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

<p>With the rapid development of the social economy and the continuous progress of science and technology, vocational education plays a crucial role in cultivating high-quality talents and promoting economic development. In this paper, based on the requirements of practice and innovation of digitization-enabled high-quality development of vocational education, a time-series decomposition model based on Transformer is proposed, which is defined as the DMR former model. The original data are decomposed using the temporal decomposition model, combined with the multi-scale fusion residual attention mechanism to capture and process the temporal feature information of vocational education in multiple time scales, and finally, the obtained results are analyzed. The results show that vocational education performs poorly in the classroom effect, most of the attention of vocational education students in the classroom is not concentrated, and the degree of liking for the classroom is lower than 0.5. After the digital empowerment of classroom effect, the student’s performance can be stabilized at more than 85 compared to the average of about 60 in the previous period, which is a good effect of improvement. After improving the curriculum of the College of Vocational Education, the employment rate of students increased to more than 95%. The high-quality development of vocational education and meeting the social demand for talent can be promoted through time series data analysis and digital empowerment.</p>
</abstract>

With the rapid development of the social economy and the continuous progress of science and technology, vocational education plays a crucial role in cultivating high-quality talents and promoting economic development. In this paper, based on the requirements of practice and innovation of digitization-enabled high-quality development of vocational education, a time-series decomposition model based on Transformer is proposed, which is defined as the DMR former model. The original data are decomposed using the temporal decomposition model, combined with the multi-scale fusion residual attention mechanism to capture and process the temporal feature information of vocational education in multiple time scales, and finally, the obtained results are analyzed. The results show that vocational education performs poorly in the classroom effect, most of the attention of vocational education students in the classroom is not concentrated, and the degree of liking for the classroom is lower than 0.5. After the digital empowerment of classroom effect, the student’s performance can be stabilized at more than 85 compared to the average of about 60 in the previous period, which is a good effect of improvement. After improving the curriculum of the College of Vocational Education, the employment rate of students increased to more than 95%. The high-quality development of vocational education and meeting the social demand for talent can be promoted through time series data analysis and digital empowerment.

Practices and Innovations in Analyzing Digital Enabling High-Quality Development of Vocational Education Based on Time-Series Data Analytics

Applied Mathematics and Nonlinear Sciences

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

With the rapid development of the social economy and the continuous progress of science and technology, vocational education plays a crucial role in...

Practices and Innovations in Analyzing Digital Enabling High-Quality Development of Vocational Education Based on Time-Series Data Analytics

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 26. Dez. 2023

Akzeptiert: 31. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0289

Schlüsselwörter
Chronological data analysis, Digital empowerment, DMR former model, Vocational education

© 2024 Bocheng Ji, published by Sciendo

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

Practices and Innovations in Analyzing Digital Enabling High-Quality Development of Vocational Education Based on Time-Series Data Analytics

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 26. Dez. 2023

Akzeptiert: 31. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0289

SchlüsselwörterChronological data analysis, Digital empowerment, DMR former model, Vocational education

© 2024 Bocheng Ji, published by Sciendo

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

Schlüsselwörter
Chronological data analysis, Digital empowerment, DMR former model, Vocational education