Learning path recommendation using hybrid particle swarm optimization

Revised Bloom's Taxonomy (RBT) is proposed in general to look more forward in responding to the demands of the developing educational community, including how students develop and learn and how teachers prepare Learning Objects (LO). The variety of characteristics of students' abilities in a class has always been a problem that is often faced by a teacher. Unfortunately, cognitive classifications to develop students' knowledge to a high level have not been used to plan a learning path that is appropriate for their cognitive level. The purpose of this study is to recommend a learning path that matches the cognitive abilities of students from a learning object ontology. The method used in this research is Hybrid Particle Swarm Optimization (HPSO) which integrates Binary Particle Swarm Optimization (BPSO) and Discrete Particle Swarm Optimization (DPSO). The Connection Weight (CW) function is used to test the quality of the connection between the learning objects of an ontology subject controlled by the cognitive class. Based on experimental studies, the HPSO method can recommend a suitable learning path for cognitive classes, namely Low Cognitive (CL), Medium Cognitive (CM), and High Cognitive (CH). The similarity of the sequence of learning paths based on population in CL-class is 87.5, CM class 75, and CH class 87.5. © 2021 ASTES Publishers. All rights reserved.