Forming automatic groups of learners using particle swarm optimization for applications of differentiated instructionForming automatic groups of learners using particle swarm optimization for applications of differentiated instruction Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2020-10-292020enThe aim of this paper is to present a method that uses computational intelligence techniques to classify students according to the principles of differentiated instruction. A clustering algorithm based on particle swarm optimization is applied to two sets of data emerging from the holistic assessment of the student's particular characteristics and needs. The results illustrate the algorithm's contribution to the effective formation of heterogeneous student groups, with the members of each having homogeneous characteristics of skills, difficulties, psychosocial and cognitive profiles. Thus, the teacher can easily manage students, by knowing the characteristics of each group. A comparison with a genetic algorithm as well as cuckoo search algorithm shows that the proposed method provides improved categorization capabilities.http://creativecommons.org/licenses/by/4.0/Computer Applications in Engineering Education282282-292 Zervoudakis Konstantinos Ζερβουδακης Κωνσταντινος Mastrothanasis Konstantinos Tsafarakis Stelios Τσαφαρακης Στελιος John Wiley and Sons Differentiated instruction Group design Holistic assessment NP-hard problem Particle swarm optimization