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Trends, challenges, and opportunities of Multiple-Representation in Science learning: a systematic literature review.

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Title: Trends, challenges, and opportunities of Multiple-Representation in Science learning: a systematic literature review.
Authors: DIANI, RAHMA1 rahmadiani@radenintan.ac.id, VIYANTI1, LENGKANA, DEWI1, JALMO, TRI1, DESTIANA, ALIYA2, SAREGAR, ANTOMI2, PUTRA, FREDI GANDA2
Source: Review of Science Mathematics & ICT Education. 2024, Vol. 18 Issue 1, p29-52. 24p.
Subject Terms: *Science education, *Curriculum planning, *Research personnel, *Educators, Evidence gaps
Abstract (English): The multiple-representation approach, central to this systematic literature review (SLR), aims to enhance the effectiveness of science learning. Conducted based on the PRISMA 2020 framework, this review analyzed 56 articles published between 2018 and 2022 from the Scopus and Web of Science (WoS) databases. The study uncovers a significant increase in the use of multiple representations to boost student understanding and engagement in science. Notably, it identifies specific challenges, such as integrating technology and pedagogical alignment, and opportunities including innovative educational tools and curriculum development. These findings bridge a critical research gap, offering valuable insights and a comprehensive guide for educators, researchers, and practitioners to meet the dynamic needs of evolving science education. [ABSTRACT FROM AUTHOR]
Abstract (French): Cette revue systématique de la littérature (SLR), centrée sur l'approche de représentation multiple, vise à renforcer l'efficacité de l'apprentissage des sciences. Réalisée selon le cadre PRISMA 2020, cette revue a analysé 56 articles publiés entre 2018 et 2022 issus des bases de données Scopus et Web of Science (WoS). L'étude révèle une augmentation significative de l'utilisation des représentations multiples pour améliorer la compréhension et l'engagement des étudiants en sciences. Elle identifie des défis spécifiques, tels que l'intégration de la technologie et l'alignement pédagogique, et des opportunités, incluant des outils éducatifs innovants et le développement de programmes d'études. Ces découvertes comblent une lacune importante dans la recherche, offrant des perspectives précieuses et un guide complet pour les éducateurs, les chercheurs et les praticiens afin de répondre aux besoins dynamiques de l'éducation scientifique en évolution. [ABSTRACT FROM AUTHOR]
Copyright of Review of Science Mathematics & ICT Education is the property of Library & Information Center 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
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  Data: Trends, challenges, and opportunities of Multiple-Representation in Science learning: a systematic literature review.
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  Data: <searchLink fieldCode="JN" term="%22Review+of+Science+Mathematics+%26+ICT+Education%22">Review of Science Mathematics & ICT Education</searchLink>. 2024, Vol. 18 Issue 1, p29-52. 24p.
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  Data: *<searchLink fieldCode="DE" term="%22Science+education%22">Science education</searchLink><br />*<searchLink fieldCode="DE" term="%22Curriculum+planning%22">Curriculum planning</searchLink><br />*<searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink><br />*<searchLink fieldCode="DE" term="%22Educators%22">Educators</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+gaps%22">Evidence gaps</searchLink>
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  Data: The multiple-representation approach, central to this systematic literature review (SLR), aims to enhance the effectiveness of science learning. Conducted based on the PRISMA 2020 framework, this review analyzed 56 articles published between 2018 and 2022 from the Scopus and Web of Science (WoS) databases. The study uncovers a significant increase in the use of multiple representations to boost student understanding and engagement in science. Notably, it identifies specific challenges, such as integrating technology and pedagogical alignment, and opportunities including innovative educational tools and curriculum development. These findings bridge a critical research gap, offering valuable insights and a comprehensive guide for educators, researchers, and practitioners to meet the dynamic needs of evolving science education. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (French)
  Group: Ab
  Data: Cette revue systématique de la littérature (SLR), centrée sur l'approche de représentation multiple, vise à renforcer l'efficacité de l'apprentissage des sciences. Réalisée selon le cadre PRISMA 2020, cette revue a analysé 56 articles publiés entre 2018 et 2022 issus des bases de données Scopus et Web of Science (WoS). L'étude révèle une augmentation significative de l'utilisation des représentations multiples pour améliorer la compréhension et l'engagement des étudiants en sciences. Elle identifie des défis spécifiques, tels que l'intégration de la technologie et l'alignement pédagogique, et des opportunités, incluant des outils éducatifs innovants et le développement de programmes d'études. Ces découvertes comblent une lacune importante dans la recherche, offrant des perspectives précieuses et un guide complet pour les éducateurs, les chercheurs et les praticiens afin de répondre aux besoins dynamiques de l'éducation scientifique en évolution. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Review of Science Mathematics & ICT Education is the property of Library & Information Center 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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