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SOME PATTERN RECOGNITIONS FOR A RECOMMENDATION FRAMEWORK FOR HIGHER EDUCATION STUDENTS' GENERIC COMPETENCE DEVELOPMENT USING MACHINE LEARNING.

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Title: SOME PATTERN RECOGNITIONS FOR A RECOMMENDATION FRAMEWORK FOR HIGHER EDUCATION STUDENTS' GENERIC COMPETENCE DEVELOPMENT USING MACHINE LEARNING.
Authors: Chi-ho So, Joseph1 joseph.so@cpce-polyu.edu.hk, Ka-lok Wong, Adam1 adam.wong@cpce-polyu.edu.hk, Kia Ho-yin Tsang1 kia.tsang@speed-polyu.edu.hk, Pui-ling Chan, Ada2 ada.chan@cpce-polyu.edu.hk, Chi-wang Wong, Simon1 simon.wong@cpce-polyu.edu.hk, Chan, Henry C. B.3 henry.chan.comp@polyu.edu.hk
Source: Journal of Technology & Science Education. 2023, Vol. 13 Issue 1, p104-115. 12p.
Subject Terms: *Machine learning, *Education students, *Higher education, *Student activities, Pattern recognition systems, Python programming language
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:20145349
DOI:10.3926/jotse.1707