With the rapid advancement of internet technology, the adaptability of adolescents to online learning has emerged as a focal point of interest within the educational sphere. However, the academic community's efforts to develop predictive models for adolescent online learning adaptability require further refinement and expansion. Utilizing data from the "Chinese Adolescent Online Education Survey" spanning the years 2014 to 2016, this study implements five machine learning algorithms - logistic regression, K-nearest neighbors, random forest, XGBoost, and CatBoost - to analyze the factors influencing adolescent online learning adaptability and to determine the model best suited for prediction. The research reveals that the duration of courses, the financial status of the family, and age are the primary factors affecting students' adaptability in online learning environments. Additionally, age significantly impacts students' adaptive capacities. Among the predictive models, the random forest, XGBoost, and CatBoost algorithms demonstrate superior forecasting capabilities, with the random forest model being particularly adept at capturing the characteristics of students' adaptability.

翻译：随着互联网技术的飞速发展，青少年对在线学习的适应性已成为教育领域关注的焦点。然而，学术界在构建青少年在线学习适应性预测模型方面的研究仍需进一步完善和拓展。本研究利用2014年至2016年“中国青少年在线教育调查”数据，采用逻辑回归、K近邻、随机森林、XGBoost和CatBoost五种机器学习算法，分析影响青少年在线学习适应性的因素，并确定最适合的预测模型。研究发现，课程时长、家庭经济状况和年龄是影响学生在在线学习环境中适应性的主要因素。此外，年龄对学生的适应能力具有显著影响。在预测模型中，随机森林、XGBoost和CatBoost算法展现出更优的预测能力，其中随机森林模型尤其擅长捕捉学生适应性的特征。