Design and Analysis of Dynamic Stratified Teaching of Advanced Mathematics Driven by Artificial Intelligence Archives

DOI: https://doi.org/10.62517/jike.202604306

Author(s)

Chao Zhang*, Haixia Lu, Haiqing Zhou

Affiliation(s)

Nanjing Tech University Pujiang Institute, Nanjing, Jiangsu, China *Corresponding Author

Abstract

This study explores the necessity and practical value of differentiated teaching reform in advanced mathematics based on the school-running features and talent cultivation objectives of application-oriented universities. It develops a data-driven intelligent student learning profile platform and applies quantitative analysis to evaluate students’ learning status, habits and academic progress, enabling dynamic and accurate implementation of tiered teaching. From the perspective of technical pathway design, this paper proposes multi-dimensional dynamic teaching optimization strategies covering student ability classification, hierarchical teaching objectives, modularized teaching content, diverse teaching methods and comprehensive evaluation systems based on intelligent data monitoring. Adopting statistical analysis to objectively assess students’ learning behaviors and academic performance, this research addresses major bottlenecks in traditional advanced mathematics teaching. Under the background of intelligent education, this study aims to improve the overall teaching quality of advanced mathematics, cultivate students’ innovative and practical abilities, and promote the high-quality achievement of talent cultivation goals in tertiary institutions.

Keywords

Intelligent Archives; Advanced Mathematics; Stratified Teaching; Statistical Analysis

References

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