Machine Learning-Based Research on the Adaptability of Adolescents to Online Education
This work addresses the need for better predictive models of adolescent online learning adaptability, which is an incremental improvement for educational researchers and practitioners.
This study tackled the problem of predicting adolescent adaptability to online education by analyzing survey data from 2014-2016 using five machine learning algorithms, finding that random forest, XGBoost, and CatBoost performed best, with random forest being particularly effective at capturing adaptability characteristics.
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.