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

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) aggregation operator (AO). This approach enables us to combine multiple data sources, which reveals the impact of PL on individuals, captures the interrelation between various factors, and provides a more comprehensive and precise way to assess student progress. In this paper, we integrate the Bonferroni Mean (BM) AO by using the Aczel-Alsina (A-A) operational rules within the circular context, which provides a more realistic way to evaluate these assessments. The proposed approach demonstrates its practical application in optimizing personalized learning paths and enhancing student engagement by accurately assessing these complex criteria. Its effectiveness is validated through comparative analysis with existing methods. The results validate the use of our model, which impacts the accuracy of PL evaluations for educators in a scalable and empirical decision support framework.