Predicting Online Learners' Performance through Ontologies: A Systematic Literature Review
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| Title: | Predicting Online Learners' Performance through Ontologies: A Systematic Literature Review |
|---|---|
| Language: | English |
| Authors: | Safa Ridha Albo Abdullah, Ahmed Al-Azawei |
| Source: | International Review of Research in Open and Distributed Learning. 2025 26(1):16-37. |
| Availability: | Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org |
| Peer Reviewed: | Y |
| Page Count: | 22 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Electronic Learning, Academic Achievement, Grade Prediction, Data Analysis, Models, Evaluation Methods, Foreign Countries |
| Geographic Terms: | Brazil, Greece, Egypt, Spain, Morocco |
| ISSN: | 1492-3831 |
| Abstract: | This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the existing methodologies and tools used to forecast students' performance. In addition, the environment for generating ontologies, as considered by academics in the field, is likewise identified. Based on the inclusion criteria and by adopting PRISMA as a research methodology, seven studies and two systematic reviews were selected. The findings reveal a scarcity of research devoted to ontologies in the prediction of learners' achievement. However, the research outcomes suggest that building an ontological model to harness machine-learning capabilities could help accurately predict students' academic performance. The results of this systematic review are useful for higher education institutes and curriculum planners. This is especially pertinent in online learning settings to avoid dropout or failure. Also highlighted in this study are numerous possible directions for future research. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1463389 |
| Database: | ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting Online Learners' Performance through Ontologies: A Systematic Literature Review – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Safa+Ridha+Albo+Abdullah%22">Safa Ridha Albo Abdullah</searchLink><br /><searchLink fieldCode="AR" term="%22Ahmed+Al-Azawei%22">Ahmed Al-Azawei</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Review+of+Research+in+Open+and+Distributed+Learning%22"><i>International Review of Research in Open and Distributed Learning</i></searchLink>. 2025 26(1):16-37. – Name: Avail Label: Availability Group: Avail Data: Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 22 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Information Analyses – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Prediction%22">Grade Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Brazil%22">Brazil</searchLink><br /><searchLink fieldCode="DE" term="%22Greece%22">Greece</searchLink><br /><searchLink fieldCode="DE" term="%22Egypt%22">Egypt</searchLink><br /><searchLink fieldCode="DE" term="%22Spain%22">Spain</searchLink><br /><searchLink fieldCode="DE" term="%22Morocco%22">Morocco</searchLink> – Name: ISSN Label: ISSN Group: ISSN Data: 1492-3831 – Name: Abstract Label: Abstract Group: Ab Data: This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the existing methodologies and tools used to forecast students' performance. In addition, the environment for generating ontologies, as considered by academics in the field, is likewise identified. Based on the inclusion criteria and by adopting PRISMA as a research methodology, seven studies and two systematic reviews were selected. The findings reveal a scarcity of research devoted to ontologies in the prediction of learners' achievement. However, the research outcomes suggest that building an ontological model to harness machine-learning capabilities could help accurately predict students' academic performance. The results of this systematic review are useful for higher education institutes and curriculum planners. This is especially pertinent in online learning settings to avoid dropout or failure. Also highlighted in this study are numerous possible directions for future research. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: EJ1463389 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1463389 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 16 Subjects: – SubjectFull: Electronic Learning Type: general – SubjectFull: Academic Achievement Type: general – SubjectFull: Grade Prediction Type: general – SubjectFull: Data Analysis Type: general – SubjectFull: Models Type: general – SubjectFull: Evaluation Methods Type: general – SubjectFull: Foreign Countries Type: general – SubjectFull: Brazil Type: general – SubjectFull: Greece Type: general – SubjectFull: Egypt Type: general – SubjectFull: Spain Type: general – SubjectFull: Morocco Type: general Titles: – TitleFull: Predicting Online Learners' Performance through Ontologies: A Systematic Literature Review Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Safa Ridha Albo Abdullah – PersonEntity: Name: NameFull: Ahmed Al-Azawei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-electronic Value: 1492-3831 Numbering: – Type: volume Value: 26 – Type: issue Value: 1 Titles: – TitleFull: International Review of Research in Open and Distributed Learning Type: main |
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