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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Academic International Dialogue (AID)
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32122/ 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 |
| _version_ | 1848823942517620736 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:05:09Z |
| format | Conference or Workshop Item |
| id | ump-32122 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:05:09Z |
| publishDate | 2021 |
| publisher | Academic International Dialogue (AID) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://imicaidconference.weebly.com/imic-2021-e-proceeding.html |
| 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 |
| title | 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_short | 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 |
| topic | LB1603 Secondary Education. High schools QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/32122/ http://umpir.ump.edu.my/id/eprint/32122/ 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 |
