Title: Scheduling constraints and implementation in teaching management systems incorporating association rule mining algorithms

Authors: Jin Yan; Meng Li; Xiaoyan Ma; Junmian Wang

Addresses: Department of Basic Teaching, Hebi Automotive Engineering Professional College, Hebi, Henan, 458030, China ' School of Arts and Business, Xi'an Siyuan University, Xi'an, Shaanxi, 710038, China ' School of Arts and Business, Xi'an Siyuan University, Xi'an, Shaanxi, 710038, China ' School of Physical Education, Yan'an University, Yan'an, Shaanxi, 716000, China

Abstract: This paper examines class schedules, precautions, and association rule algorithms and builds a more scientific class scheduling system. Data mining technology association rules handle scheduling conflicts. This method extracts efficient negative sequence rules from patterns. Using local utility value and utility confidence, e-HUNSR formalises the problem of efficient negative sequence rules, generates candidate rules and a pruning strategy quickly, designs a data structure to store the necessary information, and proposes an efficient way to compute the antecedent local utility value and a simplified utility value calculation. Association rules and mining are used to solve the scheduling problem. The system can conduct course queries, OSes, and performs well in data mining. After experimental verification, the hybrid method with different scheduling condition criteria obtains 98.12% course selection satisfaction. Rule satisfaction averages 94.98%, and intelligent scheduling system scheduling efficiency is 91.91%. Adding fresh ideas and methods to the intelligent scheduling system increases instructional resources and university scheduling. Smarter university timetable management allocates teaching resources and completes education and teaching plans.

Keywords: association rules; data mining; scheduling algorithms; scheduling constraints and implementation; teaching management system; incorporating association rule; mining algorithm.

DOI: 10.1504/IJKBD.2024.141628

International Journal of Knowledge-Based Development, 2024 Vol.14 No.3, pp.229 - 248

Received: 11 Dec 2023
Accepted: 16 Apr 2024
Published online: 27 Sep 2024 *

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