Vol. 5 No. 1 (2025): Issue 5
Articles

Recognition and Detection of Student Emotional States through Bayesian Inference

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  • Ping Ren ,
  • Zhiqiang Zhao
Ping Ren
Chengdu Ding Yi Education Consulting Co., Ltd, Chengdu 610023, China
Zhiqiang Zhao
Beijing PhD Village Education Technology Co., Ltd; Beijing 100871, China

Published 2025-05-18

Keywords

  • Emotions,
  • Learning Process,
  • Emotional Recognition,
  • Bayesian Inference,
  • Educational Technology

How to Cite

Ren , P., & Zhao, Z. (2025). Recognition and Detection of Student Emotional States through Bayesian Inference. Bulletin of Education and Psychology, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/BEP/article/view/119

Abstract

Emotions play a crucial role in the learning process, affecting students' cognitive abilities, motivation, and overall academic performance. Recognizing and detecting student emotional states have become essential for enhancing educational outcomes. However, existing research in this field faces challenges such as the complexity of emotional signals and the lack of efficient detection methods. To address these gaps, this paper proposes a novel approach utilizing Bayesian inference for the recognition and detection of student emotional states. Our research contributes by developing a robust framework that integrates physiological signals and behavioral data to accurately infer emotional states in real-time within educational settings. This innovative methodology has the potential to revolutionize the field of educational technology and personalize learning experiences based on individual emotional needs.

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References

  1. D. Litman and Katherine Forbes-Riley, "Annotating Student Emotional States in Spoken Tutoring Dialogues," in SIGDIAL Workshop, 2004.
  2. C. Wegner and N. Tönnesmann, "The Effects of Using Living Sea Animals on the Student’s Emotional States," in Frontiers in Education Technology, 2018.
  3. J. C. Taylor et al., "The Effects of Project-Based Learning on Student Behavior and Teacher Burnout in an Emotional/Behavioral Support Classroom," in Journal of Emotional & Behavioral Disorders, 2024.
  4. M. Elbawab and R. Henriques, "Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures," in Education and Information Technologies, 2023.
  5. S. Cotterill et al., "Gender differences in the perceived impact that athlete leaders have on team member emotional states," in Journal of sport science, 2020.
  6. O. Vasylenko et al., "Diagnosing the Negative Psycho-Emotional States among Students," in Revista Romaneasca pentru Educatie Multidimensionala, 2020.
  7. R. Trigueros et al., "Validation of the Scale of Emotional States in the Physical Education Context," in Sustainability, 2019.
  8. M. Kryza-Lacombe et al., "Hedonic and Eudaimonic Motives: Associations with Academic Achievement and Negative Emotional States Among Urban College Students," in Journal of Happiness Studies, 2018.
  9. M. B. Ripski et al., "Pre-Service Teachers: Dispositional Traits, Emotional States, and Quality of Teacher-Student Interactions," in Teacher Education Quarterly, 2011.
  10. C. Lara-Álvarez et al., "Induction of Emotional States in Educational Video Games Through a Fuzzy Control System," in IEEE Transactions on Affective Computing, 2018.
  11. J. Huelsenbeck, F. Ronquist, "MRBAYES: Bayesian inference of phylogenetic trees," Bioinformatics, vol. 17, no. 8, pp. 754-755, 2001.
  12. F. Ronquist, et al., "MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space," Systematic Biology, vol. 61, pp. 539-542, 2012.
  13. F. Ronquist, J. Huelsenbeck, "MrBayes 3: Bayesian phylogenetic inference under mixed models," Bioinformatics, vol. 19, no. 12, pp. 1572-1574, 2003.
  14. X. Deng, Z. Dong, X. Ma, H. Wu, B. Wang, and X. Du, ‘Exploration on mechanics design for scanning tunneling microscope’, in 2009 Symposium on Photonics and Optoelectronics, IEEE, 2009, pp. 1–4.
  15. X. Deng, Z. Dong, X. Ma, H. Wu, and B. Wang, ‘Active gear-based approach mechanism for scanning tunneling microscope’, in 2009 International Conference on Mechatronics and Automation, IEEE, 2009, pp. 1317–1321.
  16. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  17. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  18. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  19. Y. Shu, Z. Zhu, S. Kanchanakungwankul, and D. G. Truhlar, ‘Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods’, J. Phys. Chem. A, vol. 128, no. 31, pp. 6412–6422, Aug. 2024, doi: 10.1021/acs.jpca.4c03137.
  20. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  21. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  22. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  23. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  24. Z. Zhao, P. Ren, and Q. Yang, ‘Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America’, Apr. 17, 2024, arXiv: arXiv:2404.11029. doi: 10.48550/arXiv.2404.11029.
  25. Z. Zhao, P. Ren, and M. Tang, ‘Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China’, Journal of Linguistics and Education Research, vol. 5, no. 2, pp. 15–31, 2022.
  26. M. Tang, P. Ren, and Z. Zhao, ‘Bridging the gap: The role of educational technology in promoting educational equity’, The Educational Review, USA, vol. 8, no. 8, pp. 1077–1086, 2024.
  27. P. Ren, Z. Zhao, and Q. Yang, ‘Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China’, Apr. 17, 2024, arXiv: arXiv:2404.11034. doi: 10.48550/arXiv.2404.11034.
  28. P. Ren and Z. Zhao, ‘Parental Recognition of Double Reduction Policy, Family Economic Status And Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource’, Economics & Management Information, pp. 1–12, 2024.
  29. Z. Zhao, P. Ren, and M. Tang, ‘How Social Media as a Digital Marketing Strategy Influences Chinese Students’ Decision to Study Abroad in the United States: A Model Analysis Approach’, Journal of Linguistics and Education Research, vol. 6, no. 1, pp. 12–23, 2024.
  30. G. Zhang and T. Zhou, ‘Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model’, IAET, pp. 1–13, Sep. 2024, doi: 10.62836/iaet.v3i1.232.
  31. G. Zhang, W. Huang, and T. Zhou, ‘Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation’, Electrical Science & Engineering, vol. 6, no. 1, Art. no. 1, Oct. 2024, doi: 10.30564/ese.v6i1.7532.
  32. T. Zhou, G. Zhang, and Y. Cai, ‘Unsupervised Autoencoders Combined with Multi-Model Machine Learning Fusion for Improving the Applicability of Aircraft Sensor and Engine Performance Prediction’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Feb. 2025, doi: 10.71070/oaml.v5i1.83.
  33. Y. Tang and C. Li, ‘Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises’, Journal of Computational Methods in Engineering Applications, pp. 1–17, 2023.
  34. C. Li and Y. Tang, ‘Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region’, Economics & Management Information, pp. 1–13, 2024.
  35. C. Li and Y. Tang, ‘The Factors of Brand Reputation in Chinese Luxury Fashion Brands’, Journal of Integrated Social Sciences and Humanities, pp. 1–14, 2023.
  36. C. Y. Tang and C. Li, ‘Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises’, OAJRC Social Science, vol. 4, no. 3, pp. 63–77, 2023.
  37. W. Huang, T. Zhou, J. Ma, and X. Chen, ‘An ensemble model based on fusion of multiple machine learning algorithms for remaining useful life prediction of lithium battery in electric vehicles’, Innovations in Applied Engineering and Technology, pp. 1–12, 2025.
  38. W. Huang and J. Ma, ‘Predictive Energy Management Strategy for Hybrid Electric Vehicles Based on Soft Actor-Critic’, Energy & System, vol. 5, no. 1, 2025, Accessed: Jun. 01, 2025.
  39. J. Ma, K. Xu, Y. Qiao, and Z. Zhang, ‘An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms’, Journal of Computational Methods in Engineering Applications, pp. 1–12, 2022.
  40. W. Huang, Y. Cai, and G. Zhang, ‘Battery Degradation Analysis through Sparse Ridge Regression’, Energy & System, vol. 4, no. 1, Art. no. 1, Dec. 2024, doi: 10.71070/es.v4i1.65.
  41. Z. Zhang, ‘RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations’, Optimizations in Applied Machine Learning, vol. 4, no. 1, 2024, Accessed: Jun. 01, 2025.
  42. Z. Zhang, K. Xu, Y. Qiao, and A. Wilson, ‘Sparse Attention Combined with RAG Technology for Financial Data Analysis’, Journal of Computer Science Research, vol. 7, no. 2, Art. no. 2, Mar. 2025, doi: 10.30564/jcsr.v7i2.8933.
  43. P.-M. Lu and Z. Zhang, ‘The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering’, Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025, Accessed: Mar. 12, 2025.
  44. Y. Qiao, K. Xu, Z. Zhang, and A. Wilson, ‘TrAdaBoostR2-based Domain Adaptation for Generalizable Revenue Prediction in Online Advertising Across Various Data Distributions’, Advances in Computer and Communication, vol. 6, no. 2, 2025, Accessed: Jun. 01, 2025.
  45. K. Xu, Y. Gan, and A. Wilson, ‘Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances’, Innovations in Applied Engineering and Technology, pp. 1–12, 2024.
  46. A. Wilson and J. Ma, ‘MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection’, Optimizations in Applied Machine Learning, vol. 5, no. 1, 2025, Accessed: Jun. 01, 2025.