Korhan GunelKazim ErdogduRefet PolatYasin OzarslanPolat, RefetErdogdu, KazimOzarslan, YasinGunel, KorhanA RochaAM CorreiaH AdeliLP ReisS Costanzo2025-10-062017978-3-319-56538-5, 978-3-319-56537-8978331956538597833195653782194-53572194-536510.1007/978-3-319-56538-5_942-s2.0-85018597356http://dx.doi.org/10.1007/978-3-319-56538-5_94https://gcris.yasar.edu.tr/handle/123456789/6804https://doi.org/10.1007/978-3-319-56538-5_94Recent developments of computational intelligence on educational technology yield concept map mining as a new research area. Concept map mining covers the extraction of learning concepts specifying relations among them and generating a concept map from educational contents. In this study we focused on determining the features that characterize a learning concept extracted from an educational text as raw data. The first three features are detected by using a hybrid system of Multi Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) and the performance of the applied method is gauged in the viewpoint of a typical classification problem.Englishinfo:eu-repo/semantics/closedAccessArtificial intelligence on educational technology, Feature selection, Swarm intelligence, PSO, Particle Swarm Optimization, Concept Map MiningCONCEPT MAPS, CONSTRUCTION, CREATIONParticle Swarm OptimizationArtificial Intelligence on Educational TechnologyPSOFeature SelectionSwarm IntelligenceConcept Map MiningConference Object