The ant colony optimization meta-heuristic
New ideas in optimization
Guided paths through web-based collections: design, experiences, and adaptations
Journal of the American Society for Information Science - digital libraries: Part 1
Social navigation research agenda
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Providing SCORM with adaptivity
Proceedings of the 15th international conference on World Wide Web
A Competency-Oriented Modeling Approach for Personalized E-Learning Systems
ICIW '08 Proceedings of the 2008 Third International Conference on Internet and Web Applications and Services
An autonomic approach to offer services in OSGi-based home gateways
Computer Communications
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, Bayesian-Networks (BN) and Ant Colony Optimization (ACO) techniques are combined to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work is that the amount of pheromones released is variable. This amount is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required. By using ACO and BN, a fitness function, responsible of automatically selecting the next course in the learning graph, is defined. This is done by generating a path that maximizes the probability of each user's success in the course. Therefore, the path can change to improve learners’ average performance, taking into account the pedagogical weight of each learning unit and the social behavior of the system. Furthermore, a discrete dynamical system is obtained and its stability is studied. How to wrap an existing Learning Management System is also described in this work. Finally, an experiment compares this approach with the old on-line learning system being used previously. (These initial values were agreed with the Pedagogical Team. In addition, all the edges of the learning graph were initialized with zero pheromones. © 2012 Wiley Periodicals, Inc.)