Downloads

https://doi.org/10.62836/iaet.v4i1.562
Liang, X., Lei, T., Zhang, L., & Zhang, X. (2025). Research on Computational Modeling and Simulation-Based Instructional Strategies in Higher Vocational Mathematics Education: An Empirical Analysis Using Matrix Operations. Innovations in Applied Engineering and Technology, 34–48. https://doi.org/10.62836/iaet.v4i1.562
Volume 4, Issue 1, 2025

Research on Computational Modeling and Simulation-Based Instructional Strategies in Higher Vocational Mathematics Education: An Empirical Analysis Using Matrix Operations

To address the disconnection between theory and practice and the inefficiency of knowledge transfer in higher vocational mathematics education, this study proposes an instructional model centered on computational thinking, structured around the framework of “Modeling Concept-Professional Integration-Complexity Simplification-Application Innovation” (MCPAI). Using “matrix operations” as an empirical case, we explore a deep integration pathway between mathematics education and professional practice. Leveraging online-offline blended teaching methods, a three-phase instructional framework (“pre-class preparation, in-class task-driven learning, post-class extension”) is designed. This framework incorporates discipline-specific scenarios such as digital image processing and agricultural economic modeling, guiding students to utilize MATLAB tools for modeling, simulation, and optimization, thereby enabling effective translation of mathematical tools into real-world problem-solving. Teaching practices demonstrate that this model significantly enhances students’ computational thinking (average improvement: 32.7%), teamwork (improvement: 28.4%), and innovation capabilities (improvement: 25.9%). The effectiveness is validated through pre- and post-test score comparisons (experimental group: N = 45 vs. control group: N = 43, p < 0.01). Furthermore, the organic integration of value-oriented elements strengthens the value-guiding function of mathematics education. This research provides a paradigm with both theoretical innovation and practical feasibility for the reform of higher vocational mathematics education.

higher vocational education computational thinking modeling and simulation disciplinary integration Computational Thinking

References

  1. Yadav U, Shrawankar U. Artificial Intelligence Across Industries: A Comprehensive Review with a Focus on Education. In AI Applications and Strategies in Teacher Education; IGI Global Scientific Publishing: Hershey, PA, USA, 2025; pp. 275–320.
  2. Purwanto M B, Hartono R, Wahyuni S. Essential skills Challenges for the 21st Century Graduates: Creating a Generation of High-Level Competence in the Industrial Revolution 4.0 Era. Asian Journal of Applied Education (AJAE) 2023; 2(3): 279–292.
  3. Hidayat DN, Restiandi A, Sukresna IM. Designing an Optimal Education and Training Model for Relationship Managers in the Digital Era. Research Horizon 2025; 5(1): 79–94.
  4. Jaskari MME. Bridging the Skills Gap in Higher Marketing Education: Enhancing Graduate Competencies Through Education-Related Industry-Academia Collaboration. In Developing Managerial Skills for Global Business Success; IGI Global Scientific Publishing: Hershey, PA, USA, 2025; pp. 151–174.
  5. Asmara A, Ramadianti W, Jumri R, et al. Improving the Quality of Mathematics Education through Innovative Approaches: A Literature Review. International Journal of Teaching and Learning 2024; 2(1): 282–296.
  6. Wells S, Warelow P, Jackson K. Problem Based Learning (PBL): A Conundrum. Contemporary Nurse 2009; 33(2): 191–201.
  7. Fisher R, Tran Q, Verezub E. Teaching English as a Foreign Language in Higher Education using flipped learning/flipped classrooms: a literature review. Innovation in Language Learning and Teaching, 2024: 18: 332–351.
  8. Alahmari M, Jdaitawi M T, Rasheed A, et al. Trends and Gaps in Empirical Research on Gamification in Science Education: A Systematic Review of the Literature. Contemporary Educational Technology 2023; 15(3): ep431.
  9. Santana-Vega LE, Feliciano-García L, Suárez-Perdomo A. Inquiry-Based Learning in the University Context: A Systematic Review. Revista Espanola De Pedagogia 2023; 78(277): 9.
  10. Zhong Q, Peng G, Pan X. Exploration and Practice of Cultivation Mode of Cooperative Innovation Ability Based on Integration of Higher Mathematics and Specialties of Higher Vocational Colleges. Popular Science & Technology 2019; 21(4): 98–101.
  11. Korkmaz Ö, Çakir R, Özden MY. A Validity and Reliability Study of the Computational Thinking Scales (CTS). Computers in Human Behavior 2017; 72: 558–569.
  12. Hmelo-Silver CE. Problem-Based Learning: What and How do Students Learn? Educational Psychology Review 2004; 16: 235–266.

Supporting Agencies

  1. Funding: Guangdong Province Undergraduate Teaching Quality and Teaching Reform Construction Project (Higher Education Teaching Reform Project: 2024-30-884). Quality Engineering Project of Shenzhen Polytechnic University (General Project: 1005-0452). Smart Course Project of Guangdong University of Petrochemical Technology: 2024-59. Projects of Talents Recruitment of GDUPT:2020rc039.