Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data

Yingcan Wang; Jianxin Zhao

doi:doi:10.11648/j.si.20251302.12

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Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data

Yingcan Wang, Jianxin Zhao^*

Published in Science Innovation (Volume 13, Issue 2)

Received: 21 March 2025 Accepted: 19 April 2025 Published: 29 April 2025

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Abstract

To address the inconsistency between student evaluation satisfaction data and supervisory evaluation conclusions in university teaching assessments, this study introduces structured data such as class size and course intensity to establish a "normalization-entropy weight-TOPSIS" evaluation methodology. Using a semester's teaching evaluation dataset from the Naval Submarine Academy as an empirical testbed, we implemented a comparative analysis of four distinct data preprocessing strategies: 1) RobustScaler enhanced with Sigmoid function transformation, 2) conventional RobustScaler, 3) Z-score standardization, and 4) Min-Max normalization. The experimental design rigorously evaluated each method's capacity to harmonize student feedback with expert evaluations through correlation analysis and distribution pattern verification.The empirical validation demonstrates that when applying the proposed evaluation framework to datasets processed by the Sigmoid-enhanced RobustScaler method, the resulting assessment scores achieved the highest correlation coefficient with supervisory ratings. Compared with traditional methods relying solely on student satisfaction rates, when applied within the proposed evaluation framework, this approach improved the correlation coefficient between the resulting assessment scores and supervisory ratings by 19.0%, which means generated assessment scores demonstrating superior alignment with supervisory ratings. The novel evaluation approach demonstrated superior performance in this practical application scenario, effectively resolving the contradiction inherent in single-source evaluation systems that exclusively utilize student feedback.

Published in	Science Innovation (Volume 13, Issue 2)
DOI	10.11648/j.si.20251302.12
Page(s)	16-20
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Multi-source Teaching Evaluation Data, Teaching Evaluation Indicator System, Entropy Weight Method, TOPSIS Method

References

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[10]	孙娓娓, 孙怀波, 李俊林. 基于AHP-TOPSIS模型的“数学建模”课程思政的成效分析 [J]. 湖北理工学院学报, 2025, 41(02): 73-77.
[11]	李木洲, 曾思鑫. 新高考科目改革的区域评价差异与推进策略——基于熵权TOPSIS模型的分析 [J]. 中国高教研究, 2025, (03): 59-67. https://doi.org/10.16298/j.cnki.1004-3667.2025.03.08
[12]	李洪英, 卢凯莉, 王亚楠. 基于概率语言术语集改进TOPSIS法的社区应急治理韧性评价研究 [J/OL]. 数学的实践与认识, 1-12 [2025-04-15]. https://doi.org/10.20266/j.math.24-0713
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APA Style

Wang, Y., Zhao, J. (2025). Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data. Science Innovation, 13(2), 16-20. https://doi.org/10.11648/j.si.20251302.12

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ACS Style

Wang, Y.; Zhao, J. Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data. Sci. Innov. 2025, 13(2), 16-20. doi: 10.11648/j.si.20251302.12

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AMA Style

Wang Y, Zhao J. Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data. Sci Innov. 2025;13(2):16-20. doi: 10.11648/j.si.20251302.12

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@article{10.11648/j.si.20251302.12,
  author = {Yingcan Wang and Jianxin Zhao},
  title = {Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data
},
  journal = {Science Innovation},
  volume = {13},
  number = {2},
  pages = {16-20},
  doi = {10.11648/j.si.20251302.12},
  url = {https://doi.org/10.11648/j.si.20251302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20251302.12},
  abstract = {To address the inconsistency between student evaluation satisfaction data and supervisory evaluation conclusions in university teaching assessments, this study introduces structured data such as class size and course intensity to establish a "normalization-entropy weight-TOPSIS" evaluation methodology. Using a semester's teaching evaluation dataset from the Naval Submarine Academy as an empirical testbed, we implemented a comparative analysis of four distinct data preprocessing strategies: 1) RobustScaler enhanced with Sigmoid function transformation, 2) conventional RobustScaler, 3) Z-score standardization, and 4) Min-Max normalization. The experimental design rigorously evaluated each method's capacity to harmonize student feedback with expert evaluations through correlation analysis and distribution pattern verification.The empirical validation demonstrates that when applying the proposed evaluation framework to datasets processed by the Sigmoid-enhanced RobustScaler method, the resulting assessment scores achieved the highest correlation coefficient with supervisory ratings. Compared with traditional methods relying solely on student satisfaction rates, when applied within the proposed evaluation framework, this approach improved the correlation coefficient between the resulting assessment scores and supervisory ratings by 19.0%, which means generated assessment scores demonstrating superior alignment with supervisory ratings. The novel evaluation approach demonstrated superior performance in this practical application scenario, effectively resolving the contradiction inherent in single-source evaluation systems that exclusively utilize student feedback.
},
 year = {2025}
}

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TY  - JOUR
T1  - Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data

AU  - Yingcan Wang
AU  - Jianxin Zhao
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.si.20251302.12
DO  - 10.11648/j.si.20251302.12
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 16
EP  - 20
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20251302.12
AB  - To address the inconsistency between student evaluation satisfaction data and supervisory evaluation conclusions in university teaching assessments, this study introduces structured data such as class size and course intensity to establish a "normalization-entropy weight-TOPSIS" evaluation methodology. Using a semester's teaching evaluation dataset from the Naval Submarine Academy as an empirical testbed, we implemented a comparative analysis of four distinct data preprocessing strategies: 1) RobustScaler enhanced with Sigmoid function transformation, 2) conventional RobustScaler, 3) Z-score standardization, and 4) Min-Max normalization. The experimental design rigorously evaluated each method's capacity to harmonize student feedback with expert evaluations through correlation analysis and distribution pattern verification.The empirical validation demonstrates that when applying the proposed evaluation framework to datasets processed by the Sigmoid-enhanced RobustScaler method, the resulting assessment scores achieved the highest correlation coefficient with supervisory ratings. Compared with traditional methods relying solely on student satisfaction rates, when applied within the proposed evaluation framework, this approach improved the correlation coefficient between the resulting assessment scores and supervisory ratings by 19.0%, which means generated assessment scores demonstrating superior alignment with supervisory ratings. The novel evaluation approach demonstrated superior performance in this practical application scenario, effectively resolving the contradiction inherent in single-source evaluation systems that exclusively utilize student feedback.

VL  - 13
IS  - 2
ER  -

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Author Information

Yingcan Wang

Naval Submarine Academy, Qingdao, China

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Jianxin Zhao

Naval Submarine Academy, Qingdao, China

Contact Email

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Plain Text BibTeX RIS

APA Style

Wang, Y., Zhao, J. (2025). Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data. Science Innovation, 13(2), 16-20. https://doi.org/10.11648/j.si.20251302.12

Copy | Download

ACS Style

Wang, Y.; Zhao, J. Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data. Sci. Innov. 2025, 13(2), 16-20. doi: 10.11648/j.si.20251302.12

Copy | Download

AMA Style

Wang Y, Zhao J. Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data. Sci Innov. 2025;13(2):16-20. doi: 10.11648/j.si.20251302.12

Copy | Download

@article{10.11648/j.si.20251302.12,
  author = {Yingcan Wang and Jianxin Zhao},
  title = {Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data
},
  journal = {Science Innovation},
  volume = {13},
  number = {2},
  pages = {16-20},
  doi = {10.11648/j.si.20251302.12},
  url = {https://doi.org/10.11648/j.si.20251302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20251302.12},
  abstract = {To address the inconsistency between student evaluation satisfaction data and supervisory evaluation conclusions in university teaching assessments, this study introduces structured data such as class size and course intensity to establish a "normalization-entropy weight-TOPSIS" evaluation methodology. Using a semester's teaching evaluation dataset from the Naval Submarine Academy as an empirical testbed, we implemented a comparative analysis of four distinct data preprocessing strategies: 1) RobustScaler enhanced with Sigmoid function transformation, 2) conventional RobustScaler, 3) Z-score standardization, and 4) Min-Max normalization. The experimental design rigorously evaluated each method's capacity to harmonize student feedback with expert evaluations through correlation analysis and distribution pattern verification.The empirical validation demonstrates that when applying the proposed evaluation framework to datasets processed by the Sigmoid-enhanced RobustScaler method, the resulting assessment scores achieved the highest correlation coefficient with supervisory ratings. Compared with traditional methods relying solely on student satisfaction rates, when applied within the proposed evaluation framework, this approach improved the correlation coefficient between the resulting assessment scores and supervisory ratings by 19.0%, which means generated assessment scores demonstrating superior alignment with supervisory ratings. The novel evaluation approach demonstrated superior performance in this practical application scenario, effectively resolving the contradiction inherent in single-source evaluation systems that exclusively utilize student feedback.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Mathematical Modeling and Empirical Analysis of Multi-source Teaching Evaluation Data

AU  - Yingcan Wang
AU  - Jianxin Zhao
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.si.20251302.12
DO  - 10.11648/j.si.20251302.12
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 16
EP  - 20
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20251302.12
AB  - To address the inconsistency between student evaluation satisfaction data and supervisory evaluation conclusions in university teaching assessments, this study introduces structured data such as class size and course intensity to establish a "normalization-entropy weight-TOPSIS" evaluation methodology. Using a semester's teaching evaluation dataset from the Naval Submarine Academy as an empirical testbed, we implemented a comparative analysis of four distinct data preprocessing strategies: 1) RobustScaler enhanced with Sigmoid function transformation, 2) conventional RobustScaler, 3) Z-score standardization, and 4) Min-Max normalization. The experimental design rigorously evaluated each method's capacity to harmonize student feedback with expert evaluations through correlation analysis and distribution pattern verification.The empirical validation demonstrates that when applying the proposed evaluation framework to datasets processed by the Sigmoid-enhanced RobustScaler method, the resulting assessment scores achieved the highest correlation coefficient with supervisory ratings. Compared with traditional methods relying solely on student satisfaction rates, when applied within the proposed evaluation framework, this approach improved the correlation coefficient between the resulting assessment scores and supervisory ratings by 19.0%, which means generated assessment scores demonstrating superior alignment with supervisory ratings. The novel evaluation approach demonstrated superior performance in this practical application scenario, effectively resolving the contradiction inherent in single-source evaluation systems that exclusively utilize student feedback.

VL  - 13
IS  - 2
ER  -

Copy | Download