PREDICTING DROPOUT RATES IN ONLINE LEARNING

This study focused on predicting student dropout in online learning environments using a data-driven approach and the Random Forest machine learning model. The results show that student dropout is mainly influenced by factors such as low engagement, poor academic performance, and inactivity. Students who frequently log in, spend more time on the platform, and complete assignments are more likely to finish their courses. The developed model performs effectively in identifying students who are at risk of dropping out. This proves that machine learning can be a useful tool in analysing student behaviour and making accurate predictions. By using such models, online learning platforms can take early actions to support students and reduce dropout rates. The study also highlights the importance of monitoring student activity and providing timely intervention. Features like progress percentage, days since last login, and assignment completion play a key role in predicting outcomes. In conclusion, this research demonstrates that combining educational data with machine learning techniques can improve student retention and enhance the overall effectiveness of online learning systems.

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