PREDICTIVE ANALYTICS IN ARTS EDUCATION MANAGEMENT - Shinde, Jyoti M.; Chavan, Gajanan; Sargam, Sadhana; Patil, Mukesh; Seethaladevi S.; Nikam, Ashwini Prakash (ShodhKosh: Journal of Visual and Performing Arts, 2026)
The growing sophistication of arts education management, which is created by the variety of learning trajectories, subjective evaluation practices, and production of multimodal creative products, is the reason why the decision-support mechanisms developed beyond the conventional descriptive analytics. The given paper presents an IEEE-based predictive analytics framework adapted to arts education management with the incorporation of heterogeneous data, interpretable feature engineering, and management-focused predictive modeling. The framework integrates the administrative records, rubric-based testing, digital portfolios, reflective narratives, and behavioral interaction logs in a systematic way to simulate the learner progress, dynamics of engagement, and institutional demand. Several predictive modeling methods including regression and tree models, ensemble and neural methods are tested through a single experimental protocol focusing on accuracy, ability to detect risks early and interpretability. The experimental findings show that the ensemble-based models with the aid of process-oriented and multi-modal features provide better predictive capability and detect the at-risk learners much earlier than the traditional methods do. The results demonstrate that the indicators of engagement frequency, frequency of portfolio iteration, and depth of reflection are more informative than the academic measures that remain constant in a creative learning setting. Noteworthy, interpretability and ethical conformity are critical concerns in the research that make sure that predictive outputs are used to facilitate transparent, accountable, and pedagogically informed decision-making. The current work shows how predictive analytics as an advisory system within a feedback-based management system can be used to promote foresight, support learners, and resource planning without losing creative independence. The recommended solution will provide a scaffolding platform of future studies and application of predictive analytics in educational institutions of arts.