Expert Weight Assignment for Student Assessment in a Problem-Based Learning Course

A study by Limin Yu proposes a data-driven framework for curriculum assessment in Problem-Based Learning environments. The dataset, last updated in May 2026, contains evaluation data from a real-life case study involving 24 students assessed by peer teams, instructors, and industry experts. The framework integrates Z-number theory and a hybrid Entropy-CRITIC weighting scheme to handle uncertainty in expert judgments.

Use Cases

• Benchmarking student performance based on multi-stakeholder evaluations described in the study
• Testing the robustness of the proposed Z-number MABAC ranking method for educational assessment
• Analyzing the impact of a hybrid Entropy-CRITIC weighting scheme on criterion importance
• Conducting sensitivity analysis on a multi-stage assessment process (Pass check, Score determination, Grading)

Strengths

• Data originates from a real-life case study with 24 students, providing a concrete application context.
• The framework incorporates evaluations from multiple stakeholder groups (peer teams, instructors, industry experts).
• The methodology is explicitly described, integrating Z-number theory and the MABAC method for handling uncertainty.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope and sample size.
• Row count and column-level documentation are unknown, requiring manual inspection after download.
• Generalizability is limited to the studied course context, as noted by the author.

Provenance

Source

figshare, author Limin Yu

Collection Method

Data collected from a multi-stage assessment process within a Problem-Based Learning course, involving expert evaluations.

Freshness

Last updated 2026-05-18 17:41:02