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<p>The ability to solve mathematics problems results from the relationship between critical cognitive skills and behavioral influences. This ability is important for ensuring academic success and can be applied in the real world. However, conventional assessment methods often fail to capture the multifaceted and uncertain nature of these abilities. Therefore, this study introduces a novel Single-Valued Neutrosophic (SVN) Entropy and Decision-Making Trial and Evaluation Laboratory (DEMATEL) based hierarchical Adaptive Neuro-Fuzzy Inference System (ANFIS) model to predict students’ mathematics problem-solving ability. The proposed model integrates SVN Entropy for attribute weighting in data preparation and SVN DEMATEL for the hierarchical structuring of its feature engineering modifications, thereby overcoming limitations commonly observed in traditional ANFIS models, especially in handling indeterminacy, vagueness, and complex causal relationships. The proposed model demonstrates superior predictive performance compared to state-of-the-art machine learning models due to its enhanced capability to manage uncertainty and cognitive, behavioral interactions. The model proves the credibility and reliability of the modifications implemented by recording an increase in prediction accuracy and thus reducing the error in it. Performance metrics show that the model configuration with three triangular membership functions is the most consistent by yielding improvements in root mean square error (RMSE) of 61.93%, mean absolute error (MAE) of 15.83%, and R² of 64.93% compared to the traditional ANFIS model. These results highlight the advantages and novelty of the proposed hybrid neutrosophic, fuzzy framework and contribute valuable insights to the mathematical sciences, particularly in using soft computing techniques to improve the predictive power of assessment models in education. This work further supports the development of a more synergistic framework for cognitive and behavioral assessment within complex humanitarian systems.</p>

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Improving educational predictive modeling with a hierarchical ANFIS approach based on neutrosophic entropy and DEMATEL

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Mohamad Ariffin Abu Bakar,
  • Ahmad Termimi Ab Ghani,
  • Mohd Lazim Abdullah,
  • Fatin Nadiah Mohamed Yussof

摘要

The ability to solve mathematics problems results from the relationship between critical cognitive skills and behavioral influences. This ability is important for ensuring academic success and can be applied in the real world. However, conventional assessment methods often fail to capture the multifaceted and uncertain nature of these abilities. Therefore, this study introduces a novel Single-Valued Neutrosophic (SVN) Entropy and Decision-Making Trial and Evaluation Laboratory (DEMATEL) based hierarchical Adaptive Neuro-Fuzzy Inference System (ANFIS) model to predict students’ mathematics problem-solving ability. The proposed model integrates SVN Entropy for attribute weighting in data preparation and SVN DEMATEL for the hierarchical structuring of its feature engineering modifications, thereby overcoming limitations commonly observed in traditional ANFIS models, especially in handling indeterminacy, vagueness, and complex causal relationships. The proposed model demonstrates superior predictive performance compared to state-of-the-art machine learning models due to its enhanced capability to manage uncertainty and cognitive, behavioral interactions. The model proves the credibility and reliability of the modifications implemented by recording an increase in prediction accuracy and thus reducing the error in it. Performance metrics show that the model configuration with three triangular membership functions is the most consistent by yielding improvements in root mean square error (RMSE) of 61.93%, mean absolute error (MAE) of 15.83%, and R² of 64.93% compared to the traditional ANFIS model. These results highlight the advantages and novelty of the proposed hybrid neutrosophic, fuzzy framework and contribute valuable insights to the mathematical sciences, particularly in using soft computing techniques to improve the predictive power of assessment models in education. This work further supports the development of a more synergistic framework for cognitive and behavioral assessment within complex humanitarian systems.