Indeterminate and Hierarchical Modeling of Instructional Effectiveness in University Chinese Language and Literature: A Python-Based Approach

Authors

  • Dalun Zhu Nanchang Institute of Science and Technology, Nanchang, 330108, China
  • Yongfo Xiong Nanchang Institute of Science and Technology, Nanchang, 330108, China
  • Qun Xia Nanchang Institute of Science and Technology, Nanchang, 330108, China

Keywords:

Soft set extensions; teaching quality; university Chinese Language and Literature courses; educational assessments

Abstract

This paper applies four advanced soft set extensions  HyperSoft Set, 
IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set  to evaluate teaching quality in 
university Chinese Language and Literature courses. Each extension is mathematically 
defined and illustrated through two case studies per model, using realistic data scenarios. 
A comprehensive practical case study applies all models to a unified dataset, solved step
by-step with numerical data. Python implementations and a results table consolidate the 
findings, highlighting the models' ability to handle multi-attribute, indeterminate, and 
hierarchical data in educational assessments. 

 

DOI: 10.5281/zenodo.15467996

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Published

2025-08-01

Issue

Vol. 86 (2025): Neutrosophic Sets and Systems

Section

Article

License

Copyright (c) 2025 Neutrosophic Sets and Systems

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

 Creative Commons Attribution (CC BY) license.

How to Cite

Dalun Zhu, Yongfo Xiong, & Qun Xia. (2025). Indeterminate and Hierarchical Modeling of Instructional Effectiveness in University Chinese Language and Literature: A Python-Based Approach . Neutrosophic Sets and Systems, 86, 266-281. https://fs.unm.edu/nss8/index.php/111/article/view/6386