This paper presents a methodology for predicting student performance using neutrosophic sets and deep learning techniques. The proposed approach involves feature selection and representation using neutrosophic sets, followed by model development using a suitable deep learning architecture. Uncertainty integration is achieved by incorporating neutrosophic values during training using specialized activation functions and modified loss functions. The model's performance is evaluated using appropriate metrics, and interpretation techniques are employed to understand the decision-making processes. Ethical considerations regarding student data collection and usage are also addressed. The proposed methodology offers a novel approach to student performance prediction that considers uncertainty and provides insights into the decision-making process, which can help educators, identify areas for improvement and provide targeted interventions.

Keywords: Neutrosophic sets, Deep Learning, Student Performance Prediction, Uncertainty Integration, Feature Representation

Keywords: Neutrosophic sets, Deep Learning, Student Performance Prediction, Uncertainty Integration, Feature Representation

Keywords: Neutrosophic sets, Deep Learning, Student Performance Prediction, Uncertainty Integration, Feature Representation