The Cross-Cultural Teaching Model of Foreign Literature Under the Application of Machine Learning Technology

Abstract

As globalization spreads, so does the world's ethnic makeup, leading to a surge in cultural diversity that has become a major issue for the educational systems of all countries. Many western countries advocate for cross-cultural education (CCE) as a means of dealing with cultural variety and promoting trust, tolerance, and interaction between individuals of different backgrounds. The way to achieve this goal is to work toward solving the issue while also fostering greater national unity. One of the newest developments in international education is the concept of CCE, which has also given rise to a whole new area of research within the subject of education. Too much time has passed since students were actively engaged in the learning process, and the new curriculum reform has seriously harmed the traditional approaches to teaching foreign literature. As a result, the proposed research has shown that around half of all education is dedicated to the FL cross-cultural teaching paradigm. Chinese students' data were first gathered for this study and divided into two groups: Control Class (CC) and Experimental Class (EC). The performance of the students in both groups is then forecasted using the extreme gradient boosting (XGBoost) technique, which is based on machine learning. Then, we use an optimization method known as the Flower Pollination Algorithm (FPA) to improve XGBoost's prediction performance. According to the descriptive findings, students who adhere to the suggested teaching strategy show more learning interest than those who adhere to existing strategies.