PERFORMANCE DATA ANALYTICS FOR MUSIC INSTITUTIONS - Verma, Subhash Kumar; Dandu, Varalakshmi; Ghosh, Debanjan; Tripathy, Sasmeeta; Malhotra, Abhiraj; Ojha, Varun (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Potential to transform the music education system the use of data analytics in music education has the potential to radically transform the performance, pedagogy, and institutional effectiveness assessment system. The given paper will provide a detailed Performance Data Analytics framework, which will be used in the environment of the music institutions, considering both academic and artistic sides of the performance. It also discusses the way different sources of data (student assessment, the documentation of recitals, student attendance, peer rating, etc.) can be analyzed systematically towards arriving at actionable information. The research employs mixed research model that is a blend of the quantitative analysis that is using statistics, and the qualitative interpretation, in order to measure the performance outcomes. The computation and graphical representation of complicated data patterns are done in Python, R, SPSS, and Excel. The systematic analysis of the literature shows a lack of domain-specific analytics models in music education where the traditional data evaluation practices do not take into account data-driven insights. The suggested analytics framework proposes some Key Performance Indicators (KPIs) peculiar to musical performance including technical mastery, expressive interpretations, teamwork, and temporal stability. An example of this framework application with the help of selected music institutions to case study gives the evidence of correlations between the teaching methods, practices, and performance development.