A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
This work addresses student performance prediction for educational institutions, but it is incremental as it combines existing methods.
The paper tackled the problem of forecasting university students' outcomes to improve learning and provide early warnings, achieving the best accuracy compared to other models.
Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.