Korhan GünelKazım ErdoǧduRefet PolatYasin OzarslanH. Adeli , A.M. Correia , S. Costanzo , L.P. Reis , A. Rocha2025-10-0620179783319604855, 9783319276427, 9783319419343, 9783319232034, 9783319938844, 9783642330414, 9783319262833, 9788132220084, 9783642375019, 978303002682021945357, 2194536510.1007/978-3-319-56538-5_94https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018597356&doi=10.1007%2F978-3-319-56538-5_94&partnerID=40&md5=8d7ae68cac7e1248d3c7a969cdc078achttps://gcris.yasar.edu.tr/handle/123456789/9732Recent 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. © 2017 Elsevier B.V. All rights reserved.EnglishArtificial Intelligence On Educational Technology, Concept Map Mining, Feature Selection, Particle Swarm Optimization, Pso, Swarm Intelligence, Artificial Intelligence, Education, Educational Technology, Feature Extraction, Hybrid Systems, Information Systems, Swarm Intelligence, Concept Detection, Concept Maps, Educational Contents, Multi Layer Perceptron, Particle Swarm Optimization (pso)Artificial intelligence, Education, Educational technology, Feature extraction, Hybrid systems, Information systems, Swarm intelligence, Concept detection, Concept maps, Educational contents, Multi layer perceptron, Particle swarm optimization (PSO)Conference Object