Study on the Learning Effect of Intelligent Foreign Language Learning System based on Data Mining Technology

Abstract

The intelligent foreign language learning system has become an indispensable learning tool for college students. The rich learning resources in the system can help students improve their foreign language viewing, listening, speaking, reading, writing and translation. This study collected the learning behavior data generated by some students of China University of Geosciences (Wuhan) in the intelligent Foreign Language learning system, and selected the total learning time, the average length of stay in each online learning, the daily homework scores, and the number of discussions and exchanges as the feature data. After data preprocessing, K-means clustering algorithm is used for cluster analysis. Setting the number of clusters to 4 and the number of iterations to 20 can obtain better analysis results. The results of cluster analysis show that the learning behavior of students in the foreign language intelligent learning system is closely related to the learning effect, and the students with excellent learning behavior tend to have a better learning effect. Teachers can use the data mining method provided by this research to conduct a regular cluster analysis of students' learning effects, and timely give warning of learning and teaching intervention according to the analysis results.