Forecasting Analysis of Online Learning Activity Using Machine Learning Models
Ricardo RodrÃ­guez Jorge
International Journal of Web Applications, Vol. 18, No. 2, 2026.
DOI: https://doi.org/10.6025/ijwa/2026/18/2/39-65
Link: https://www.dline.info/ijwa/fulltext/v18n2/ijwav18n2_1.pdf
Abstract:
The rapid expansion of digital learning ecosystems and online educational platforms has generated substantial
educational interaction data that can be utilized for intelligent forecasting and educational decision making.
The COVID-19 pandemic further accelerated global dependence on online learning systems, thereby
increasing the need for predictive analytical frameworks capable of understanding temporal educational
behavior evolution. This study presents a machine learning based forecasting framework for predicting
online learning activity trends using the dataset, â€œA.....Covid-19â€. Dataset on Online Learning based Web
Behavior from Different Countries Before and After COVID-19. The proposed framework integrates Random
Forest Regression, Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) for both
single-country and simultaneous multi country forecasting. Comparative benchmarking was conducted
using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error
(MAPE). The results demonstrate that XGBoost achieved superior forecasting performance because of its
gradient-boosting optimization capability and nonlinear feature-learning efficiency. The developed
framework further demonstrates the feasibility of AI-driven educational forecasting for large scale
educational trend monitoring, digital infrastructure planning, and intelligent online learning analytics. The
study also identifies several research directions involving explainable AI, deep learning forecasting
architectures, and ethical educational intelligence systems.