Achievement Analysis And Lecture Setting Research Based On Association Rules

Researching on data mining technology has a history for many years, which attracteda lot of research institutions, organizations, and schools with fruitful results. Universityteaching management in foreign countries, Data mining has become an effective tool forimproving the teaching level and the teaching management quality. Leading the datamining technology into the educational management system of higher vocationaleducation, and analyzing existing data with multi angle, will undoubtedly help themanagers to make decisions, but also can improve the students’ competence.Aimed at the present student achievement management part of educationalmanagement system, the application of data mining technology in scientific in-depthanalysis of higher vocational college students’ score can help the manager to understandthe individual difference exists and the gains and losings of students in the learningprocess. Via the in-depth analysis of the course information by data mining, therelationship between the curriculum and the requirements of enterprises can be known,further more, the teaching quality can be improved in the future. The main researchcontents are as follows:This thesis analyzed the students’ achievement management and curriculummanagement of the present educational management system, and used the associationrules algorithm to find out the potential factors affecting student achievement andcurriculum setting. According to the actual date source, basing on the analysis of theinfluence relation among courses and the influence relation between course and score,setting the mining association rules as standard, analyze the students’ achievement effect,basic information, and Professional course data, seek the correlation ship betweencurriculum and major requirement, curriculum and students’ achievement, find out theimportant factors that affect the students’ achievement and curriculum setting, and putforward impeccable aim and idea. Especially the thesis did the research on theimprovement of AprioriTid algorithm, which improved the count of candidate dateitem-sets’ supporting degree. While the single date item was smaller than minimumsupporting count in the frequent date item-sets, delete the element which contained thesingle date item. Thus, reduce the scale of frequent date item-sets and the number ofregulation needed to be considered effectively, get rid of the elements that can’t generate the efficient candidate date item-sets, and enhance the efficiency of connecting step.