Study of educational achievement based on examining machine learning techniques: Systematic Literature


EOI: 10.11242/viva-tech.01.09.28

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Citation

Kshiteeja Mhatre,Dr.Hiren Dand,"Study of educational achievement based on examining machine learning techniques: Systematic Literature" VIVA-IJRI Volume 1, Issue 9, Article 28, pp. 1-8, 2026. Published by MCA Department, VIVA Institute of Technology, Virar, India.

Abstract

TStudents, courses, and academic activities produce a lot of data for educational institutions. The amount, diversity, and complexity of this data make effective analysis difficult. Learners may be categorized according to their skills and abilities, learning behavior can be understood, and academic performance can be predicted using sophisticated data analysis tools. In order to solve these issues, this study uses the machine learning technique to analyze 15 relevant research papers published between 2019 and 2025 as part of a systematic literature review on machine learning technology for forecasting student performance. Examine machine learning techniques based on classification, data mining, ensemble approaches, k-nearest neighbor, and K-means. According to the survey, the most popular method for forecasting student academic performance also yielded the best accuracy findings, ranging from 70% to 98.6%. The study emphasizes how important it is to identify students who are at risk of learning challenges early on so that educators and policymakers may offer timely and appropriate support. It also highlights a number of difficulties, such as inconsistent performance metrics, restricted model application in different situations, skewed datasets, and ethical challenges, underscoring the need for more reliable research techniques and well- rounded prediction strategies in educational analytics.

Keywords

academic performance, classification, data mining, ensemble approaches, K-means, k-nearest neighbor, literature review, machine learning, student.

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