revolutionary Real-Time Interactive Distance Learning System
ICWL '02 Proceedings of the First International Conference on Advances in Web-Based Learning
Can e-learning replace classroom learning?
Communications of the ACM - New architectures for financial services
KnowledgeTree: a distributed architecture for adaptive e-learning
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Constraint-Based Validation of Adaptive e-Learning Courseware
IEEE Transactions on Learning Technologies
Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning
IEEE Transactions on Knowledge and Data Engineering
Subontology-Based Resource Management for Web-Based e-Learning
IEEE Transactions on Knowledge and Data Engineering
Personalized Service-Oriented E-Learning Environments
IEEE Internet Computing
Exploring e-learning knowledge through ontological memetic agents
IEEE Computational Intelligence Magazine
Starting directions for personalized E-Learning
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
Enhancing E-Learning Through Teacher Support: Two Experiences
IEEE Transactions on Education
Effects of Competitive E-Learning Tools on Higher Education Students: A Case Study
IEEE Transactions on Education
IEEE Transactions on Education
Interactive e-learning system using pattern recognition and augmented reality
IEEE Transactions on Consumer Electronics
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With the rapid development of information and education technology, online learning system become more and more important in improving learners' grade. To provide personalized, flexible learning examination and efficiently keep students' confidence, the key problem is how to control the learning difficulty. In this paper, we present personalized learning difficulty-based online learning system (PLD-OLS) which can adaptively provide learning examination with reasonable difficulty for learners. In this method, each exercise is appended a hex string which represents the learners' understanding degree based on exercise difficulty. With the procedure of learning, this string will be automatically changed to represent the current understanding degree of learners. In addition, according to this novel representation, the learning state can be achieved by merging all hex strings to help customize learning examination with personalized learning difficulty for learners' re-learning. Learning results demonstrate that PLD-OLS can efficiently control the learning difficulty and improve learning effect of learners.