IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT'S INTELLIGENCES.
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| Title: | IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT'S INTELLIGENCES. |
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| Authors: | RAFIQ, Jamal Eddine1 amal.rafiq-etu@etu.univh2c.ma, ZAKRANI, Abdelali1 abdelali.zakrani@univh2c.ma, AMRAOUY, Mohammed2 amraouy.mohamed1@gmail.com, NOUH, Said3 said.nouh@univh2m.ma, BENNANE, Abdellah2 abdellah.bennane@gmail.com |
| Source: | Turkish Online Journal of Distance Education (TOJDE). Apr2025, Vol. 26 Issue 2, p39-52. 14p. |
| Subject Terms: | *Individualized instruction, *Effective teaching, *Classroom environment, *Academic achievement, *Online education, Deep learning |
| Abstract: | The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aimes to predict learners' performance in online learning settings. This systemic model integrates cognitive, social, emotional, contextual, and normative aspects to predict the learners' performance in online learning environment. This model, based on Competency-Based Learning Traces, takes a holistic approach by integrating various data reflecting knowledge acquisition and skills development. By Taking into account the exchanges among the learners, as well as the interactions with their teachers and the complexity of their online learning environment, the model aims to provide accurate and informed predictions of academic performance. This study provides a detailed overview of the APPMLT-CBT model, its data collection methodology, and discusses its potential implications for online teaching. Results suggest that the model can serve as a robust framework for improving online teaching and learning while offering a deep understanding of the underlying mechanisms of online learning. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Online Journal of Distance Education (TOJDE) is the property of Turkish Online Journal of Distance Education 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: 184366924 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT'S INTELLIGENCES. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22RAFIQ%2C+Jamal+Eddine%22">RAFIQ, Jamal Eddine</searchLink><relatesTo>1</relatesTo><i> amal.rafiq-etu@etu.univh2c.ma</i><br /><searchLink fieldCode="AR" term="%22ZAKRANI%2C+Abdelali%22">ZAKRANI, Abdelali</searchLink><relatesTo>1</relatesTo><i> abdelali.zakrani@univh2c.ma</i><br /><searchLink fieldCode="AR" term="%22AMRAOUY%2C+Mohammed%22">AMRAOUY, Mohammed</searchLink><relatesTo>2</relatesTo><i> amraouy.mohamed1@gmail.com</i><br /><searchLink fieldCode="AR" term="%22NOUH%2C+Said%22">NOUH, Said</searchLink><relatesTo>3</relatesTo><i> said.nouh@univh2m.ma</i><br /><searchLink fieldCode="AR" term="%22BENNANE%2C+Abdellah%22">BENNANE, Abdellah</searchLink><relatesTo>2</relatesTo><i> abdellah.bennane@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Distance+Education+%28TOJDE%29%22">Turkish Online Journal of Distance Education (TOJDE)</searchLink>. Apr2025, Vol. 26 Issue 2, p39-52. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br />*<searchLink fieldCode="DE" term="%22Effective+teaching%22">Effective teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Classroom+environment%22">Classroom environment</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aimes to predict learners' performance in online learning settings. This systemic model integrates cognitive, social, emotional, contextual, and normative aspects to predict the learners' performance in online learning environment. This model, based on Competency-Based Learning Traces, takes a holistic approach by integrating various data reflecting knowledge acquisition and skills development. By Taking into account the exchanges among the learners, as well as the interactions with their teachers and the complexity of their online learning environment, the model aims to provide accurate and informed predictions of academic performance. This study provides a detailed overview of the APPMLT-CBT model, its data collection methodology, and discusses its potential implications for online teaching. Results suggest that the model can serve as a robust framework for improving online teaching and learning while offering a deep understanding of the underlying mechanisms of online learning. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Online Journal of Distance Education (TOJDE) is the property of Turkish Online Journal of Distance Education 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: 10.17718/tojde.1477677 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 39 Subjects: – SubjectFull: Individualized instruction Type: general – SubjectFull: Effective teaching Type: general – SubjectFull: Classroom environment Type: general – SubjectFull: Academic achievement Type: general – SubjectFull: Online education Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT'S INTELLIGENCES. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: RAFIQ, Jamal Eddine – PersonEntity: Name: NameFull: ZAKRANI, Abdelali – PersonEntity: Name: NameFull: AMRAOUY, Mohammed – PersonEntity: Name: NameFull: NOUH, Said – PersonEntity: Name: NameFull: BENNANE, Abdellah IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13026488 Numbering: – Type: volume Value: 26 – Type: issue Value: 2 Titles: – TitleFull: Turkish Online Journal of Distance Education (TOJDE) Type: main |
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