Development of a Hybrid Machine Learning Model for Analyzing Behavioral Patterns in Educational Mobile Applications

This paper presents a hybrid machine learning model to analyze user behavior patterns in educational mobile applications for engagement frequency, session lengths, and interaction events. The new model combines Decision Trees and Neural Networks into a unique system that predicts learning outcomes accurately rather than deviating toward accuracy-guided prediction models. The research data has been collected over the past six months with approximately 10,000 middle school students aged 12-15 using the educational app, all of which contributed to a dataset of 50,000 user sessions. So extensive data collection was, as discussed in the Data Collection section, that it enabled our model to outperform standard methods such as logistic regression, k-NN, and support vector machines at an accuracy of 91 percent. This approach can be applied to app design to personalize learning experiences and teacher training approaches, focusing on the frequency of engagement and range of interaction diversity. The findings will facilitate the development of enhanced, student-centered educational apps, while personalized learning environments will provide useful information to the developers and educators on optimizing use for learning in mobile educational App development.