Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{198426,
        author = {Repala Praveen and S.Sai Kowshik and Singareddy Mahesh},
        title = {Al-Based Student Dropout Prediction and Programming Skill Assessment System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9787-9794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198426},
        abstract = {Student dropout is a major challenge for educational institutions, affecting both academic outcomes and institutional performance. Early identification of students who are at risk can help educators provide timely support and improve retention rates. This research proposes a machine learning–based system that predicts student dropout risk while also evaluating programming skills through an online mock test platform. The system analyzes academic performance data, attendance records, and student engagement metrics to detect patterns related to academic difficulties. Machine learning algorithms such as Random Forest, Decision Tree, and Support Vector Machine are used to classify students based on dropout risk. In addition, programming mock tests are used to assess coding proficiency and categorize students into different skill levels. Based on these results, the system provides personalized learning recommendations. The proposed approach helps educational institutions monitor student performance more effectively and supports improved learning outcomes.},
        keywords = {Educational Data Mining, Machine Learning, Student Performance Prediction, Programming Skill Assessment, Learning Analytics.},
        month = {April},
        }