Assessing Academic Stress and Developing Feedback Systems to Enhance Student Well-being and Performance

Authors

Supriya, Dr. Navin Kumar

Abstract

This study investigates the factors contributing to academic stress and student dropout in educational settings, aiming to develop effective feedback systems to support students' growth. The research uses a Multidimensional Feedback System (MFS) to analyze a range of stressors, including academic pressures, social dynamics, and environmental influences. By employing data analysis techniques like regression modeling and clustering, the study identifies key stress factors and their impact on student well-being. An AI-based MFS, with real-time monitoring and personalized feedback, helps predict student outcomes and detect at-risk students early, facilitating timely interventions. The system employs machine learning models such as Random Forest to assess dropout risks, achieving high accuracy in prediction. Factors like comprehensive data collection, real-time monitoring, and multifaceted analysis are shown to be crucial in creating effective feedback systems. The study underscores the importance of a multifaceted approach to addressing academic stress and student dropout, suggesting that AI-driven educational analytics can foster a supportive learning environment and improve student outcomes.

Keywords

Machine Learning Models, AI, Stress Factors, Multidimensional Feedback System, optimizing teaching practices, Analysis of students’ Performance

Assessing Academic Stress and Developing Feedback Systems to Enhance Student Well-being and Performance. Supriya, Dr. Navin Kumar. 2023. IJIRCT, Volume 9, Issue 6. Pages 1-13. https://www.ijirct.org/viewPaper.php?paperId=2405010