<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the improvement of information technology, all industries have developed rapidly. However, the education industry is lagging behind in the process of informationization. Most still follow the educational paradigm of the past, where teachers rely on personal experience to make judgments and teaching decisions for students, just like a blind man feeling like an elephant. In this paper, we design a teaching quality monitoring and evaluation strategy according to the K-modes algorithm in big data technology, which is a dynamic data collection and intelligent analysis based on the learning process and learning effectiveness, ensuring data authenticity and also realizing multi-dimensional data collection and multi-angle evaluation and analysis, and finally comparing each index of three different algorithms. It is verified that the minimum error sum of squares of the optimized K-modes algorithm is 711, and the correct rate is 0.97, while the values of this metric for the other two algorithms are 1587 and 986, and the correct rates are 0.91 and 0.92, respectively. Therefore, the designed mechanism is a system with a superior evaluation effect and a feasible prediction model for faculty development in local vocational colleges.</p>
</abstract>

With the improvement of information technology, all industries have developed rapidly. However, the education industry is lagging behind in the process of informationization. Most still follow the educational paradigm of the past, where teachers rely on personal experience to make judgments and teaching decisions for students, just like a blind man feeling like an elephant. In this paper, we design a teaching quality monitoring and evaluation strategy according to the K-modes algorithm in big data technology, which is a dynamic data collection and intelligent analysis based on the learning process and learning effectiveness, ensuring data authenticity and also realizing multi-dimensional data collection and multi-angle evaluation and analysis, and finally comparing each index of three different algorithms. It is verified that the minimum error sum of squares of the optimized K-modes algorithm is 711, and the correct rate is 0.97, while the values of this metric for the other two algorithms are 1587 and 986, and the correct rates are 0.91 and 0.92, respectively. Therefore, the designed mechanism is a system with a superior evaluation effect and a feasible prediction model for faculty development in local vocational colleges.

Evaluation Mechanism of Teaching Staff’s Building in Local Vocational Colleges Based on Big Data Technology

Nanjing Vocational University of Industry Technology

Shanghai University of Engineering Science

Applied Mathematics and Nonlinear Sciences

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

With the improvement of information technology, all industries have developed rapidly. However, the education industry is lagging behind in the process of...

Evaluation Mechanism of Teaching Staff’s Building in Local Vocational Colleges Based on Big Data Technology

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 24 févr. 2022

Accepté: 17 juil. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00067

Mots clés
Big data, Teacher teaching quality, Evaluation mechanism, Ordered classification, Unordered classification

© 2023 Jingjing Dai et al., published by Sciendo

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

Evaluation Mechanism of Teaching Staff’s Building in Local Vocational Colleges Based on Big Data Technology

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 24 févr. 2022

Accepté: 17 juil. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00067

Mots clésBig data, Teacher teaching quality, Evaluation mechanism, Ordered classification, Unordered classification

© 2023 Jingjing Dai et al., published by Sciendo

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

Mots clés
Big data, Teacher teaching quality, Evaluation mechanism, Ordered classification, Unordered classification