MANAGING MUSIC CURRICULUM WITH PREDICTIVE ANALYTICS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6874Keywords:
Predictive Analytics, Music Education, Curriculum Management, Learning Outcome Prediction, LSTM, Random ForestAbstract [English]
The given research provides a data-driven model of improving music education based on predictive modeling and learner analytics. Music programs based on traditional curriculum management are mostly subjective-based and lack flexibility to accommodate the needs of different learners due to their fixed progression. Three predictive algorithms Multiple Linear Regression (MLR), Random Forest (RF), and Long Short-Term Memory (LSTM) networks were used to predict performance, engagement, and creative development of students to increase accuracy and response rates. The data used in experimental assessment with 620 music learners in six institutions found that LSTM was the highest predicted accuracy of 94.6, better than RF (89.3) and MLR (83.7). In addition, the efficiency of curriculum adaptation increased by 28 percent and the general student engagement was increased by 32 percent as compared to the manual approaches to planning. The most important predictors included such essential features as practice frequency, tonal recognition, rhythmic precision, and ensemble collaboration scores. As the comparative analysis shows, predictive analytics can be of great benefit when it comes to designing, evaluating, and personalizing music curricula. With the assistance of ongoing data-feedback and smart prediction, teachers will be able to make evidence-based choices, which will enhance creativity, inclusiveness, and quantifiable artistic progress. This paradigm signifies the transition to smart, flexible, and results-focused music education paradigms.
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Copyright (c) 2025 Rakesh Srivastava, Shailendra Kumar Sinha, Dr. Kruti Sutaria, Danish Kundra, V. Nirupa, Shailesh Kulkarni
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