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A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm

The presence of a global health crisis on coronavirus pandemic (COVID-19) has been accelerated the global uptakes the transformation towards the digital economy. Consequently, the rapid digital transformation has risen the demands of technologically competent workforces in which open the big doors f...

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Main Authors: Chuan, Zun Liang, Norhayati, Rosli, Fam, Soo Fen, Tan, Ee Hiae
Format: Conference or Workshop Item
Language:English
English
Published: Academic International Dialogue (AID) 2021
Subjects:
LB1603 Secondary Education. High schools
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/32122/1/IMIC%202021.pdf
http://umpir.ump.edu.my/id/eprint/32122/7/A%20predictive%20model%20of%20the%20enrolment%20in%20the%20key%20subject%20of%20STEM%20education%20.pdf
http://umpir.ump.edu.my/id/eprint/32122/
https://imicaidconference.weebly.com/imic-2021-e-proceeding.html
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spelling my.ump.umpir.321222022-09-02T04:18:18Z http://umpir.ump.edu.my/id/eprint/32122/ A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm Chuan, Zun Liang Norhayati, Rosli Fam, Soo Fen Tan, Ee Hiae LB1603 Secondary Education. High schools QA Mathematics The presence of a global health crisis on coronavirus pandemic (COVID-19) has been accelerated the global uptakes the transformation towards the digital economy. Consequently, the rapid digital transformation has risen the demands of technologically competent workforces in which open the big doors for the education and careers of Sciences, Technology, Engineering and Mathematics (STEM). Due to Additional Mathematics is the principal subject for the STEM related subjects in producing qualified and skilful human capital demanded in 21st digital economy era. Therefore, this article presented a predictive model of the enrolment in Additional Mathematics using a supervised machine learning model, namely binary logistic regression model. The findings of this article can be beneficial the decision makers by taking appropriate initiatives in increasing the number upper secondary students enrol in STEM education, particularly school teachers and students’ parents. Academic International Dialogue (AID) 2021-02-04 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32122/1/IMIC%202021.pdf pdf en http://umpir.ump.edu.my/id/eprint/32122/7/A%20predictive%20model%20of%20the%20enrolment%20in%20the%20key%20subject%20of%20STEM%20education%20.pdf Chuan, Zun Liang and Norhayati, Rosli and Fam, Soo Fen and Tan, Ee Hiae (2021) A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm. In: International Multidisciplinary Innovation Competition (IMIC) 2021, 04 February 2021 , Virtual. pp. 4-6.. ISBN 9789671866160 https://imicaidconference.weebly.com/imic-2021-e-proceeding.html
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic LB1603 Secondary Education. High schools
QA Mathematics
spellingShingle LB1603 Secondary Education. High schools
QA Mathematics
Chuan, Zun Liang
Norhayati, Rosli
Fam, Soo Fen
Tan, Ee Hiae
A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm
description The presence of a global health crisis on coronavirus pandemic (COVID-19) has been accelerated the global uptakes the transformation towards the digital economy. Consequently, the rapid digital transformation has risen the demands of technologically competent workforces in which open the big doors for the education and careers of Sciences, Technology, Engineering and Mathematics (STEM). Due to Additional Mathematics is the principal subject for the STEM related subjects in producing qualified and skilful human capital demanded in 21st digital economy era. Therefore, this article presented a predictive model of the enrolment in Additional Mathematics using a supervised machine learning model, namely binary logistic regression model. The findings of this article can be beneficial the decision makers by taking appropriate initiatives in increasing the number upper secondary students enrol in STEM education, particularly school teachers and students’ parents.
format Conference or Workshop Item
author Chuan, Zun Liang
Norhayati, Rosli
Fam, Soo Fen
Tan, Ee Hiae
author_facet Chuan, Zun Liang
Norhayati, Rosli
Fam, Soo Fen
Tan, Ee Hiae
author_sort Chuan, Zun Liang
title A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm
title_short A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm
title_full A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm
title_fullStr A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm
title_full_unstemmed A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm
title_sort predictive model of the enrolment in the key subject of stem education using the machine learning paradigm
publisher Academic International Dialogue (AID)
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/32122/1/IMIC%202021.pdf
http://umpir.ump.edu.my/id/eprint/32122/7/A%20predictive%20model%20of%20the%20enrolment%20in%20the%20key%20subject%20of%20STEM%20education%20.pdf
http://umpir.ump.edu.my/id/eprint/32122/
https://imicaidconference.weebly.com/imic-2021-e-proceeding.html
_version_ 1743107212208766976
score 13.211869

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