A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

TL;DR

This paper proposes a hybrid model combining a modified recurrent neural network with an adapted Grey Wolf Optimizer to accurately forecast student outcomes, aiming to improve educational interventions.

Contribution

It introduces a novel hybrid system integrating a modified RNN with an adapted GWO for student performance prediction, enhancing accuracy over existing models.

Findings

The hybrid model outperforms other predictive models in accuracy.

The system can serve as an effective early warning tool for student performance.

Improved instruction strategies can be developed based on the model's predictions.

Abstract

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.