Soft Alert Generation for student Dropout Mitigation and Proactive Management by Machine Learning Algorithms
S. Geetha, A. Spandana, D Vijay, M. V. Vishruth · 2025
Proactive dropout mitigation is crucial for enhancing student success and retention in schools. Dropout rates pose significant challenges in education systems globally, impacting students, communities, and societies. This paper emphasizes the necessity of proactive measures to address dropout rates and support student achievement by building an educational network. Through proactive approaches, schools can identify and address risk factors early, preventing student disengagement and dropout. Various strategies such as early interventions, personalized support systems, academic enrichment programs, and community partnerships are examined for their efficacy in promoting student engagement and retention. Additionally, the role of data-driven decision-making and predictive analytics in identifying at-risk students and tailoring interventions is explored. Implementing proactive dropout mitigation requires collaboration among educators, administrators, families, and community stakeholders. Prioritizing proactive measures can foster a supportive learning environment, empowering students to excel academically. This paper highlights the significance of proactive dropout mitigation that has been enabled using a seemingly enhanced automated full-stack student management system integrated with ML algorithms used in enhancing student success and promoting educational equity. By investing in proactive interventions, educational institutions can reduce dropout rates and foster a culture of academic excellence and lifelong learning.