Personalized learning assessment for secondary education using a hybrid model of circular intuitionistic fuzzy and Aczel-Alsina bonferroni aggregation operator.

ABSTRACT: Personalized learning (PL) achievement in secondary schools is often questioned because it does not always lead to improved student performance, as it has consistently lacked strong data-driven models that can effectively address the uncertainty in student data. Although teachers tailor the lessons to meet the requirements of individual students, it is always challenging to measure if these efforts improve learning. This is because student preferences, strengths, and weaknesses generate a significant amount of unclear and complex data. To manage these complexities, our study introduced an innovative and novel decision-making approach to determining the effectiveness of personalized learning, utilizing the proposed Circular Intuitionistic Fuzzy Aczel-Alsina Bonferroni means (CIFAABM) aggreg

SUBMITTER: Imran R 

PROVIDER: S-EPMC12549940 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature