A Hybrid Machine Learning and Fuzzy Logic Model for Enhancing Teacher Perspectives on Curriculum and Pedagogy

Secondary schools are progressively accommodating international pupils, particularly from nations where English is not the primary language. This trend signifies a broader internationalisation in schooling. Financial incentives and the aspiration to foster intercultural understanding have prompted public schools to emulate private institutions that have historically offered specialised language instruction and accommodation for overseas students. There is a pressing need for flexible teaching approaches since these developments have such a dramatic effect on educators' views on curriculum and pedagogy. This research presents a data-driven way to improve pedagogical responsiveness in inclusive classrooms as a solution to these difficulties. The methodology comprises two primary components: first, preprocessing using the MIF approach to ensure data quality, and second, model training with a combination of RSA and ANFuzzy. ANFuzzy parameters are fine-tuned for best performance using RSA. Upon evaluation, the proposed model exceeded prior enhanced ANFuzzy-RFA methodologies, achieving an impressive accuracy of 96.57%. The results suggest that intelligent systems can aid with curriculum development and educational decision-making in globalised schools. The findings underscore the necessity of matching curriculum and pedagogy with the evolving dynamics of diverse student populations to enable instructors to provide more inclusive and effective training.