Work

Leveraging Machine Learning for Predicting Student Performance

Year: 2025

Type: article

Abstract: Predicting student performance is a crucial challenge in the education sector. This research explores the application of machine learning techniques to forecast academic success based on historical student data, including attendance, past grades, and behavioural patterns. We developed and evaluated multiple machine learning models, including Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression, to determine their effectiveness in predicting academic outcomes. Our analysis highlights that Random Forest provides the highest accuracy, while Logistic Regression offers a balance between interpretability and performance. The study aims to assist educators in identifying students at risk of underperformance, enabling timely intervention strategies

Source: International Journal of Advanced Research in Science Communication and Technology

DOI: 10.48175/ijarsct-23332

Authors: Ms. Riddhi Kanabar, Dr. Pallavi Devendra Tawde

Institution: University of Mumbai

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W4407809260

FWCI: 2.927

Cites:

Cited by: 3

Related to: 10