Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50021066" target="_blank" >RIV/62690094:18450/23:50021066 - isvavai.cz</a>

Result on the web

<a href="https://www.mecs-press.org/ijmecs/ijmecs-v15-n6/v15n6-1.html" target="_blank" >https://www.mecs-press.org/ijmecs/ijmecs-v15-n6/v15n6-1.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5815/ijmecs.2023.06.01" target="_blank" >10.5815/ijmecs.2023.06.01</a>

Result language

angličtina

Original language name

Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches

Original language description

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students&apos; placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students&apos; placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of thi s study could be beneficial to educational institutions, students, and career counsellors. © 2023, Modern Education and Computer Science Press. All rights reserved.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Modern Education and Computer Science

ISSN

2075-0161

e-ISSN

2075-017X

Volume of the periodical

15

Issue of the periodical within the volume

6

Country of publishing house

HK - HONG KONG

Number of pages

13

Pages from-to

1-13

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85179328560