Transforming Competition Challenges into Pedagogical Assets: A PBL Framework for Vocational Undergraduate Electronics Education
DOI: https://doi.org/10.62517/jhve.202616403
Author(s)
Yifan Qian1, Fenglian Wen1,*, Zhaohui Wu2, Qiang Gu3,
Affiliation(s)
1School of Electronic Engineering, Changzhou University of Information Technology, Changzhou, Jiangsu, China
2School of Digital Economics, Changzhou University of Information Technology, Changzhou, Jiangsu, China
3Changzhou Shucu Advanced Technology Research Co., Ltd., Changzhou, Jiangsu, China
*Corresponding Author
Abstract
The National Undergraduate Electronics Design Contest (NUEDC) is the most influential electronics competition in China, yet its rich engineering challenges are rarely systematically transformed into curriculum-aligned pedagogical resources. This paper proposes the Project-Contest-Lifting (PCL) pipeline, a five-stage framework that converts competition tasks into project-based learning (PBL) modules anchored in vocational undergraduate curricula. The PCL pipeline is governed by four design principles: Curriculum-Contract Mapping (CCM) ensures one-to-one correspondence between course knowledge units and learning modules; Difficulty-Monotone Module Ordering (DMMO) sequences modules by a Bloom-anchored difficulty coefficient α with the constraint α1 ≤ 0.3; Closed-Loop Metric Anchoring (CLMA) ties each assessment gate to verifiable competition specifications; and Auditory Feedback Loop (AFL) substitutes expensive spectrum analyzers with perceptual verification for resource-constrained programs. Three case studies spanning distinct technical domains-laser tracking (machine vision), sound-discriminating music systems (digital signal processing), and contactless control panels (multi-sensor fusion)-demonstrate the framework’s transferability. Pre/post assessment data (N = 32 per cohort) show consistent large-effect gains (Cohen’s d = 1.70–2.46, p < .001) across all competency dimensions. The framework offers a replicable pathway for vocational institutions to leverage competition heritage without reproducing competition-specific training.
Keywords
Project-based Learning; Engineering Education; Curriculum Design; Vocational Education; Bloom’s Taxonomy; Competition-Derived Pedagogy; DSP Education; Embedded Systems
References
[1]Jeffrey E. Froyd, Phillip C. Wankat and Karl A. Smith. Five major shifts in 100 years of engineering education. Proceedings of the IEEE, 2012, 100(Special): 1344-1360.
[2]Hendra Hidayat, Muhammad Anwar, Dani Harmanto, et al. Two decades of project-based learning in engineering education: A 21-year meta-analysis. TEM Journal, 2024, 13(4): 3514-3525.
[3]Brigid J. S. Barron, Daniel L. Schwartz, Nancy J. Vye,et al.Doing with understanding: Lessons from research on problem- and project-based learning. Journal of the Learning Sciences, 1998, 7(3-4): 271-311.
[4]Stephanie Bell. Project-based learning for the 21st century: Skills for the future. The Clearing House, 2010, 83(2): 39-43.
[5]Lisette Wijnia, Gera Noordzij, Lidia R. Arends, et al. The effects of problem-based, project-based, and case-based learning on students' motivation: A meta-analysis. Educational Psychology Review, 2024, 36: 1-38.
[6]Michael Prince. Does active learning work? A review of the research. Journal of Engineering Education, 2004, 93(3): 223-231.
[7]Scott Freeman, Sarah L. Eddy, Miles McDonough, et al. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 2014, 111(23): 8410-8415.
[8]Richard M. Felder and Rebecca Brent. The intellectual development of science and engineering students. Part 1: Models and challenges. Journal of Engineering Education, 2004, 93(4): 269-277.
[9]Charith M. Rathnayaka, Janani Ganapathi, Steven Kickbusch, et al. Preparative pre-laboratory online resources for effectively managing cognitive load of engineering students. European Journal of Engineering Education, 2024, 49(1): 113-138.
[10]Fred Paas, Alexander Renkl and John Sweller. Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 2003, 38(1): 1-4.
[11]David R. Krathwohl. A revision of Bloom's taxonomy: An overview. Theory into Practice, 2002, 41(4): 212-218.
[12]Rafael H. Todaro, Rafael T. Moura and Fernando A. Kurokawa. Competence-based framework for planning, teaching and assessment in engineering education: integrating Bloom's, Fink's, and SOLO taxonomies. Frontiers in Education, 2026, 11: 1787697.
[13]Zurina Zainal Abidin. Learning by brewing tea: student experiences of a Kolb-aligned, constructivist, learning-oriented assessment in chemical engineering. Education for Chemical Engineers, 2025, 54: 1-11.
[14]Daniël Lakens. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 2013, 4: 1-12.
[15]Edson C. Oliveira, Bruno S. Masiero and Fabiano Fruett. Open-source and low-cost embedded system for frequency domain acoustic monitoring//2025 9th International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT). Manaus, Brazil: IEEE, 2025: 1-6.
[16]James W. Cooley and John W. Tukey. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 1965, 19(90): 297-301.
[17]Fredric J. Harris. On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 1978, 66(1): 51-83.
[18]Edwin A. Locke and Gary P. Latham. Building a practically useful theory of goal setting and task motivation. American Psychologist, 2002, 57(9): 705-717.