Application of Improved Association Rule Algorithm in Teaching Management Systems of Colleges and Universities

Abstract

Teaching management systems (TMS) are comprehensive platforms designed to streamline various aspects of educational administration, instruction, and communication within academic institutions. These systems typically offer features such as course scheduling, grade management, attendance tracking, and communication tools for instructors, students, and administrators. TMS also facilitates content delivery, assessment creation, and student progress monitoring, providing a centralized hub for all aspects of teaching and learning. With the integration of modern technologies like cloud computing and mobile applications, TMS enhance accessibility, efficiency, and collaboration among stakeholders in education. This paper explores the integration of Frequent Pattern Decision Support Systems (FP-DSS) into Teaching Management Systems (TMS) and its impact on teaching and learning practices in higher education. Through a comprehensive experimental analysis, we investigate the effectiveness of FP-DSS in improving learning outcomes, personalization, and student engagement across various teaching methodologies. The findings reveal significant improvements in learning outcomes, with average exam scores increasing by up to 12% when FP-DSS is incorporated into innovative teaching methodologies. Additionally, we observed enhanced personalization of instruction, with a rating of 9 out of 10 for the effectiveness of FP-DSS in tailoring learning experiences to individual student needs. Furthermore, student engagement showed notable improvements across all experiments, with students actively participating in the learning process and demonstrating higher levels of motivation and interest.