Academic Performance Trajectory

Academic performance trajectory research analyzes how student achievement changes over time, aiming to identify patterns and predictors of academic success or failure. Current research employs machine learning techniques, such as LSTM networks and Hidden Markov Models, to analyze large educational datasets and model complex performance patterns. These analyses reveal that while higher overall performance generally correlates with better outcomes, nuanced trajectories—including periods of improvement or decline—can also significantly influence final academic success, highlighting the need for more sophisticated predictive models in personalized education.