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<p>The burgeoning field of wisdom education has led to a disparity in the quality of educational institutions, thereby necessitating enhancements to the existing school quality evaluation systems. This study refines the traditional fuzzy C-mean clustering algorithm by incorporating a Gaussian mixture distribution and information entropy regularization. It then employs this enhanced algorithm to construct a quality evaluation system for smart education schools. The improved fuzzy C-mean clustering algorithm demonstrated in this paper significantly outperforms the standard C-means used at SCU and the K-means algorithm in terms of Mean Absolute Error (MAE) and Total Hit Rate (THR). Specifically, the optimized algorithm decreased the MAE to 0.572 from 0.619, a reduction of 16.01% compared to the K-means algorithm. It increased the THR to 0.158 from 0.147, marking an improvement of 17.04% over the Kmeans algorithm. Furthermore, the refined clustering algorithm achieved an impressive accuracy of 95.71%. Analysis through this clustering approach reveals that the developed quality evaluation system accurately reflects the operational level of smart education within schools. The establishment of such a system is anticipated to significantly guide the future progression of smart education and aid in the foundational development of smart education schools.</p>
</abstract>

The burgeoning field of wisdom education has led to a disparity in the quality of educational institutions, thereby necessitating enhancements to the existing school quality evaluation systems. This study refines the traditional fuzzy C-mean clustering algorithm by incorporating a Gaussian mixture distribution and information entropy regularization. It then employs this enhanced algorithm to construct a quality evaluation system for smart education schools. The improved fuzzy C-mean clustering algorithm demonstrated in this paper significantly outperforms the standard C-means used at SCU and the K-means algorithm in terms of Mean Absolute Error (MAE) and Total Hit Rate (THR). Specifically, the optimized algorithm decreased the MAE to 0.572 from 0.619, a reduction of 16.01% compared to the K-means algorithm. It increased the THR to 0.158 from 0.147, marking an improvement of 17.04% over the Kmeans algorithm. Furthermore, the refined clustering algorithm achieved an impressive accuracy of 95.71%. Analysis through this clustering approach reveals that the developed quality evaluation system accurately reflects the operational level of smart education within schools. The establishment of such a system is anticipated to significantly guide the future progression of smart education and aid in the foundational development of smart education schools.

Research on Quality Evaluation of Smart Education Schools Based on Fuzzy Clustering Algorithm and Strategies for Future Development

Ningbo University of Finance and Economics

Applied Mathematics and Nonlinear Sciences

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

The burgeoning field of wisdom education has led to a disparity in the quality of educational institutions, thereby necessitating enhancements to the existing...

Research on Quality Evaluation of Smart Education Schools Based on Fuzzy Clustering Algorithm and Strategies for Future Development

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 05 mars 2024

Accepté: 01 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1602

Mots clés
Smart education, Quality evaluation, Fuzzy clustering, Gaussian mixture distribution, Information entropy regularization

© 2024 Yingying Lou, published by Sciendo

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

Research on Quality Evaluation of Smart Education Schools Based on Fuzzy Clustering Algorithm and Strategies for Future Development

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 05 mars 2024

Accepté: 01 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1602

Mots clésSmart education, Quality evaluation, Fuzzy clustering, Gaussian mixture distribution, Information entropy regularization

© 2024 Yingying Lou, published by Sciendo

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

Mots clés
Smart education, Quality evaluation, Fuzzy clustering, Gaussian mixture distribution, Information entropy regularization