Computational Models for Reducing Cognitive Overload in Business Administration Students

The study developed a predictive computational model to identify and reduce cognitive overload in undergraduate business administration students in digital environments. A quantitative approach was applied using academic, cognitive, and technological variables related to digital fatigue, academic stress, and the use of virtual platforms. Machine learning algorithms such as logistic regression, SVM, and Random Forest were implemented and complemented with computational simulation. The results showed that Random Forest achieved the best predictive performance and made it possible to detect critical scenarios of cognitive saturation. It is concluded that computational models can contribute to the design of intelligent educational systems oriented toward academic well-being.