Research on English Achievement Analysis Based on Improved CARMA Algorithm

Abstract

This paper uses data mining technology to analyze students’ English scores. In view of the influence of many factors on students’ English performance, the analysis is realized by using the association rule algorithm. The thesis analyzes and applies students’ English scores based on association rules and mainly does the following work: (1) at present, the problem of the CARMA algorithm is low operating efficiency. The combination of the genetic algorithm’s crossover, mutation, and the CARMA algorithm realizes the fast search of the algorithm. The simulation results show that the operation performance of the algorithm is greatly improved after the crossover and mutation operations in the genetic algorithm are applied to the CARMA algorithm. The simulation results show that the mining accuracy of the improved algorithm is 97.985%, and the mining accuracy before the improvement is 92.221%, indicating that the improved algorithm can improve the accuracy of mining. (2) By comparing the mining time of the improved CARMA algorithm, the traditional CARMA algorithm, the FP-Growth algorithm, and the Apriori algorithm, the results show that when the number is 6,500, the mining efficiency of the improved CARMA algorithm is twice that of the other three algorithms. As the amount of data increases, the effect of improving mining efficiency gradually increases. (3) By using the improved CARMA algorithm to analyze students’ English performance, it is found that the quality of student performance is strongly related to the quality of daily homework, and if it is related to the teacher’s gender, professional title, etc., it is recommended that schools should pay more attention to homework during the teaching process.

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“…CARMA is similar to Apriori analysis but it does not require input (predictors) or target (predicted) fields (variables). Therefore, all variables are set at both [56].…”

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“…Association rule mining (ARM) is a rule-based machine learning algorithm that can discover hidden patterns and interesting relationships in large databases. Recently, ARM has become a promising technique in multiple fields including biomedical, educational, and social sciences, such as predicting COVID-19 cases and symptom patterns ( 9 , 10 ), the application of multiresource for MOOC teaching ( 11 ), the study of improving English achievement analysis ( 12 ) and the investigation of the relationships between shifts in digital skills and cybersecurity awareness ( 13 ). Common analysis algorithms and evaluation approaches for ARM include the Apriori algorithm ( 14 ), entropy weight method (EWM) ( 15 ), technique for order preference by similarity to ideal solution (TOPSIS) ( 16 ), support vector machine (SVM) and random forest (RF) ( 17 ).…”