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<p>This study addresses the current challenges facing music major graduates, particularly the narrow scope of employment opportunities and intense job market competition. Focusing on students from higher vocational colleges and universities, this paper presents a novel career planning model specifically tailored for music majors. Utilizing the enhanced K-Means++ algorithm, this model clusters students by calculating distances between their features and constructs detailed profiles based on basic personal information. Furthermore, a hybrid recommendation model that combines feature attributes into novel cross-features is introduced. This model employs deep learning techniques to refine the predictive accuracy of the employment recommendations. Central to this approach is the development of a career planning recommendation system. For empirical validation, this study utilized data from the 2024 music major graduates of Jiaying College in Guangdong Province, China. Analysis revealed that post-intervention, students’ awareness of career planning significantly increased, with mean values exceeding 3, marking a substantial improvement from their pre-intervention assessments. Statistically significant enhancements were observed in career maturity, particularly in the dimensions of self-knowledge and self-confidence (P&lt;0.01), as well as in their understanding of the occupational landscape (P&lt;0.05). Additionally, students demonstrated a notable increase in their adversity quotient and in leveraging social support, with both metrics showing significant improvements (P&lt;0.05). This study highlights the effectiveness of the proposed career planning model in fostering a more comprehensive and informed approach to career readiness among music majors.</p>
</abstract>

This study addresses the current challenges facing music major graduates, particularly the narrow scope of employment opportunities and intense job market competition. Focusing on students from higher vocational colleges and universities, this paper presents a novel career planning model specifically tailored for music majors. Utilizing the enhanced K-Means++ algorithm, this model clusters students by calculating distances between their features and constructs detailed profiles based on basic personal information. Furthermore, a hybrid recommendation model that combines feature attributes into novel cross-features is introduced. This model employs deep learning techniques to refine the predictive accuracy of the employment recommendations. Central to this approach is the development of a career planning recommendation system. For empirical validation, this study utilized data from the 2024 music major graduates of Jiaying College in Guangdong Province, China. Analysis revealed that post-intervention, students’ awareness of career planning significantly increased, with mean values exceeding 3, marking a substantial improvement from their pre-intervention assessments. Statistically significant enhancements were observed in career maturity, particularly in the dimensions of self-knowledge and self-confidence (P<0.01), as well as in their understanding of the occupational landscape (P<0.05). Additionally, students demonstrated a notable increase in their adversity quotient and in leveraging social support, with both metrics showing significant improvements (P<0.05). This study highlights the effectiveness of the proposed career planning model in fostering a more comprehensive and informed approach to career readiness among music majors.

The Employment Trend of Music Industry and the Construction of Career Planning Model for Music Majors in Higher Vocational Colleges and Universities

University of Perpetual Help System Dalta

Applied Mathematics and Nonlinear Sciences

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

This study addresses the current challenges facing music major graduates, particularly the narrow scope of employment opportunities and intense job market...

The Employment Trend of Music Industry and the Construction of Career Planning Model for Music Majors in Higher Vocational Colleges and Universities

Data publikacji: 02 lip 2024

Zakres stron: -

Otrzymano: 29 lut 2024

Przyjęty: 16 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1555

Słowa kluczowe
Student profiling, K-Means++ algorithm, Deep learning, Career planning

© 2024 Qianhui Tang et al., published by Sciendo

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

The Employment Trend of Music Industry and the Construction of Career Planning Model for Music Majors in Higher Vocational Colleges and Universities

Data publikacji: 02 lip 2024

Zakres stron: -

Otrzymano: 29 lut 2024

Przyjęty: 16 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1555

Słowa kluczoweStudent profiling, K-Means++ algorithm, Deep learning, Career planning

© 2024 Qianhui Tang et al., published by Sciendo

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

Słowa kluczowe
Student profiling, K-Means++ algorithm, Deep learning, Career planning