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<p>With the ongoing advancement of AI in intelligent marketing, there is a pressing need for the continuous innovation and evolution of advertising planning courses in higher vocational education to keep pace with environmental shifts. This paper introduces a sophisticated smart learning model, beginning with the construction of a subject knowledge map and detailing its development methodology. Building upon this foundation, the paper enhances the Dijkstra algorithm and integrates it with the ant colony algorithm to offer personalized learning path recommendations. Furthermore, an improved convolutional neural network is employed to generate these customized learning paths. An empirical study is conducted using the advertising planning course of a higher vocational school as a case study. The findings of this research highlight the efficacy of the proposed intelligent learning model in the instructional process. Notably, there is a significant increase in student engagement, with the level of active problem-solving in the classroom rising from an initial average of 14 to 26. Additionally, the average final grades of the experimental group exceeded those of the control group by 7.63 points. The average comprehensive competence score also showed a substantial enhancement, registering at 0.447, indicating a marked improvement in overall student performance.</p>
</abstract>

With the ongoing advancement of AI in intelligent marketing, there is a pressing need for the continuous innovation and evolution of advertising planning courses in higher vocational education to keep pace with environmental shifts. This paper introduces a sophisticated smart learning model, beginning with the construction of a subject knowledge map and detailing its development methodology. Building upon this foundation, the paper enhances the Dijkstra algorithm and integrates it with the ant colony algorithm to offer personalized learning path recommendations. Furthermore, an improved convolutional neural network is employed to generate these customized learning paths. An empirical study is conducted using the advertising planning course of a higher vocational school as a case study. The findings of this research highlight the efficacy of the proposed intelligent learning model in the instructional process. Notably, there is a significant increase in student engagement, with the level of active problem-solving in the classroom rising from an initial average of 14 to 26. Additionally, the average final grades of the experimental group exceeded those of the control group by 7.63 points. The average comprehensive competence score also showed a substantial enhancement, registering at 0.447, indicating a marked improvement in overall student performance.

Innovation and Implementation of Teaching Mode of Higher Vocational Advertising Planning Course in the Context of AI Smart Marketing

Applied Mathematics and Nonlinear Sciences

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

With the ongoing advancement of AI in intelligent marketing, there is a pressing need for the continuous innovation and evolution of advertising planning...

Innovation and Implementation of Teaching Mode of Higher Vocational Advertising Planning Course in the Context of AI Smart Marketing

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 15 mar 2024

Accettato: 09 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1679

Parole chiave
Dijkstra’s algorithm, Ant colony optimization algorithm, Convolutional neural network, Knowledge graph, Learning path recommendation

© 2024 Cuifeng Zeng., published by Sciendo

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

Innovation and Implementation of Teaching Mode of Higher Vocational Advertising Planning Course in the Context of AI Smart Marketing

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 15 mar 2024

Accettato: 09 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1679

Parole chiaveDijkstra’s algorithm, Ant colony optimization algorithm, Convolutional neural network, Knowledge graph, Learning path recommendation

© 2024 Cuifeng Zeng., published by Sciendo

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

Parole chiave
Dijkstra’s algorithm, Ant colony optimization algorithm, Convolutional neural network, Knowledge graph, Learning path recommendation