A Review On Student Performance Prediction Using Supervised Learning Techniques

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Om Motvani; Ankit Kalariya; Nisha Vadodariya

A Review On Student Performance Prediction Using Supervised Learning Techniques

Authors: Om Motvani, Ankit Kalariya, Nisha Vadodariya

Unique Paper ID: 173141
Volume: 11
Issue: 9
PageNo: 2842-2844

Keywords: Decision Tree SVM CNN Neural Network.

Abstract:
Assessing student performance is an essential step for achieving academic success and delivering timely support to students who may be at risk. This review article aggregates findings from ten research studies, concentrating on using supervised learning methods to forecast academic results. Various algorithms have been thoroughly examined, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees. This article delves into the techniques used, performs a comparative analysis of different models, addresses challenges faced, and suggests future research directions, underlining the transformative impact of machine learning in the field of education.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 9
PageNo: 2842-2844

A Review On Student Performance Prediction Using Supervised Learning Techniques

Available:https://ijirt.org/article?manuscript=173141

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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