PREDICTIVE DROPOUT ANALYSIS IN ART EDUCATION MANAGEMENT - Nagpurkar, Vijay; Bhanuse, Vijaykumar; Patil, Mukesh; Gunjotikar, Shruti H.; Singh, Gopal; Pal, Sanjay (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Student drop out of the art education programs in the academic institutions is a major problem because the students tend to drop out of the programs due to a complex interplay of creative, behavioral, psychological, and the socio-economic factors and not as a result of their work performance. This research paper introduces a predictive dropout analysis model that suits the field of art education management, and which can be used to predict potential at-risk students at an early stage and effectively implement data-driven, time-based response. The framework combines institutional data that is heterogeneous, such as attendance data, studio and portfolio submissions, assessment data, and traces of use of digital tools with psycho-creative data, such as creativity indices, portfolio development rates, and qualitative feedback on critique. The advanced feature engineering methods are used to extract the measures of engagement-trajectories, skill-growth-slopes, and creative-consistency indicators of the longitudinal dynamics of learning specific to art-based programs. Several machine learning models, which include logistic regression, random forest, support vectors machines, artificial neural networks, and gradient boosting are trained and tested through a structured training-validation pipeline through hyperparameter optimization. The accuracy, area under the ROC curve, F1-score, and the precision-recall are the measures of model performance that are evaluated to guarantee that the model can be robust under class imbalance conditions. The experimental findings show that ensemble and non-linear models have a higher performance compared to the traditional baselines and show the predictive power of creative interaction and behavioral characteristics as well as academic indicators.