Machine Learning in Education: Predicting Student Performance Using Long Short-Term Memory Networks

This exploratory study investigates how Long Short-Term Memory (LSTM) networks—a particular kind of intermittent neural network—work to predict students’ academic success. Researchers seek to create models capable of divining students’ academic problems based on input data such as prior academic records, socioeconomic circumstances, and behavioral patterns, employing machine literacy techniques, especially LSTM networks. This project aims to find significant patterns and correlations that can improve our comprehension of the elements influencing student achievement by assaying large datasets. Compared to conventional statistical methods, the use of LSTM networks allows for the prisoner of temporal dependencies that are crucial in educational data, enabling more accurate prognostications. In the end, the investigation’s findings support the development of validated literacy systems.