AI Insight
This study introduces a multi-stage assessment framework for Problem-Based Learning (PBL) in practical courses, combining Z-number theory with the MABAC decision-making method to handle uncertainty and subjectivity in expert evaluations. The framework operates across three phases — Pass check, Score determination, and Grading — and uses a hybrid Entropy-CRITIC weighting scheme to objectively determine the importance of evaluation criteria. Applied to a real case study of 24 students assessed by peers, instructors, and industry experts, the framework demonstrated robustness and reliability-awareness in structured educational assessment.
Why it matters
As practical education increasingly demands personalized competency evaluation beyond standardized testing, this framework offers a more transparent and structured approach to multi-stakeholder assessment. It could benefit institutions seeking to reduce evaluator bias and improve the fairness of PBL-based grading, though broader validation across larger cohorts and diverse educational contexts remains necessary.
Understand the Science
by Limin Yu, Zhe Chen, Zeyu Qin
Confronting the growing disparity between standardized evaluation systems and personalized competency development in practical education, this study proposes a novel data-driven framework integrating Multi-Criteria Group Decision Making (MCGDM) to enhance curriculum assessment within Problem-Based Learning (PBL) environments. Specifically, the framework incorporates Z-number theory to effectively capture the uncertainty and reliability inherent in expert evaluations, addressing the challenges posed by subjective and imprecise human judgments. The well-established Multi-Attributive Border Approximation Area Comparison (MABAC) method is extended through the integration of Z-number modeling to enhance robustness in ranking and decision-making processes. A multi-stage assessment process is designed, encompassing a Pass check phase, a Score determination phase, and a Grading phase, aligned with the pedagogical principles of PBL. Furthermore, a hybrid Entropy-Criteria Importance Through Intercriteria Correlation (CRITIC) weighting scheme under Z-number representation is introduced to objectively determine the importance of evaluation criteria, considering both information dispersion and inter-criteria correlation. The proposed method is applied to a real-life case study involving 24 students, evaluated by multiple stakeholder groups, including peer teams, instructors, and industry experts. Sensitivity and comparative analyses suggest that the proposed framework provides a robust and reliability-aware assessment procedure within the studied course context. The findings indicate its potential to support a more transparent and structured evaluation process, although broader generalizability requires further validation across cohorts, courses, and institutions.