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Analytic Study for Predictor Development on Student Participation in Generic Competence Development Activities Based on Academic Performance

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Title: Analytic Study for Predictor Development on Student Participation in Generic Competence Development Activities Based on Academic Performance
Language: English
Authors: So, Joseph Chi-Ho (ORCID 0000-0001-8784-3083), Ho, Yik Him (ORCID 0000-0001-9774-2294), Wong, Adam Ka-Lok (ORCID 0000-0001-7288-0199), Chan, Henry C. B. (ORCID 0000-0001-8024-0597), Tsang, Kia Ho-Yin (ORCID 0000-0003-2513-7102), Chan, Ada Pui-Ling, Wong, Simon Chi-Wang (ORCID 0000-0003-3408-9747)
Source: IEEE Transactions on Learning Technologies. 2023 16(5):790-803.
Availability: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Peer Reviewed: Y
Page Count: 14
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Predictor Variables, Higher Education, Online Courses, Correlation, Academic Achievement, Learning Analytics, Student Behavior, Artificial Intelligence, Algorithms, Holistic Approach, Competency Based Education, Learning Activities
DOI: 10.1109/TLT.2023.3291310
ISSN: 1939-1382
Abstract: Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand student participation, especially its relationship to student backgrounds, activity profiles, and academic results. With the fast advancement of educational technologies and data mining, data analytics (DA) in formal learning and online education has been widely explored. However, there has been little work on student behavior in GCDA. To fill this gap and to provide new contributions, we conduct a comprehensive study to investigate the interrelationship of GCDA participation and academic performance before and after higher education with significant and representative data (over 10 000 records) across three years. Hypotheses are formulated and validated, and the findings are triangulated with machine learning (ML) and DA. With supervised learning, the predictors of academic performance and GCDA participation are formulated, and the features to enhance predictions are analyzed. We develop predictors using novel approaches of genetic algorithms and Stacking in ML. The impacts of the breadth and depth of involvement are also studied. Results indicate that involvement in GCDA positively impacts student academic results. Our novel approaches give improvements in predicting student participation. Our holistic studies covering hypothesis validation, data analysis, and ML provide valuable insights into GCDA development.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1396454
Database: ERIC
FullText Text:
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  Data: Analytic Study for Predictor Development on Student Participation in Generic Competence Development Activities Based on Academic Performance
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22So%2C+Joseph+Chi-Ho%22">So, Joseph Chi-Ho</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8784-3083">0000-0001-8784-3083</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ho%2C+Yik+Him%22">Ho, Yik Him</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9774-2294">0000-0001-9774-2294</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wong%2C+Adam+Ka-Lok%22">Wong, Adam Ka-Lok</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7288-0199">0000-0001-7288-0199</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chan%2C+Henry+C%2E+B%2E%22">Chan, Henry C. B.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8024-0597">0000-0001-8024-0597</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tsang%2C+Kia+Ho-Yin%22">Tsang, Kia Ho-Yin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2513-7102">0000-0003-2513-7102</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chan%2C+Ada+Pui-Ling%22">Chan, Ada Pui-Ling</searchLink><br /><searchLink fieldCode="AR" term="%22Wong%2C+Simon+Chi-Wang%22">Wong, Simon Chi-Wang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3408-9747">0000-0003-3408-9747</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22IEEE+Transactions+on+Learning+Technologies%22"><i>IEEE Transactions on Learning Technologies</i></searchLink>. 2023 16(5):790-803.
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  Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
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  Data: 14
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  Data: <searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Holistic+Approach%22">Holistic Approach</searchLink><br /><searchLink fieldCode="DE" term="%22Competency+Based+Education%22">Competency Based Education</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Activities%22">Learning Activities</searchLink>
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  Data: 10.1109/TLT.2023.3291310
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  Data: 1939-1382
– Name: Abstract
  Label: Abstract
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  Data: Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand student participation, especially its relationship to student backgrounds, activity profiles, and academic results. With the fast advancement of educational technologies and data mining, data analytics (DA) in formal learning and online education has been widely explored. However, there has been little work on student behavior in GCDA. To fill this gap and to provide new contributions, we conduct a comprehensive study to investigate the interrelationship of GCDA participation and academic performance before and after higher education with significant and representative data (over 10 000 records) across three years. Hypotheses are formulated and validated, and the findings are triangulated with machine learning (ML) and DA. With supervised learning, the predictors of academic performance and GCDA participation are formulated, and the features to enhance predictions are analyzed. We develop predictors using novel approaches of genetic algorithms and Stacking in ML. The impacts of the breadth and depth of involvement are also studied. Results indicate that involvement in GCDA positively impacts student academic results. Our novel approaches give improvements in predicting student participation. Our holistic studies covering hypothesis validation, data analysis, and ML provide valuable insights into GCDA development.
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        Value: 10.1109/TLT.2023.3291310
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      – Text: English
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    Subjects:
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Higher Education
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      – SubjectFull: Online Courses
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      – SubjectFull: Algorithms
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      – SubjectFull: Competency Based Education
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      – SubjectFull: Learning Activities
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