Work

A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms

Year: 2021

Type: article

Abstract: In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission.

Source: International Journal of Online and Biomedical Engineering (iJOE)

DOI: 10.3991/ijoe.v17i02.20049

Authors: Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, Aimad Qazdar

Institutions: Université Hassan 1er, Cadi Ayyad University

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $312