Predicting academic major of students using bayesian networks to the case of iran

TL;DR

This paper employs Bayesian networks to predict high school students' academic majors in Iran, analyzing influential factors and providing guidance for educational planning.

Contribution

It introduces the use of Bayesian networks with effective indicators for predicting students' academic majors in Iran for the first time.

Findings

Bayesian networks effectively predict students' academic majors.

Identified key factors influencing major selection.

Provided educational guidance based on predictions.

Abstract

In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.