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A novel Z-number based multi-stage assessment framework for problem-based learning in practical courses.

Limin Yu1, Zhe Chen1, Zeyu Qin1

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Summary
This summary is machine-generated.

This study introduces a Z-number enhanced Multi-Criteria Group Decision Making (MCGDM) framework for robust curriculum assessment in Problem-Based Learning (PBL), improving evaluation transparency and reliability.

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Area of Science:

  • Educational Technology
  • Decision Sciences
  • Artificial Intelligence in Education

Background:

  • Standardized evaluations often conflict with personalized competency development in practical education.
  • Problem-Based Learning (PBL) environments require nuanced assessment methods.
  • Subjective and imprecise expert judgments pose challenges in educational evaluation.

Purpose of the Study:

  • To propose a novel data-driven framework for enhancing curriculum assessment in PBL.
  • To integrate Z-number theory and Multi-Criteria Group Decision Making (MCGDM) for uncertainty handling.
  • To improve the robustness and reliability of educational assessment processes.

Main Methods:

  • Developed a framework integrating Z-number theory with the Multi-Attributive Border Approximation Area Comparison (MABAC) method.
  • Implemented a multi-stage assessment process: Pass check, Score determination, and Grading phases.
  • Introduced a hybrid Entropy-CRITIC (Criteria Importance Through Intercriteria Correlation) weighting scheme using Z-numbers.

Main Results:

  • Applied the framework to a case study with 24 students evaluated by peers, instructors, and industry experts.
  • Sensitivity and comparative analyses confirmed the framework's robustness and reliability.
  • The proposed method demonstrated a more transparent and structured evaluation procedure.

Conclusions:

  • The Z-number enhanced MCGDM framework offers a reliable approach to PBL curriculum assessment.
  • The method effectively addresses uncertainty and imprecision in expert evaluations.
  • Further validation is needed for broader generalizability across different educational settings.