A Hybrid Neuro-Fuzzy and Machine Learning Model for Student Performance Prediction in Online Mathematics Education

Accurate and early prediction of student success in online mathematics education is critical for improving learning processes and developing personalized instruction strategies. However, students' problem-solving behaviors, interaction levels, and learning speeds in online learning environments inherently contain uncertainty, limiting the effectiveness of traditional assessment and singular machine learning approaches. This study proposes a hybrid neuro-fuzzy and machine learning-based student success prediction framework that combines the ability of fuzzy logic to represent uncertainty with the strong generalization and adaptive learning capabilities of machine learning methods. In the proposed approach, student success trends are primarily modeled using ANFIS, Random Forest, and XGBoost models, employing raw and derived features on the ASSISTments dataset. The predictions from these models are treated as continuous success representations reflecting uncertainty in students' learning behaviors and are used as input to a hybrid classification structure to make binary success/failure decisions. Thus, the ANFIS model is positioned as an uncertainty-aware and interpretable context generator, while the Random Forest and XGBoost models provide discriminative classification power. Experimental results demonstrate that both ANFIS and Random Forest models exhibit high individual performance; however, combining their complementary features within a hybrid structure significantly increases prediction stability and generalization. Unlike the limitations of 'black box' models in the literature, the interpretability provided by ANFIS, through its linguistic rules and membership functions, enables the proposed approach to generate pedagogically transparent and actionable implications. The findings reveal that this hybrid framework, which integrates uncertainty management with high prediction accuracy, offers a powerful decision-support mechanism for early identification of at-risk students and for developing personalized intervention strategies in online mathematics education.