Identification and Minimization of Students Academics Failure Using Machine Learning Techniques
Year: 2025
Type: article
Abstract: Early identification of students at risk of academic failure is critical for ensuring timely support and improving educational outcomes. Despite the availability of extra assignments and projects, many students struggle academically without early intervention. This project aims to address this issue by developing a predictive model that identifies high school students at risk of failing before the end of the academic period. By accurately predicting these students, teachers can provide additional assistance and resources to prevent failure and enhance overall student performance. The core objective of this project is to increase student success by offering targeted support to those identified as at risk. We will analyze various factors influencing student performance, such as academic history, behavior, and engagement, and use this data to develop a model that can predict potential failures early. By providing insights into students’ risk levels, educators will be equipped to take timely actions, such as offering extra learning materials and personalized interventions, to improve academic outcomes. This project emphasizes the importance of early intervention in education and aims to reduce student failure rates
Author: Kirti Ambekar
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4408207936