Analysis of the Practice Path of the Flipped Classroom Model Assisted by Big Data in English Teaching

Abstract

This paper makes a detailed analysis of the integrated mining algorithm, analyzes the characteristics of curriculum big data, and analyzes the existing problems of the current association rule mining algorithm, as well as the defects and deficiencies when applied to the curriculum data. Aiming at the problem of mining the entire data set by the mining algorithm, this topic proposes the idea of using the K-means algorithm for clustering processing and uses the Ball-tree structure on the basis of the original K-means algorithm to improve the efficiency of the algorithm. The data set is separated into several clusters of an appropriate number. In the flipped classroom, the basic knowledge is put before the class for learning, and the further deepening and practical application of language knowledge is completed in the class. Teachers can give timely guidance when encountering unsolvable difficulties so that students’ learning can be more effective. This new teaching model not only strengthens students’ confidence in learning and increases their interest in learning, but also increases the opportunities for students to interact with teachers and classmates in the classroom, allowing them to construct the meaning of knowledge in the fun of interactive communication. The classroom has become relaxed, lively, and attractive, and students’ sense of autonomy, self-learning ability, and collaborative inquiry ability have also been unknowingly improved. Among the main factors, the willingness to flip, emotional state, leadership role, and online learning input have a significant positive impact on collaborative learning performance, and the sense of competition has a partial negative impact on collaborative learning performance, of which positively affecting individual knowledge mastery. Among the nonmain factors, the degree of difficulty of the course, teacher-student interaction, teacher motivation, and evaluation mechanism have a significant positive impact on collaborative learning performance. Classroom assistive technology has a partial negative impact on collaborative learning performance. From the perspective of group performance, group-level performance considerations such as the quality of group conversations and the degree of group knowledge sharing in collaborative learning performance are more affected by nonsubject factors.