The application of new media communication technology in vocational education teaching under the background of big data

Exploring the application of new media communication technology in vocational education teaching is better for improving the skill level of vocational school students. In this paper, starting from the KNN algorithm in the context of big data, the basic principle and process of the algorithm are expl...

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Bibliographic Details
Published inApplied mathematics and nonlinear sciences Vol. 9; no. 1
Main Author Wang, Xueying
Format Journal Article
LanguageEnglish
Published Sciendo 01.01.2024
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Summary:Exploring the application of new media communication technology in vocational education teaching is better for improving the skill level of vocational school students. In this paper, starting from the KNN algorithm in the context of big data, the basic principle and process of the algorithm are explained, and the principle and model of the wolf pack optimization algorithm are introduced. The parameters of the KNN algorithm are optimized using the wolf pack search algorithm, and the definition of the objective function, initialization and solution of the optimal K value are given, which leads to the WPOA-KNN analysis model. The new media communication technology is analyzed, including teaching media, new media teaching technology and the advantages of new media communication technology applied to vocational education teaching. The WPOA-KNN analysis model was used to analyze and demonstrate the teaching of new media technology for vocational education in a higher education institution. From the satisfaction evaluation, the percentage of those who said they liked it was 76.06%, and the percentage of those who disliked it was only 9.09%. In terms of students’ learning behavior, the overall percentage of those who rated above C grade was 92.37%. This shows that using new media communication technology for vocational education teaching can enhance students’ learning enthusiasm, make them have a more exquisite technical level, and meet the diversified talent needs of employing enterprises.
ISSN:2444-8656
2444-8656
DOI:10.2478/amns.2023.2.00295