Academic Journal
A Novel Hybrid Multi-Criteria Decision and Data Mining Framework for Educational Intelligence Systems.
| Τίτλος: | A Novel Hybrid Multi-Criteria Decision and Data Mining Framework for Educational Intelligence Systems. |
|---|---|
| Συγγραφείς: | Octaria, Orissa, Manongga, Danny, Sembiring, Irwan |
| Πηγή: | Engineering, Technology & Applied Science Research; Jun2026, Vol. 16 Issue 3, p36608-36616, 9p |
| Θεματικοί όροι: | Analytic hierarchy process, Data mining, Classification, Student exchange programs, Clustering algorithms, Higher education, Learning analytics, Multiple criteria decision making |
| Περίληψη: | Although student exchange programs provide substantial advantages, a large number of university students are still unaware that such opportunities exist. This study addresses this gap by proposing a hybrid Educational Intelligence System (EIS), which is a data-driven decision-support framework that integrates Multi-Criteria Decision Analysis (MCDA) with data mining techniques. Specifically, the Analytical Hierarchy Process (AHP) is adopted as the MCDA method. Using AHP, the relative significance of each criterion influencing student awareness is determined. Concurrently, data mining methods, namely, clustering and classification are employed to reveal underlying patterns within student data. Clustering serves to categorize students according to their comprehension level of exchange programs, whereas Decision Tree-based classification pinpoints the dominant factors that shape student awareness. A total of 446 students from diverse higher education institutions participated in the study by completing a structured questionnaire. The clustering analysis reveals that 47.31% of respondents have a general familiarity with exchange programs yet lack detailed knowledge of specific requirements, whereas 30.94% exhibit an overall limited awareness. Based on these findings, promotional strategies for exchange programs should be differentiated according to students' academic progression. Moreover, the data-driven framework introduced in this study holds potential for broader application across various educational settings to strengthen the impact of academic initiatives. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: A Novel Hybrid Multi-Criteria Decision and Data Mining Framework for Educational Intelligence Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Octaria%2C+Orissa%22">Octaria, Orissa</searchLink><br /><searchLink fieldCode="AR" term="%22Manongga%2C+Danny%22">Manongga, Danny</searchLink><br /><searchLink fieldCode="AR" term="%22Sembiring%2C+Irwan%22">Sembiring, Irwan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Engineering, Technology & Applied Science Research; Jun2026, Vol. 16 Issue 3, p36608-36616, 9p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Analytic+hierarchy+process%22">Analytic hierarchy process</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Student+exchange+programs%22">Student exchange programs</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Although student exchange programs provide substantial advantages, a large number of university students are still unaware that such opportunities exist. This study addresses this gap by proposing a hybrid Educational Intelligence System (EIS), which is a data-driven decision-support framework that integrates Multi-Criteria Decision Analysis (MCDA) with data mining techniques. Specifically, the Analytical Hierarchy Process (AHP) is adopted as the MCDA method. Using AHP, the relative significance of each criterion influencing student awareness is determined. Concurrently, data mining methods, namely, clustering and classification are employed to reveal underlying patterns within student data. Clustering serves to categorize students according to their comprehension level of exchange programs, whereas Decision Tree-based classification pinpoints the dominant factors that shape student awareness. A total of 446 students from diverse higher education institutions participated in the study by completing a structured questionnaire. The clustering analysis reveals that 47.31% of respondents have a general familiarity with exchange programs yet lack detailed knowledge of specific requirements, whereas 30.94% exhibit an overall limited awareness. Based on these findings, promotional strategies for exchange programs should be differentiated according to students' academic progression. Moreover, the data-driven framework introduced in this study holds potential for broader application across various educational settings to strengthen the impact of academic initiatives. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.48084/etasr.18583 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 36608 Subjects: – SubjectFull: Analytic hierarchy process Type: general – SubjectFull: Data mining Type: general – SubjectFull: Classification Type: general – SubjectFull: Student exchange programs Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Higher education Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Multiple criteria decision making Type: general Titles: – TitleFull: A Novel Hybrid Multi-Criteria Decision and Data Mining Framework for Educational Intelligence Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Octaria, Orissa – PersonEntity: Name: NameFull: Manongga, Danny – PersonEntity: Name: NameFull: Sembiring, Irwan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22414487 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: Engineering, Technology & Applied Science Research Type: main |
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