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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

International Journal of Modern Education and Computer Science

ISSN

2075-0161

e-ISSN

2075-017X

Svazek periodika

15

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

HK - Hongkong

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85179328560