Reducing Subjectivity in Rubric-Based Assessment Through an Adaptive Weighted Penalty Model: A Computational Approach to Fair and Consistent Evaluation

Rubric-based assessment is widely used in higher education to evaluate student performance against predefined criteria. However, conventional rubric scoring remains susceptible to assessor subjectivity, inconsistent interpretation of performance levels, and variations in scoring decisions. This paper presents an Adaptive Weighted Penalty Model (AWPM) implemented through a computational assessment system to improve fairness, consistency, and transparency in rubric-based evaluation. The proposed approach converts qualitative assessor judgements into structured numerical scores by applying adaptive penalty values according to the degree of deviation from expected performance. The system integrates rubric configuration, assisted score entry, automated computation, and structured reporting within a unified assessment workflow. By maintaining academic judgement while standardising the computational treatment of assessment decisions, the approach has the potential to reduce scoring variability and improve the traceability of assessment outcomes. The proposed model is applicable to courses involving rubric-based evaluation, including projects, presentations, practical work, and other performance-based assessments. Its spreadsheet-based implementation also provides a practical and accessible basis for adoption across different educational settings without requiring specialised software infrastructure. The study contributes a structured computational approach to addressing persistent concerns regarding fairness and consistency in rubric-based assessment.

Rubric-based Assessment, Adaptive Weighted Penalty, Assessment Fairness, Assessment Consistency, Computational Assessment

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