Digital and Intelligent Empowerment for Precision Teaching: A Process-oriented Evaluation Model for Higher Mathematics Blended Learning
Wang Na *
Shenyang Normal University, School of Mathematics and Systems Science, Shenyang, China.
*Author to whom correspondence should be addressed.
Abstract
Aims: To solve problems like strong subjectivity, unbalanced weights, and lagging evaluation dimensions in blended teaching of Higher Mathematics, and build a process-oriented comprehensive evaluation model empowered by digital intelligence for precision teaching.
Study Design: Constructed an evaluation system and comprehensive evaluation model based on dual-drive logic of Delphi method and Analytic Hierarchy Process (AHP).
Place and Duration of Study: Shenyang Normal University, School of Mathematics and Systems Science, between December 2025 and February 2026.
Methodology: Established a 15-member interdisciplinary expert consultation pool; screened and optimized indicators through two rounds of Delphi surveys; used AHP to determine indicator weights and conduct consistency tests.
Results: The results of AHP weight determination and consistency tests indicate that the "Teaching Support" dimension holds a dominant position, among which "Automated Learning Early Warning and Intervention Response Efficiency" has the highest composite weight (0.112), emerging as a core variable driving precision teaching.
Conclusion: The research demonstrates that the focus of evaluation has achieved a paradigm reconstruction from "end-product output" to "process intervention". This model provides decision-making support for the precise implementation of Higher Mathematics teaching reform and offers an operational framework for quality assessment in the digital transformation of higher education.
Keywords: Higher mathematics, blended teaching, digital and intelligent empowerment, process-oriented, evaluation model