Quality Evaluation of Chinese-Foreign Cooperative Schools Based on Machine Learning

Abstract

This study investigates the use of linear regression modelling to evaluate the quality of Chinese-foreign cooperative schools. In an era of globalization and educational philosophy convergence, collaborative ventures serve as dynamic venues for innovation and cross-cultural exchange. However, maintaining the success and excellence of such partnerships necessitates the use of robust evaluation frameworks that can capture the complex nature of educational quality across varied cultural contexts. Traditional assessment methods frequently fall short of addressing this complexity, necessitating the use of data-driven procedures such as linear regression modelling. Using a wide range of educational indicators and demographic data, linear regression provides a systematic and transparent approach to measuring educational quality, allowing stakeholders to identify significant predictors and drive evidence-based decision-making. This study lays the theoretical groundwork for using linear regression to evaluate Chinese-foreign cooperative schools by conducting a thorough analysis of relevant literature and methodology. Using multidisciplinary ideas from education, statistics, and machine learning, the study creates a framework for examining predictors of educational success and providing solutions for improving teaching and learning outcomes in cross-cultural collaborations. By giving practical insights and supporting ongoing improvement in educational practices, this research helps to progress educational quality evaluation in an increasingly linked world.