Korhan GünelKazım ErdoǧduRefet PolatYasin Ozarslan2025-10-062019089671074202664720, 146803940266-47201468-039410.1111/exsy.12278https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045843502&doi=10.1111%2Fexsy.12278&partnerID=40&md5=b15ecc76a2c18e0e315fe5af19316cf9https://gcris.yasar.edu.tr/handle/123456789/9407Concept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content specifying relations among them and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed-forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state-of-the-art algorithms have been made from the viewpoint of a typical classification problem. © 2019 Elsevier B.V. All rights reserved.EnglishAnt Colony Optimization, Artificial Intelligence In Educational Technology, Concept Map Mining, Evolutionary Computation, Feature Selection, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Intelligence, Data Mining, Educational Technology, Evolutionary Algorithms, Genetic Algorithms, Hybrid Systems, Particle Swarm Optimization (pso), Accuracy And Precision, Concept Detection, Concept Maps, Educational Contents, Empirical Studies, Intelligent Tutors, Performance Comparison, State-of-the-art Algorithms, Feature ExtractionAnt colony optimization, Artificial intelligence, Data mining, Educational technology, Evolutionary algorithms, Genetic algorithms, Hybrid systems, Particle swarm optimization (PSO), Accuracy and precision, Concept detection, Concept maps, Educational contents, Empirical studies, Intelligent tutors, Performance comparison, State-of-the-art algorithms, Feature extractionConference Object