MOOC and Flipped Classroom Task-Based English Teaching Model for Colleges and Universities Using Data Mining and Few-Shot Learning Technology

Abstract

As a revolutionary education model, the flipped classroom teaching model has unique advantages over the traditional education model. How to change the teaching method of flipped classroom into a teaching method suitable for college English courses is a problem. The goal of this research is to investigate how to use data mining and few-shot learning technology to investigate the impact of MOOC and flipped classroom task-based college English teaching modes. This work provides a data mining-based decision tree algorithm and examines the enhanced decision tree method. The experimental results of this study demonstrate that two students in each of the two groups believe that the English teaching mode, which is mostly taught by traditional teachers, is very favorable to a thorough understanding of basic knowledge, accounting for 4% of the total. There are 17 students that believe the new teaching methodology is really beneficial, accounting for 34% of the total. This mode was determined to be effective for in-depth knowledge of the basics by a total of 25 students. It can be seen that the flipped classroom model is more popular with students.