Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

Abstract

With the increasing popularity of higher education and the increasing scale of students in schools, the educational administration management system of various colleges and universities has also accumulated a large amount of performance data. In the face of these massive data, many teaching managers still stay in the simple operation of adding, deleting, modifying, and checking the data and cannot effectively extract and analyze the useful knowledge and information hidden behind the data. Therefore, Markov model is proposed. The advantage of the Markov model is that it has better prediction effect on random series and data series with large volatility; that is, grey prediction model is used to reveal the overall trend of development and change of prediction data series. This paper studies the curriculum association classification model and student achievement prediction based on the Markov model. According to the student’s historical achievement, the average missed detection rate of future grade point is 48.65%, the average missed detection rate of Apriori algorithm is 35.5%, the average missed detection rate of FP growth algorithm is 43.2%, the average missed detection rate of this algorithm is 37.5%, and only 17 of the 40 grade point interval predictions match the actual interval. Make full use of the association rules between courses to provide students with early warning and teacher guidance for specific courses, which can effectively reduce the failure rate while reducing the academic burden. The Markov model can mine students’ data inside and outside the classroom, establish and improve college students’ achievement index system, then deeply analyze and discuss the development of college students’ achievement and ability, and finally give each student’s achievement results.