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

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.

34 pages, published in PLoS ONE

References in corpus (2)

• Mining Educational Data to Analyze Students' Performance
• Lecturer Performance System Using Neural Network with Particle Swarm Optimization

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