The Application of Association Rules in Teaching Evaluation

In order to discover frequent itemsets from a given dataset and the connections between data, the author proposes a more efficient matrix association rule algorithm based on bit vectors for application in teaching evaluation. The author introduced the relevant concepts of association rules and the Apriori algorithm. An enhanced algorithm for association rules, addressing the limitations of the Apriori algorithm, was introduced, leveraging bit vectors for improved efficiency and performance. Its effectiveness was demonstrated through experiments, and the algorithm was used for association mining of data items in teaching evaluations. The experimental results show that using the association rule algorithm of bit vectors to mine association rules on the generalized data table, with a minimum support of 50% and a minimum confidence of 80%, teachers with a master's degree or above are usually male teachers, aged between 30 and 40 years old, and have good teaching ability and effectiveness. At the same time, it can also be seen that teachers with good teaching abilities have good teaching attitudes, while teachers with good teaching attitudes usually have good teaching abilities. experiments have shown that the algorithm has certain feasibility and effectiveness, and can be successfully applied to the evaluation of teaching quality.