Article: Logistic regression mathematical algorithms based on alternating direction method of multipliers in higher education teaching Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2025 Vol.17 No.8 pp.1 - 10 Abstract: Academic performance prediction has become a crucial instrument for education management as higher education institutions keep improving the quality of their offerings. Many times lacking accuracy and great computing complexity, traditional academic performance prediction methods suffer. Thus, this work presents a logistic regression model based on the optimisation of alternating direction method of multipliers (ADMM) which is named Edu-ADDM-LR. By including ADMM into the logistic regression model, the model improves its predictive and generalising capacities as well as optimises the computing process. The experimental results show that the Edu-ADMM-LR model can efficiently manage the variety and complexity of students' performance in higher education teaching. Concurrently, the model shows great computational efficiency, great adaptability and stability in handling big-scale educational data. This work offers reliable decision support for educational managers and a fresh approach for academic achievement prediction in higher vocational colleges. Inderscience Publishers - linking academia, business and industry through research

Title: Logistic regression mathematical algorithms based on alternating direction method of multipliers in higher education teaching

Authors: Huaizhe Zhang; Min Guo

Addresses: Basic Teaching Department, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China ' Basic Teaching Department, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China

Abstract: Academic performance prediction has become a crucial instrument for education management as higher education institutions keep improving the quality of their offerings. Many times lacking accuracy and great computing complexity, traditional academic performance prediction methods suffer. Thus, this work presents a logistic regression model based on the optimisation of alternating direction method of multipliers (ADMM) which is named Edu-ADDM-LR. By including ADMM into the logistic regression model, the model improves its predictive and generalising capacities as well as optimises the computing process. The experimental results show that the Edu-ADMM-LR model can efficiently manage the variety and complexity of students' performance in higher education teaching. Concurrently, the model shows great computational efficiency, great adaptability and stability in handling big-scale educational data. This work offers reliable decision support for educational managers and a fresh approach for academic achievement prediction in higher vocational colleges.

Keywords: academic performance prediction; higher education; alternating direction method of multipliers; ADMM; logistic regression; educational data analysis.

DOI: 10.1504/IJRIS.2025.147120

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.8, pp.1 - 10

Received: 30 Mar 2025
Accepted: 17 May 2025
Published online: 10 Jul 2025 *

Title: Logistic regression mathematical algorithms based on alternating direction method of multipliers in higher education teaching

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