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September 16, 2025CogITo Smart JournalOpen Access

Expert System for Learning Styles Diagnosis Using Dempster–Shafer and Bayesian Network

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Authors

TPTesalonika PalilinganKKKrismiyati KrismiyatiTWTeguh Wahyono

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Overview

This analysis combines Bayesian Networks and Dempster-Shafer Theory for reliable learning styles diagnosis, enhancing personalization.

Key Points

  • The approach improves the accuracy of diagnosing learning styles, achieving an 86.67% match rate with expert evaluations.
  • A hybrid inference method integrates Dempster-Shafer and Bayesian Networks to manage uncertainty in student assessments.
  • System development follows the Expert System Development Life Cycle for a structured creation process.
  • Findings highlight the effectiveness of combining belief and probabilistic inference to enhance adaptive learning recommendations.

Cite This Study

Palilingan et al. (2025) studied this question.

synapsesocial.com/papers/68d4565431b076d99fa5ae07https://doi.org/10.31154/cogito.v11i1.943.126-139
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identification of Student Learning Styles based on Multiple Intelligences Aspects using the Dempster-Shafer Method2024
  2. 2Expert System for Student Talent and Interest Using Certainty Factor and Dempster-Shafer Methods2025
  3. 3Bayesian Intelligent Tutoring System for Vocational High Schools2024
  4. 4An Intelligent Tutoring System for Identification of Learning Styles and Assignment Educational Strategies2024 · 2 citations
  5. 5Expert System for Early Childhood Talent Detection Using Certainty Factor and Dempster Shafer Algorithms2025