Research Of The Method To Predict Students’ Career Development On EDM

Our country has increased it’s investment in tertiary education.The enrollment scale of colleges has also expanded.The number of students in school is increasing.So most ordinary colleges are faced with the problem of student`s employment difficulty.Graduates’ career development is influenced by factors such as individual quality,family background,employment policy,etc.But the impact of the education boot in universities is still a major factor.The ministry of education also issued a series of notification documents on the status quo.Therefore,in college education,how to conduct career development direction for students reasonable,effective,and timely become an important task.But in the context of the rapid development of educational informationization,in the case of a teacher’s job,there is a huge amounts of data has been accumulated in different computers in different forms.It’s not being used at a deeper level,so this caused the data to be largely idle.In the face of this,We found that it is necessary to predict the future career of college students in the course of the study.Effective forecasting methods can provide objective,simple data support and theoretical basis for educator,this will be help to implement education guidance.Therefore,this article has conducted a series of studies.First,this articleanalyzes the problems of education in college career,then analyzes the problems of education in college career.After that,in order to determine the dimensions of the study,we do some work aboat the related theory and common method of education data mining are analyzed and the factor dimension analysis of the career development direction of college students.Andthen modeling the predictive models,designed and implemented the algorithm,the example test of prediction method,etc.According to the above study,the author has the following conclusion: Grey prediction algorithms perform better on small and medium size data forecasts;Based on the comprehensive quality assessment data,the prediction can be made more objective and accurate.But there is still some problems with in this study,we can try to adjust the parameters by more data and more experimental validation in future studies to optimize the algorithm.