MUMBAI, India, Feb. 27 -- Intellectual Property India has published a patent application (202641018807 A) filed by Dr. V. Sucharita; Dr. P. Venkateswara Rao; B. Vishnu; M. Pavan Kumar; R. V. Sivateja; M. Sasi Kumar; K. Sudharsan; E. Saranya; and Ch. Bhargav Sai, Gudur, Andhra Pradesh, on Feb. 19, for 'explainable machine learning framework for predicting student burnout and academic risk using behavioural learning analytics.'
Inventor(s) include Dr. V. Sucharita; Dr. P. Venkateswara Rao; B. Vishnu; M. Pavan Kumar; R. V. Sivateja; M. Sasi Kumar; K. Sudharsan; E. Saranya; and Ch. Bhargav Sai.
The application for the patent was published on Feb. 27, under issue no. 09/2026.
According to the abstract released by the Intellectual Property India: "An explainable machine learning framework for predicting student burnout and academic risk using behavioural learning analytics is disclosed. The system collects structured and unstructured engagement data from digital educational platforms including learning management systems, assessment modules, and attendance records. A feature engineering module derives behavioural and temporal indicators such as procrastination index, engagement variability, sentiment polarity, and performance volatility. A hybrid predictive modeling engine generates individualized burnout risk scores and academic risk probabilities. An integrated explainability engine applies feature attribution and counterfactual reasoning techniques to provide transparent, interpretable insights for stakeholders. The framework further includes an intervention recommendation module that maps identified risk factors to targeted academic or psychological support strategies. Continuous retraining and privacy preserving mechanisms enhance predictive reliability and ethical deployment. The invention enables early detection, proactive intervention, and improved student well being management within educational institutions."
Disclaimer: Curated by HT Syndication.
