Research pick: From notes to noteworthy – tuning up music education in China - "Application of improved K-means algorithm in the cultivation of creative music talents under the needs of sustainable development and transformation"

Research published in the International Journal of Web Engineering and Technology has looked at how training might be enhanced to help foster innovative musical talent in university education. The work used advanced data analysis techniques with an improved K-means clustering algorithm to help educators identify how they might improve matters in this area.

Peng Li of Beijing Normal University and Zeng Fan of the City University of Macau, China, explain how China has experienced substantial development, leading to major changes in industry and its economic structure. This recent progress has led to greater demands for educational reform that fit society’s needs. The government hopes to develop world-class universities that have distinctive Chinese character and to use technology to assist in this endeavour. As such, data analysis has become important in understanding educational matters.

The team used this data-driven technology to analyse learning outcomes among students on music courses. In their work, the K-means clustering algorithm, known for its efficiency, was assisted by a noise reduction autoencoder, a type of neural network, to improve the results beyond what is commonly possible with just the clustering algorithm. This approach allows them to manage and analyse large data sets.

The results show a disparity between student performance in theoretical and practical music courses. Students generally perform better in theoretical subjects, with a high percentage achieving passing and top scores. In contrast, practical courses such as composition, improvisation, and live performance show lower and more average scores, highlighting a gap between theoretical knowledge and practical skills.

Li and Fan suggest that their findings point to how current curricula, which place a lot of emphasis on theoretical knowledge at the expense of practical skills, might be modified to rectify this imbalance.

Li, P. and Fan, Z. (2024) ‘Application of improved K-means algorithm in the cultivation of creative music talents under the needs of sustainable development and transformation’, Int. J. Web Engineering and Technology, Vol. 19, No. 1, pp.4–19.