Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Web-based education for all: a tool for development adaptive courseware
WWW7 Proceedings of the seventh international conference on World Wide Web 7
From adaptive hypermedia to the adaptive web
Communications of the ACM - The Adaptive Web
Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process
Applied Intelligence
Personalized e-learning system using Item Response Theory
Computers & Education
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Expert Systems with Applications: An International Journal
A Prediction Mechanism of Adaptive Learning Content in the Scalable E-Learning Environment
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Genetic algorithm based multi-agent system applied to test generation
Computers & Education
Competency-Based Learning Object Sequencing Using Particle Swarms
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system
Computers & Education
Intelligent web-based learning system with personalized learning path guidance
Computers & Education
Expert Systems with Applications: An International Journal
Dynamic question generation system for web-based testing using particle swarm optimization
Expert Systems with Applications: An International Journal
A learning style classification mechanism for e-learning
Computers & Education
Expert Systems with Applications: An International Journal
A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems
Computer Standards & Interfaces
Information Sciences: an International Journal
Implementation of Learning Path in Process Control Model
The Computer Journal
PC2PSO: personalized e-course composition based on Particle Swarm Optimization
Applied Intelligence
An Experimental Study of a Personalized Learning Environment Through Open-Source Software Tools
IEEE Transactions on Education
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This paper proposes a self-adjusting e-course generation process, which support to provide a truly personalized learning environment. The proposed process is divided into four steps: (1) determining learning concept structure, (2) adjusting the difficulty of the e-learning material, (3) analyzing a learner's ability and learning goals, and (4) composing personalized e-courses. Meanwhile, this paper applies the collaborative voting approach to determine the difficulty of the e-learning material, and the maximum likelihood estimation (MLE) to analyze a learner's ability and her/his learning goals. Since evolutionary algorithms (EAs) have been developed to find close optimal solutions, this paper adopts them to compose personalized e-courses that meet individual learners' demands. Once a learner learns one or more learning concepts covered in a personalized e-course, the feedback information from the learner must be returned to: (I) self-adjust the difficulty of the e-learning material for step 2, and (II) update the learners' ability and learning goals for step 3. Furthermore, to find appropriate EAs for personalized e-course composition, this paper devises some experiments to compare two widely applied EAs, Genetic algorithms (GA) and Particle Swarm Optimization (PSO). When the number of e-learning materials is less than 300, the experimented results indicate that the executing effectiveness of PSO is better than that of GA. Besides, to validate the practicability of the proposed process, an e-course authoring tool based on the proposed process is developed to generate personalized e-courses. The generated personalized e-courses have been provided to 103 actual learners who participate in an ''Introduction to Computer'' curriculum. The investigation results indicate that the proposed process adapts to learners by utilizing the feedback from many learners. In other words, learning experiences of one organization/class can benefit to another organization/class's learners in the same curriculum.