Enhancing Transparency and Fairness in Chinese Student Design Competitions: A Five-Dimensional Evaluation Framework for Sustainable Design Education

  1. Kai Huo
  2. Yunjia Yang
  3. Qiang Zhao

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Abstract

Student design competitions connect higher education and professional practice, yet their educational value depends on whether evaluation is transparent, interpretable, and fair. In the context of sustainable design education, opaque and heterogeneous judging practices may weaken the link between competition participation, capability development, and long-term talent cultivation. To address this problem, this study develops a five-dimensional evaluation framework based on design evaluation theory and data-driven modelling. The framework is composed of innovation and creativity, artistic aesthetics, applied technology, work normativity, and practical promotion. A total of 202 award-winning entries from six national competitions in China were re-evaluated by six expert judges under a double-blind procedure, producing 1,212 valid scoring records. Regression modelling was used to identify event-specific weighting patterns and to examine how dimension scores relate to overall evaluation outcomes. The results show clear variation across competitions and experts, indicating that evaluation is not simply the direct application of stated criteria, but a practice shaped by competition-specific priorities and judgement patterns. The framework provides an evidence-based basis for improving transparency and fairness in competition governance and for supporting more sustainable design education through clearer and more actionable evaluation.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22161441.

    Does the introduction explain the objective of the research presented in the preprint? Yes
    Are the methods well-suited for this research? Somewhat appropriate
    Are the conclusions supported by the data? Somewhat supported
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly
    Is the preprint likely to advance academic knowledge? Moderately likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.

  2. Version published to 10.20944/preprints202605.0058.v1