Analysis and Implementation of Key Technologies for Teaching Quality Evaluation System in Universities Based on Data Mining
Mingwei Li, Yongfang Li · 2019
Contraposing to the problems of professionalization of curriculum design, subjective fairness of teaching evaluation and diversity of importance among itemsets caused by selection factors of employers, an improved Apriori algorithm for mining association rules is proposed by introducing the concept of influence factors and combining with the fast pruning mechanism of address mapping. For the problems of indistinguishability of importance and large candidate itemsets in classical algorithms, a fast pruning of candidate itemsets is accomplished by address mapping, and the pseudo-code description of the algorithm is provided and explained in detail. Then, the performance of the improved algorithm is verified and analyzed, which shows the feasibility and efficiency of the algorithm, to make help in teaching evaluation system of universities.