Association Analysis of University Course Information Based on Knowledge Map

In order to optimize educational decision-making and curriculum reform, trace the undergraduates' current development situation and forecast the follow-up study status, this paper adopts association rule algorithms to analyse the educational variables such as students' grades and curriculum selections information. The dataset referred in this paper is bigger, fresher, and more compound compared with the previous version. Based on those undergraduates' pre-existing data, pruning strategy is used to divide and process the dataset mathematically. By implementing modified Apriori algorithm, several significant association rules are dug out. In association rule mining, the Booleanized transaction data, optimized connection and pruning processing help us to find frequent item sets fleetly. Not only the dimension of the project set increases but also the efficiency of the modified algorithm are greatly improved. Through a rigorous survey of the experimental results, we carried out extensive analyses based on the heuristic knowledge of curriculum map. There is a certain degree of correlation hidden in the students' compulsory course scores. In the choice of public elective courses, some association rules related to the innate character of public elective courses are obtained. The analysis results provide fundamental basis for the construction of school public course map and the innovation of teaching method.