Mining Smart Learning Analytics Data Using Ensemble Classifiers.
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| Title: | Mining Smart Learning Analytics Data Using Ensemble Classifiers. |
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| Authors: | Kausar, Samina1,2, Oyelere, Solomon Sunday3, Salal, Yass Khudheir4, Hussain, Sadiq5 sadiq@dibru.ac.in, Cifci, Mehmet Akif6, Hilcenko, Slavoljub7, Shahid Iqbal, Muhammad8, Zhu Wenhao1, Xu Huahu1 |
| Source: | International Journal of Emerging Technologies in Learning. 2020, Vol. 15 Issue 12, p81-102. 22p. |
| Subject Terms: | *Concept learning, *Psychology of students, *Data analysis, *Comprehension, Smart materials |
| Abstract: | Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse that reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively, and contentedly. Smart Learning (SL), despite not being a new concept describing learning methods in the digital agehas caught the attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievement. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, SLA dataset was explored, and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) 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.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 144277140 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mining Smart Learning Analytics Data Using Ensemble Classifiers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kausar%2C+Samina%22">Kausar, Samina</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Oyelere%2C+Solomon+Sunday%22">Oyelere, Solomon Sunday</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Salal%2C+Yass+Khudheir%22">Salal, Yass Khudheir</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Hussain%2C+Sadiq%22">Hussain, Sadiq</searchLink><relatesTo>5</relatesTo><i> sadiq@dibru.ac.in</i><br /><searchLink fieldCode="AR" term="%22Cifci%2C+Mehmet+Akif%22">Cifci, Mehmet Akif</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Hilcenko%2C+Slavoljub%22">Hilcenko, Slavoljub</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Shahid+Iqbal%2C+Muhammad%22">Shahid Iqbal, Muhammad</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhu+Wenhao%22">Zhu Wenhao</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xu+Huahu%22">Xu Huahu</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2020, Vol. 15 Issue 12, p81-102. 22p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Concept+learning%22">Concept learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Comprehension%22">Comprehension</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+materials%22">Smart materials</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse that reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively, and contentedly. Smart Learning (SL), despite not being a new concept describing learning methods in the digital agehas caught the attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievement. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, SLA dataset was explored, and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) 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: 10.3991/ijet.v15i12.13455 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 81 Subjects: – SubjectFull: Concept learning Type: general – SubjectFull: Psychology of students Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Comprehension Type: general – SubjectFull: Smart materials Type: general Titles: – TitleFull: Mining Smart Learning Analytics Data Using Ensemble Classifiers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kausar, Samina – PersonEntity: Name: NameFull: Oyelere, Solomon Sunday – PersonEntity: Name: NameFull: Salal, Yass Khudheir – PersonEntity: Name: NameFull: Hussain, Sadiq – PersonEntity: Name: NameFull: Cifci, Mehmet Akif – PersonEntity: Name: NameFull: Hilcenko, Slavoljub – PersonEntity: Name: NameFull: Shahid Iqbal, Muhammad – PersonEntity: Name: NameFull: Zhu Wenhao – PersonEntity: Name: NameFull: Xu Huahu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 18630383 Numbering: – Type: volume Value: 15 – Type: issue Value: 12 Titles: – TitleFull: International Journal of Emerging Technologies in Learning Type: main |
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