Grading Results Based on Z-MABAC Ranking for 24 Students

24 student evaluation records from a Problem-Based Learning course, assessed by peer teams, instructors, and industry experts. The dataset, created by Limin Yu and last updated in May 2026, applies a Z-number-based Multi-Criteria Group Decision Making framework to model uncertainty in grading. It is stored in an XLS file sized 5.5 KB.

Use Cases

• Benchmarking novel grading frameworks based on the described Z-MABAC ranking method.
• Analyzing multi-stakeholder evaluation patterns based on peer, instructor, and expert assessments.
• Studying the impact of hybrid Entropy-CRITIC weighting on criterion importance in educational contexts.
• Validating structured assessment processes for Problem-Based Learning environments.

Strengths

• Applies a documented, novel methodological framework integrating Z-number theory and MABAC.
• Includes evaluations from multiple stakeholder groups (peer teams, instructors, industry experts).
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size indicates a very limited scope, likely containing only summary or processed results.

Provenance

Source

figshare

Collection Method

Results from a real-life case study applying a proposed multi-criteria assessment framework.

Freshness

Last updated 2026-05-18 17:41:05; freshness should be verified.