A HYBRID FUZZY-AHP FRAMEWORK FOR PRIORITIZING FACTORS IN PEER-TO-PEER AND CHILD-TO-CHILD MOBILE LEARNING FOR CHILDREN

Abstract

This research formulates a hybrid fuzzy Analytic Hierarchy Process (fuzzy-AHP) to structure and assess the factors impacting the use of Peer-to-Peer (P2P) and Child-to-Child (C2C) mobile learning for children. Mobile learning systems provide a unique toolkit for interactive and collaborative learning. They pedagogical, technical, and social interactions factors of mobile learning environments and mobile learning systems. Mobile learning of children is a complex and. Children motivation. Mobile learning systems deploy systems and interactive learning materials. Evaluation using Traditional AHP methods entails using elements which are confined to sharp boundaries and differentiated from fuzzy sets and usually are unable to capture valuable nuances associated with the parameters set out by professionals. Evaluation using Traditional AHP methods entails using elements are is indifferent and usually are unable to capture valuable insights utilizing sharp boundaries. Empirical data were collected from a panel of 12 experts, including educational technologists, instructional designers, and child psychologists. The results reveal that pedagogical factors (weight = 0.411) are the most critical, followed by technological factors (0.323) and social-interactional factors (0.266). At the sub-criteria level, motivation and interactive content design emerged as the most influential determinants of mobile learning success. Comparative analysis demonstrates that fuzzy-AHP provides more consistent and reliable results than classical AHP, while sensitivity tests confirm the robustness of the findings. The research is novel in the theoretical domain as it applies fuzzy multi-criteria decision-making methods on child-centered mobile learning, and it is practical in that it directly provides tips to developers, teachers, and policy makers. This underscores the fact that mobile learning effectiveness is determined primarily by rigorous pedagogical frameworks, and offers decision support system for the mobile learning frameworks to be instituted in the future regarding their level of effectiveness and impact.