We examine here how refined competition-based learning approaches enhance the practical competence of Big Data undergraduates in local application-oriented universities. To address persistent challenges such as fragmented interdisciplinary skill acquisition, insufficient experiential learning, and limited exposure to real industry contexts, the study draws on teaching practice records, instructor reflections, expert consultations, and documentation from multiple iterations of the CP Group Market Survey and Analysis Competition. Using factor analysis, eleven instructional strategies are consolidated into three essential dimensions: Curriculum and competition integration, authentic industry practice orientation, and collaborative support mechanisms for talent cultivation. The findings show that competition-based learning significantly strengthens students’ applied data analysis abilities, promotes interdisciplinary problem-solving, and improves the alignment between academic training and industry expectations. This offers a framework for implementing competition-driven pedagogical strategies and provides practical guidance for enhancing the development of practice-oriented Big Data professionals in local higher education institutions.

Chengqiang Wang, Jianmin Gao. Enhancing the Practical Competence of Undergraduates in Big Data Majors at Local Application-Oriented Colleges and Universities through Competition-Based Learning: A Case Study of the CP Group Market Survey and Analysis Competition. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 202-211. https://doi.org/10.25236/FER.2026.090528.

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