Research on Innovative Modes of College English Teaching Based on Data Mining and Intelligent CAI

In the era of big data, student learning data have become an important basis for evaluating teaching effectiveness and guiding teaching direction. However, in the current English teaching process in many universities, there is still insufficient collection, organization, and analysis of student learning data. This makes it difficult for teachers to comprehensively and accurately grasp the learning situation and needs of students, thus being unable to provide targeted teaching guidance. Data mining technology provides teachers with the possibility of gaining a deeper understanding of student learning by collecting and analyzing a large amount of teaching data. The intelligent CAI system can automatically adjust teaching content and difficulty based on students’ English proficiency and learning progress, providing tailored learning resources for students. This study proposes an innovative English teaching model based on data mining and intelligent CAI. By collecting learning data from students, including learning duration, grades, interactive behavior, etc., data mining algorithms are used for in-depth analysis to reveal their learning characteristics, difficulties, and interests. These analysis results not only help teachers better understand the learning status of students, but also provide strong support for optimizing teaching content and personalized teaching. Meanwhile, this study also focuses on the application of intelligent CAI technology in college English teaching. The intelligent CAI system provides personalized learning resources and real-time feedback to students by simulating the teaching behavior of human teachers. The system can intelligently recommend learning materials and arrange learning plans based on the learning situation and needs of students, and provide timely guidance and assistance when students encounter problems. This personalized learning approach can stimulate students’ interest in learning and improve learning efficiency.