Application Research On Data Mining Technology In The Teaching Evaluation And Employment Analysis Of Vocational High School

In recent years,secondary vocational education with a wide range of changes in the social system,funding system,employment distribution policies,have to get out of the school,and face the market.It is always in a situation of survival of the fittest,and the development of the school is also faced with many pressures and challenges.But most of the school management information system is only limited to the simple processing of the data,it lack investigation and analysis of these data among the evaluation of teachers,curriculum,student achievement and student employment.Therefore,the school cannot be a better teaching reform and effective employment guidance for students,resulting in the slow development of vocational schools,the obvious contradiction between supply and demand.And it also lead a serious impediment to the development of vocational schools.When faced with large amounts of raw data vocational schools in recent decades,how to quickly and accurately identify find out effective information,which provide a reference guide to the school curriculum and teaching methods is the purpose of the study.It has become a new challenge for vocational education institutions managers to adapt to social development and market change and improve teaching and the quality of employment by using a lot of raw business data effectively which has been accumulated.This paper focused on the related theory about data mining,and analyzed the characteristic of the Apriori algorithm which based on iterative and pruning emphatically.We introduced a improved algorithm to find more frequent items hidden in the data set by researching the problems which exist in classical algorithms.What's more,this paper put forward a new Apriori algorithm after improving association rules,which can solve the problems just like the diversification of importance between items caused by the factors of curriculum,teaching evaluation,choice of employers,etc.In this new algorithm we introduced the concept of the impact factor,and combined with the fast pruning mechanism of address mapping,which optimized the candidate sets pruning efficiency and cut down the algorithm's time and space complexity by reducing the number of generated frequent item sets.The new algorithm can discover the inner relationship between item sets more effectively,accurately and deeply,which means it's in line with the current occupation education background.In the experimental stage,this paper verified the effectiveness of the improved algorithm by the related experiment.Finally,this paper applied the new algorithm to employment analysis and teaching evaluation in vocational education,found the internal connection in raw data and mined key factors and rules on a deeper level after the pretreatment,cleaning steps on it.The mining results provided reasonable suggestions for improvement and optimization in many ways such as the curriculum,teaching mode,evaluation methods,employment direction,and provided support for the perfect teaching management system and leadership decision-making.