Analysis of Teaching Strategies of College English Speculative Reading Based on Big Data Analysis of Student Behavior in Cross-Cultural Education Environment

Abstract

Both dynamic and interactive, cross-cultural education emphasizes respect, interaction, and understanding between various cultural groups. Its fundamental principle is to acknowledge and value cultural diversity while opposing attempts to artificially unite cultures in order to recognise the equality and common heritage of all cultures. The goal of intercultural education, from an individual's perspective, is to support those who are educated in understanding, accepting, and respecting other foreign cultures in order to better adapt to and thrive in a multicultural society. To develop students' intercultural communication skills is the goal of intercultural education. Based on the associations between intercultural education and speculative reading, this essay analyzes the crucial role that intercultural English teaching and speculative reading instruction play in colleges and universities before going over the situation and approaches that English speculative reading instruction is currently facing. This paper primarily discusses the data on students from the student work management system, the relationship between grades and actual behavior, and the rules that are mined to provide a scientific basis for decision-making for the relevant units in colleges and universities. According to experimental analysis, the data mining algorithm in this paper performs better than the other two in terms of predicting students' English achievement, with an average accuracy of 0.86 compared to 0.71, 0.075, and 0.73 for the other three algorithms. The decision tree created using this methodology can quantitatively reflect the impact of different teaching factors on learning quality and offers a clear and reliable foundation for formulating targeted improvement strategies.