Web-based education for all: a tool for development adaptive courseware
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Personalization on the Net using Web mining: introduction
Communications of the ACM
Adaptive Instructional Planning Using Ontologies
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Using a style-based ant colony system for adaptive learning
Expert Systems with Applications: An International Journal
Intelligent web-based learning system with personalized learning path guidance
Computers & Education
An attribute-based ant colony system for adaptive learning object recommendation
Expert Systems with Applications: An International Journal
A fully personalization strategy of E-learning scenarios
Computers in Human Behavior
Evolutionary computation approaches to the Curriculum Sequencing problem
Natural Computing: an international journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners' preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors - by helping them to monitor, refine, and improve e-learning modules and courses according to the learners' behaviour.