Adaptive Learning en contexte parascolaire : comprendre les usages et effets via l'analyse des traces d'un déploiement industriel.
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| Title: | Adaptive Learning en contexte parascolaire : comprendre les usages et effets via l'analyse des traces d'un déploiement industriel. |
|---|---|
| Alternate Title: | Adaptive Learning in Out-of-School Context : Understanding Uses and Effects by Analyzing the Traces of an Industrial Deployment. |
| Authors: | BADIER, Anaëlle1, LEFEVRE, Marie1, LEFORT, Mathieu1, GUIN, Nathalie1 |
| Source: | STICEF. 2024, Vol. 31 Issue 1, p1-36. 36p. |
| Subject Terms: | *Item response theory, *Mobile learning, *Educational resources, Trace analysis, Mobile apps |
| Abstract (English): | Our work takes place in a context of non-formal learning on mobile applications, where we have proposed a recommendation engine for educational resources, based on IRT (Item Response Theory) and a recommendation score with three components: pedagogical, historical and novelty. The learners concerned have a wide range of objectives and work methods. In this paper, after explaining how our recommendation engine works, we analyze the usage traces of over 8,000 learners over a 4-month period. We show that the recommendations meet different objectives and uses, that following them has a positive impact on the learning experience, and that analysis of these traces highlights levers for improvement in proposing a recommendation mechanism based on iterative design. [ABSTRACT FROM AUTHOR] |
| Abstract (French): | Nos travaux se situent dans un contexte d'apprentissage non-formel sur application mobile où nous avons proposé un moteur de recommandation de ressources pédagogiques, s'appuyant sur l'IRT (Item Response Theory) et sur un score de recommandation à trois composantes : pédagogique, historique et nouveauté. Les apprenants concernés ont des objectifs et modalités de travail très variés. Dans cet article, après avoir rappelé le fonctionnement de notre moteur de recommandations, nous analysons les traces d'utilisation de plus de 8 000 apprenants sur 4 mois. Nous montrons que les recommandations répondent à des objectifs et des usages différents, que leur suivi influe positivement sur l'expérience d'apprentissage et que l'analyse de ces traces permet de mettre en évidence des leviers d'amélioration pour proposer un mécanisme de recommandation par conception itérative. [ABSTRACT FROM AUTHOR] |
| Copyright of STICEF is the property of Sciences et Techniques de l'Information et de la Communication pour l'Education et la Formation 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: 183613194 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Learning en contexte parascolaire : comprendre les usages et effets via l'analyse des traces d'un déploiement industriel. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Adaptive Learning in Out-of-School Context : Understanding Uses and Effects by Analyzing the Traces of an Industrial Deployment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22BADIER%2C+Anaëlle%22">BADIER, Anaëlle</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LEFEVRE%2C+Marie%22">LEFEVRE, Marie</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LEFORT%2C+Mathieu%22">LEFORT, Mathieu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22GUIN%2C+Nathalie%22">GUIN, Nathalie</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22STICEF%22">STICEF</searchLink>. 2024, Vol. 31 Issue 1, p1-36. 36p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+resources%22">Educational resources</searchLink><br /><searchLink fieldCode="DE" term="%22Trace+analysis%22">Trace analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+apps%22">Mobile apps</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Our work takes place in a context of non-formal learning on mobile applications, where we have proposed a recommendation engine for educational resources, based on IRT (Item Response Theory) and a recommendation score with three components: pedagogical, historical and novelty. The learners concerned have a wide range of objectives and work methods. In this paper, after explaining how our recommendation engine works, we analyze the usage traces of over 8,000 learners over a 4-month period. We show that the recommendations meet different objectives and uses, that following them has a positive impact on the learning experience, and that analysis of these traces highlights levers for improvement in proposing a recommendation mechanism based on iterative design. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (French) Group: Ab Data: Nos travaux se situent dans un contexte d'apprentissage non-formel sur application mobile où nous avons proposé un moteur de recommandation de ressources pédagogiques, s'appuyant sur l'IRT (Item Response Theory) et sur un score de recommandation à trois composantes : pédagogique, historique et nouveauté. Les apprenants concernés ont des objectifs et modalités de travail très variés. Dans cet article, après avoir rappelé le fonctionnement de notre moteur de recommandations, nous analysons les traces d'utilisation de plus de 8 000 apprenants sur 4 mois. Nous montrons que les recommandations répondent à des objectifs et des usages différents, que leur suivi influe positivement sur l'expérience d'apprentissage et que l'analyse de ces traces permet de mettre en évidence des leviers d'amélioration pour proposer un mécanisme de recommandation par conception itérative. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of STICEF is the property of Sciences et Techniques de l'Information et de la Communication pour l'Education et la Formation 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: Languages: – Code: fre Text: French PhysicalDescription: Pagination: PageCount: 36 StartPage: 1 Subjects: – SubjectFull: Item response theory Type: general – SubjectFull: Mobile learning Type: general – SubjectFull: Educational resources Type: general – SubjectFull: Trace analysis Type: general – SubjectFull: Mobile apps Type: general Titles: – TitleFull: Adaptive Learning en contexte parascolaire : comprendre les usages et effets via l'analyse des traces d'un déploiement industriel. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: BADIER, Anaëlle – PersonEntity: Name: NameFull: LEFEVRE, Marie – PersonEntity: Name: NameFull: LEFORT, Mathieu – PersonEntity: Name: NameFull: GUIN, Nathalie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 19528302 Numbering: – Type: volume Value: 31 – Type: issue Value: 1 Titles: – TitleFull: STICEF Type: main |
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