Information Reconstruction of Student Management Work Based on Association Rules Mining

Area of Science:

• Educational Technology
• Data Mining
• Information Systems

Background:

• The increasing focus on education necessitates advancements in student management.
• Informatization of student management is crucial for modern educational institutions.
• Traditional methods present limitations in efficiency and scope.

Purpose of the Study:

• To explore the reconstruction of student management information systems.
• To apply association rule mining techniques to student data.
• To evaluate the impact of association rule mining on management efficiency.

Main Methods:

• Introduction to association rule mining and student management informationization.
• Development of a data mining algorithm for association rules.
• Application of the algorithm to mine student management data.
• Comparative analysis of traditional versus association rule-based management efficiency.

Main Results:

• Association rule mining-based student management achieved 64%-72% efficiency.
• Traditional student management methods showed 25%-35% efficiency.
• Significant increase in the adoption of information management in higher education institutions between 2017 and 2018.

Conclusions:

• Association rule mining substantially enhances student management efficiency compared to traditional methods.
• Simplifying information through data mining improves overall student management.
• Reconstructing student management information systems with association rule mining is vital for educational progress.