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Use of Learning Analytics in K–12 Mathematics Education: Systematic Scoping Review of the Impact on Teaching and Learning
Linnaeus University, Faculty of Social Sciences, Department of Pedagogy.ORCID iD: 0000-0002-1914-1626
Linnaeus University, Faculty of Social Sciences, Department of Pedagogy.ORCID iD: 0000-0002-2924-4100
Linnaeus University, Faculty of Social Sciences, Department of Pedagogy.ORCID iD: 0000-0001-7525-6180
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-3297-0189
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2024 (English)In: Journal of Learning Analytics, E-ISSN 1929-7750, Vol. 11, no 3, p. 174-191Article in journal (Refereed) Published
Abstract [en]

The generation, use, and analysis of educational data comes with many promises and opportunities, especially where digital materials allow usage of learning analytics (LA) as a tool in data-based decision-making (DBDM). However, there are questions about the interplay between teachers, students, context, and technology. Therefore, this paper presents an exploratory systematic scoping review to investigate findings regarding LA usage in digital materials, teaching, and learning in K–12 mathematics education. In all, 3,654 records were identified, of which 19 studies met all the inclusion criteria. Results show that LA research in mathematics education is an emerging field where applications of LA are used in many contexts across many curricula content and standards of K–12 mathematics education, supporting a wide variety of teacher data use. Teaching with DBDM is mainly focused on supervision and guidance and LA usage had a generally positive effect on student learning with high-performing students benefiting most. We highlight a need for further research to develop knowledge of LA usage in classroom practice that considers both teacher and student perspectives in relation to design and affordances of digital learning systems. Finally, we propose a new class of LA, which we define as guiding analytics for learners, which harnesses the potential of LA for promoting achievement and independent learning.

Place, publisher, year, edition, pages
2024. Vol. 11, no 3, p. 174-191
Keywords [en]
K-12 education, learning analytics, data-based decision-making (DBDM), analytics for learners, teaching, learning, research paper
National Category
Educational Sciences
Research subject
Pedagogics and Educational Sciences; Pedagogics and Educational Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-134328DOI: 10.18608/jla.2024.8299Scopus ID: 2-s2.0-85213544970OAI: oai:DiVA.org:lnu-134328DiVA, id: diva2:1925168
Available from: 2025-01-08 Created: 2025-01-08 Last updated: 2025-01-14Bibliographically approved

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Rundquist, RebeckaHolmberg, KristinaRack, JohnMohseni, ZeynabMasiello, Italo

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