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

<p>This paper presents an incomplete multi categorization algorithm based on relative distance, which instead of simply using the Euclidean distance between class centers to measure the degree of dissimilarity between classes, uses the relative distance between classes to measure the dissimilarity of classes. The entire algorithm strictly follows the principle of splitting the most easily separable classes first, which decreases the accumulation of errors occurring in the binomial tree structure and improves the algorithm’s overall evaluation accuracy. Relevant tests are designed to measure the comprehensive performance of the algorithm. In terms of accuracy, the algorithm in this paper can be 4.14% higher than the best OVR algorithm and 1.63% higher than the best OVO. The experimental results prove that the algorithm is effective and that the overall time consumption in testing time is less than OVR and OVO. The analysis of young teachers’ informatization teaching ability obtained that the main influencing factors were the lack of theoretical guidance for teachers’ teaching, the lack of a common sharing platform for subject teaching resources and the lack of a sound service support system, which influenced the informatization teaching ability to 86%, 83.62% and 84.06%, respectively. Therefore, the results proved that the algorithm is effective, young teachers should focus on theoretical training in teaching informatization, and the school should build an informatization teaching platform to improve the service support system.</p>
</abstract>

This paper presents an incomplete multi categorization algorithm based on relative distance, which instead of simply using the Euclidean distance between class centers to measure the degree of dissimilarity between classes, uses the relative distance between classes to measure the dissimilarity of classes. The entire algorithm strictly follows the principle of splitting the most easily separable classes first, which decreases the accumulation of errors occurring in the binomial tree structure and improves the algorithm’s overall evaluation accuracy. Relevant tests are designed to measure the comprehensive performance of the algorithm. In terms of accuracy, the algorithm in this paper can be 4.14% higher than the best OVR algorithm and 1.63% higher than the best OVO. The experimental results prove that the algorithm is effective and that the overall time consumption in testing time is less than OVR and OVO. The analysis of young teachers’ informatization teaching ability obtained that the main influencing factors were the lack of theoretical guidance for teachers’ teaching, the lack of a common sharing platform for subject teaching resources and the lack of a sound service support system, which influenced the informatization teaching ability to 86%, 83.62% and 84.06%, respectively. Therefore, the results proved that the algorithm is effective, young teachers should focus on theoretical training in teaching informatization, and the school should build an informatization teaching platform to improve the service support system.

Research on the Development of Young Teachers’ Informatization Teaching Ability in Universities Based on Big Data Technology

Applied Mathematics and Nonlinear Sciences

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

This paper presents an incomplete multi categorization algorithm based on relative distance, which instead of simply using the Euclidean distance between class...

Research on the Development of Young Teachers’ Informatization Teaching Ability in Universities Based on Big Data Technology

Online veröffentlicht: 28. Aug. 2023

Seitenbereich: -

Eingereicht: 01. Okt. 2022

Akzeptiert: 08. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00259

Schlüsselwörter
Euclidean distance, Information-based teaching ability, Binomial tree, BT-SVM algorithm

© 2023 Qiongyan Xu, published by Sciendo

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

Research on the Development of Young Teachers’ Informatization Teaching Ability in Universities Based on Big Data Technology

Online veröffentlicht: 28. Aug. 2023

Seitenbereich: -

Eingereicht: 01. Okt. 2022

Akzeptiert: 08. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00259

SchlüsselwörterEuclidean distance, Information-based teaching ability, Binomial tree, BT-SVM algorithm

© 2023 Qiongyan Xu, published by Sciendo

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

Schlüsselwörter
Euclidean distance, Information-based teaching ability, Binomial tree, BT-SVM algorithm