caa a22 4500 44584308X CHVBK 20180317145350.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s11047-010-9246-5 doi (NATIONALLICENCE)springer-10.1007/s11047-010-9246-5 Evolutionary computation approaches to the Curriculum Sequencing problem [Elektronische Daten] [Sarab Al-Muhaideb, Mohamed Menai] Within the field of e-Learning, a learning path represents a match between a learner profile and his preferences from one side, and the learning content presentation and the pedagogical requirements from the other side. The Curriculum Sequencing problem (CS) concerns the dynamic generation of a personal optimal learning path for a learner. This problem has gained an increased research interest in the last decade, as it is not possible to have a single learning path that suits every learner in the widely heterogeneous e-Learning environment. Since this problem is NP-hard, heuristics and meta-heuristics are usually used to approximate its solutions, in particular Evolutionary Computation approaches (EC). In this paper, a review of recent developments in the application of EC approaches to the CS problem is presented. A classification of these approaches is provided with emphasis on the tools necessary for facilitating learning content reusability and automated sequencing. Springer Science+Business Media B.V., 2010 Evolutionary Computing nationallicence Adaptive e-Learning nationallicence Curriculum Sequencing nationallicence Path finding nationallicence AACS : Attribute-based Ant Colony System nationallicence ACO : Ant Colony Optimization nationallicence CS : Curriculum Sequencing problem nationallicence CSP : Constraint Satisfaction Problem nationallicence DYLPA : Dynamic Learning Path Advisor nationallicence EA : Evolutionary Algorithm nationallicence EC : Evolutionary Computing nationallicence ES : Evolution Strategies nationallicence GA : Genetic Algorithm nationallicence GP : Genetic Programming nationallicence HMA : Hierarchical Memetic Algorithm nationallicence IMS : Instructional Management Systems nationallicence LO : Learning object nationallicence LOM : Learning Object Metadata nationallicence LR : Learning resource nationallicence MA : Memetic Algorithms nationallicence PC2PSO : Personalized e-Course Composition approach based on Particle Swarm Optimization algorithm nationallicence PSO : Particle Swarm Optimization nationallicence SACS : Style-based ACO system nationallicence Al-Muhaideb Sarab Department of Computer and Information Sciences, College for Women, Prince Sultan University, P.O. Box 53073, 11586, Riyadh, Saudi Arabia aut Menai Mohamed Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, 11543, Riyadh, Saudi Arabia aut Natural Computing Springer Netherlands 10/2(2011-06-01), 891-920 1567-7818 10:2<891 2011 10 11047 https://doi.org/10.1007/s11047-010-9246-5 text/html Onlinezugriff via DOI 1 review-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-010-9246-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Al-Muhaideb Sarab Department of Computer and Information Sciences, College for Women, Prince Sultan University, P.O. Box 53073, 11586, Riyadh, Saudi Arabia aut NATIONALLICENCE 700 1- Menai Mohamed Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, 11543, Riyadh, Saudi Arabia aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/2(2011-06-01), 891-920 1567-7818 10:2<891 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer