As big data continues to evolve rapidly, engineering education accreditation increasingly needs to integrate big data and related technologies to enhance its continuous improvement mechanisms. Using the computer science and technology major at Chongqing University of Posts and Telecommunications as a case study, this study first elaborates on the current state of continuous improvement mechanisms. The study then identifies key shortcomings associated with integrating continuous improvement with big data, including the absence of specialized big data platforms, insufficient analysis of students' learning behaviors, and delays in feedback and improvement. Based on these findings, the study explores a big data-based path for continuous improvement, which involves establishing dedicated big data platforms for engineering education accreditation, incorporating student learning behavior analysis into the continuous improvement process, and developing predictive feedback and improvement mechanisms. The goal is to provide valuable references and insights for constructing effective continuous improvement mechanisms in engineering education accreditation within the context of big data.

Xiaobo Zhu, Yan Yang, Qin Hang, Jingbo Xie, Xiao Feng. Exploring Pathways for Continuous Improvement in Engineering Education Accreditation in the Era of Big Data. International Journal of New Developments in Education (2024), Vol. 6, Issue 8: 260-266. https://doi.org/10.25236/IJNDE.2024.060840.

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