Construction on precise-personalized-learning evaluation system based on cipp evaluation model and integrated FCE-AHP method.

The article focuses on constructing a precise personalized learning evaluation system based on the CIPP evaluation model, which includes three first-level indicators—pre-class preview, in-class teaching, and after-class consolidation—further divided into nine second-level and twenty-five third-level indicators. Using expert input via questionnaire surveys, the Analytic Hierarchy Process (AHP) method was applied to weight these indicators, and a fuzzy comprehensive evaluation method was employed to assess learners' performance dynamically throughout the learning process. An empirical study involving five online learners in an advanced mathematics course demonstrated that this system provides a comprehensive and objective evaluation of learning performance beyond final test scores, supporting personalized learning adjustments and performance prediction. The study acknowledges limitations related to the evolving nature of online and personalized learning modes and suggests future research to refine indicator weights through expanded expert consultation and adaptability across different platforms and learning contexts.