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- Publisher Website: 10.1016/j.chb.2018.06.032
- Scopus: eid_2-s2.0-85079771111
- WOS: WOS:000523598100037
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Article: Predicting At-risk University Students in a Virtual Learning Environment via a Machine Learning Algorithm
Title | Predicting At-risk University Students in a Virtual Learning Environment via a Machine Learning Algorithm |
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Authors | |
Keywords | Academic performance At-risk students Event prediction Higher education Machine learning |
Issue Date | 2020 |
Publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/comphumbeh |
Citation | Computers in Human Behavior, 2020, v. 107, p. article no. 105584 How to Cite? |
Abstract | A university education is widely considered essential to social advancement. Ensuring students pass their courses and graduate on time have thus become issues of concern. This paper proposes a reduced training vector-based support vector machine (RTV-SVM) capable of predicting at-risk and marginal students. It also removes redundant training vectors to reduce the training time and support vectors. To examine the effectiveness of the proposed RTV-SVM, 32,593 university students on seven courses were chosen for performance evaluation. Analysis reveals that the RTV-SVM achieved a training vector reduction of at least 59.7% without altering the margin or accuracy of the classifier. Moreover, the results showed the proposed method to be capable of achieving overall accuracy of 92.2–93.8% and 91.3–93.5% in predicting at-risk and marginal students, respectively. |
Persistent Identifier | http://hdl.handle.net/10722/308253 |
ISSN | 2023 Impact Factor: 9.0 2023 SCImago Journal Rankings: 2.641 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chui, KT | - |
dc.contributor.author | Fung, DCL | - |
dc.contributor.author | Lytras, MD | - |
dc.contributor.author | Lam, TM | - |
dc.date.accessioned | 2021-11-12T13:44:39Z | - |
dc.date.available | 2021-11-12T13:44:39Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Computers in Human Behavior, 2020, v. 107, p. article no. 105584 | - |
dc.identifier.issn | 0747-5632 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308253 | - |
dc.description.abstract | A university education is widely considered essential to social advancement. Ensuring students pass their courses and graduate on time have thus become issues of concern. This paper proposes a reduced training vector-based support vector machine (RTV-SVM) capable of predicting at-risk and marginal students. It also removes redundant training vectors to reduce the training time and support vectors. To examine the effectiveness of the proposed RTV-SVM, 32,593 university students on seven courses were chosen for performance evaluation. Analysis reveals that the RTV-SVM achieved a training vector reduction of at least 59.7% without altering the margin or accuracy of the classifier. Moreover, the results showed the proposed method to be capable of achieving overall accuracy of 92.2–93.8% and 91.3–93.5% in predicting at-risk and marginal students, respectively. | - |
dc.language | eng | - |
dc.publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/comphumbeh | - |
dc.relation.ispartof | Computers in Human Behavior | - |
dc.subject | Academic performance | - |
dc.subject | At-risk students | - |
dc.subject | Event prediction | - |
dc.subject | Higher education | - |
dc.subject | Machine learning | - |
dc.title | Predicting At-risk University Students in a Virtual Learning Environment via a Machine Learning Algorithm | - |
dc.type | Article | - |
dc.identifier.email | Fung, DCL: clfung@hku.hk | - |
dc.identifier.authority | Fung, DCL=rp01655 | - |
dc.identifier.doi | 10.1016/j.chb.2018.06.032 | - |
dc.identifier.scopus | eid_2-s2.0-85079771111 | - |
dc.identifier.hkuros | 329851 | - |
dc.identifier.volume | 107 | - |
dc.identifier.spage | article no. 105584 | - |
dc.identifier.epage | article no. 105584 | - |
dc.identifier.isi | WOS:000523598100037 | - |
dc.publisher.place | United Kingdom | - |