Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning
The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attribut...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
OmniaScience
2023-01-01
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| Series: | Journal of Technology and Science Education |
| Subjects: | |
| Online Access: | https://www.jotse.org/index.php/jotse/article/view/1707 |
| _version_ | 1828040199098073088 |
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| author | Chi-ho So Pui-ling Chan Simon Chi-wang Wong Adam Ka-lok Wong Ho-yin Tsang Henry C. B. Chan |
| author_facet | Chi-ho So Pui-ling Chan Simon Chi-wang Wong Adam Ka-lok Wong Ho-yin Tsang Henry C. B. Chan |
| author_sort | Chi-ho So |
| collection | DOAJ |
| description | The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students’ generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students’ activity and academic records in the case higher education institution to find out three major types of patterns between the students’ participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper. |
| first_indexed | 2024-04-10T16:53:27Z |
| format | Article |
| id | doaj.art-c5cffcb2e1794f72809f6daf55b31b88 |
| institution | Directory Open Access Journal |
| issn | 2013-6374 |
| language | English |
| last_indexed | 2024-04-10T16:53:27Z |
| publishDate | 2023-01-01 |
| publisher | OmniaScience |
| record_format | Article |
| series | Journal of Technology and Science Education |
| spelling | doaj.art-c5cffcb2e1794f72809f6daf55b31b882023-02-07T11:17:36ZengOmniaScienceJournal of Technology and Science Education2013-63742023-01-0113110411510.3926/jotse.1707333Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learningChi-ho So0Pui-ling Chan1Simon Chi-wang Wong2Adam Ka-lok Wong3Ho-yin Tsang4Henry C. B. Chan5School of Professional Education and Executive Development, The Hong Kong Polytechnic UniversityHong Kong Community College, The Hong Kong Polytechnic UniversitySchool of Professional Education and Executive Development, The Hong Kong Polytechnic UniversitySchool of Professional Education and Executive Development, The Hong Kong Polytechnic University,School of Professional Education and Executive Development, The Hong Kong Polytechnic UniversityDepartment of Computing, The Hong Kong Polytechnic University, Hong KongThe project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students’ generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students’ activity and academic records in the case higher education institution to find out three major types of patterns between the students’ participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.https://www.jotse.org/index.php/jotse/article/view/1707classification and clustering, supervised, unsupervised learning |
| spellingShingle | Chi-ho So Pui-ling Chan Simon Chi-wang Wong Adam Ka-lok Wong Ho-yin Tsang Henry C. B. Chan Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning Journal of Technology and Science Education classification and clustering, supervised, unsupervised learning |
| title | Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning |
| title_full | Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning |
| title_fullStr | Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning |
| title_full_unstemmed | Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning |
| title_short | Some pattern recognitions for a recommendation framework for higher education students’ generic competence development using machine learning |
| title_sort | some pattern recognitions for a recommendation framework for higher education students generic competence development using machine learning |
| topic | classification and clustering, supervised, unsupervised learning |
| url | https://www.jotse.org/index.php/jotse/article/view/1707 |
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