Comparison and Analysis of Multiple Machine Learning Algorithms for Predicting Student Adaptation Levels in Online Education

With the rapid development and popularization of Internet technology, online education has become a new way of education. Compared with traditional classroom teaching, online education has a more flexible learning mode, a more convenient learning environment and a wider range of learning resources. However, at the same time, online education also faces some challenges, one of the most important challenges is the adaptability of students to online education. In this paper, we use machine learning techniques to predict students' adaptability in online classrooms. After using logistic regression model, k-neighborhood algorithm model, random forest model, XGBoost model and Cat Boost model to make predictions, it is found that random forest model is the best in predicting students' adaptability to online classroom, with a prediction accuracy of 89.6%. The XGBoost model and CatBoost model were also better in prediction, with prediction accuracies of 89.1% and 88.6%, respectively. In contrast, the logistic regression and KNN models have poorer prediction accuracy with 68.8% and 77.1%, respectively. The research in this article has important implications for the online education industry. By using machine learning techniques to predict students' adaptability in an online classroom, it can help educational institutions better understand students' learning and improve teaching effectiveness. Meanwhile, for students, knowing their adaptive ability in online classroom also helps them to better plan their study programs and improve their learning efficiency. This study uses machine learning techniques to predict students' adaptive ability in online classrooms, and the results show that the random forest model performs the best in terms of predictive effectiveness. This study provides a useful reference for the online education industry and also provides some ideas for future research.