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

Prediction of Student Disciplinary Behavior through Efficient Ridge Regression

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

Published 2025-03-19

Keywords

  • Disciplinary Behavior,
  • Prediction Models,
  • Ridge Regression,
  • Predictive Analytics,
  • Intervention Strategies

How to Cite

Zhao, Z., & Ren, P. (2025). Prediction of Student Disciplinary Behavior through Efficient Ridge Regression. Bulletin of Education and Psychology, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/BEP/article/view/117

Abstract

In light of the importance of predicting student disciplinary behavior in educational settings, this paper addresses the current challenges and limitations in existing research methodologies. The ability to accurately anticipate disciplinary issues is crucial for maintaining a safe and conducive learning environment. However, traditional prediction models often lack efficiency and effectiveness in this context. To overcome these limitations, this paper proposes a novel approach utilizing efficient Ridge Regression for the prediction of student disciplinary behavior. By integrating this innovative technique with relevant behavioral data, our study aims to provide a more precise and reliable predictive model for identifying at-risk students and implementing targeted intervention strategies. This research contributes to the advancement of predictive analytics in education and underscores the significance of proactive measures in managing disciplinary challenges.

PDF

References

  1. K. McCormack et al., "Education Under Extremes: Temperature, Student Absenteeism, and Disciplinary Infractions," 2023.
  2. [1] M. R. Jannah, "The Role of Educators in Counseling Guidance Program with Student Disciplinary Behavior," Journal of education studies, 2024.
  3. M. R. Jannah, "The Role of Educators in the Counseling Guidance Program with Student Disciplinary Behavior," Journal of education studies, 2025.
  4. D. Muslida et al., "Disciplinary Behavior and Exemplary Teacher as Influencing Factors," 2020.
  5. H. Erwin et al., "The Effect of Doubling the Amount of Recess on Elementary Student Disciplinary Referrals and Achievement Over Time," Journal of Research in Childhood Education, 2019.
  6. L. Gennetian et al., "Supplemental Nutrition Assistance Program (SNAP) Benefit Cycles and Student Disciplinary Infractions," Social Service Review, 2016.
  7. S. W. Manalo, "High School Students Disciplinary Behavior in a Catholic Private School," 2016.
  8. C. E. Kunesh and A. Noltemeyer, "Understanding Disciplinary Disproportionality: Stereotypes Shape Pre-Service Teachers’ Beliefs About Black Boys’ Behavior," Urban Education, 2019.
  9. M. J. Magier et al., "Are School Substance Use Policy Violation Disciplinary Consequences Associated with Student Engagement in Cannabis?," International Journal of Environmental Research and Public Health, 2020.
  10. N. A. Gage et al., "The Effect of School-Wide Positive Behavior Interventions and Supports on Disciplinary Exclusions: A Conceptual Replication," Behavioral Disorders, 2019.
  11. A. E. Hoerl and R. Kennard, "Ridge Regression: Biased Estimation for Nonorthogonal Problems," Technometrics, vol. 42, pp. 80-86, 2000.
  12. Y. Li, H. Zhang, and Q. Lin, "On the Saturation Effect of Kernel Ridge Regression," in International Conference on Learning Representations, 2024.
  13. Z. Xu et al., "Kernel Ridge Regression-Based Graph Dataset Distillation," in Knowledge Discovery and Data Mining, 2023.
  14. H. Zhang et al., "On the Optimality of Misspecified Kernel Ridge Regression," in International Conference on Machine Learning, 2023.
  15. T. Nguyen et al., "Dataset Meta-Learning from Kernel Ridge-Regression," in International Conference on Learning Representations, 2020.
  16. X. Deng, S. Oda, and Y. Kawano, ‘Frequency selective, high transmission spiral terahertz plasmonic antennas’, Journal of Modeling and Simulation of Antennas and Propagation, vol. 2, pp. 1–6, 2016.
  17. X. Deng and Y. Kawano, ‘Terahertz plasmonics and nano-carbon electronics for nano-micro sensing and imaging’, International Journal of Automation Technology, vol. 12, no. 1, pp. 87–96, 2018.
  18. X. Deng, S. Oda, and Y. Kawano, ‘Split-joint bull’s eye structure with aperture optimization for multi-frequency terahertz plasmonic antennas’, in 2016 41st International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz), IEEE, 2016, pp. 1–2.
  19. 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.
  20. 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.
  21. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  22. 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.
  23. 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.
  24. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  25. 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.
  26. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.