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Visual Learning Analytics for Educational Interventions in Primary and Secondary Schools: A Scoping Review
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-3297-0189
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Linnaeus University, Linnaeus Knowledge Environments, Digital Transformations.ORCID iD: 0000-0002-3738-7945
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-2901-935X
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0001-7313-1720
2024 (English)In: Journal of Learning Analytics, ISSN 1929-7750, Vol. 11, no 2, p. 91-111Article in journal (Refereed) Published
Abstract [en]

Visual Learning Analytics (VLA) uses analytics to monitor and assess educational data by combining visual and automated analysis to provide educational explanations. Such tools could aid teachers in primary and secondary schools in making pedagogical decisions, however, the evidence of their effectiveness and benefits is still limited. With this scoping review, we provide a comprehensive overview of related research on proposed VLA methods, as well as identifying any gaps in the literature that could assist in describing new and helpful directions to the field. This review searched all relevant articles in five accessible databases — Scopus, Web of Science, ERIC, ACM, and IEEE Xplore — using 40 keywords. These studies were mapped, categorized, and summarized based on their objectives, the collected data, the intervention approaches employed, and the results obtained. The results determined what affordances the VLA tools allowed, what kind of visualizations were used to inform teachers and students, and, more importantly, positive evidence of educational interventions. We conclude that there are moderate-to-clear learning improvements within the limit of the studies’ interventions to support the use of VLA tools. More systematic research is needed to determine whether any learning gains are translated into long-term improvements.

Place, publisher, year, edition, pages
Society for Learning Analytics Research (SoLAR) , 2024. Vol. 11, no 2, p. 91-111
Keywords [en]
Visual learning analytics, Learning analytics dashboard, Educational interventions, Primary school, Secondary school, Scoping review, Systematic review
National Category
Computer and Information Sciences Pedagogy
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-131233DOI: 10.18608/jla.2024.8309ISI: 001295934400006Scopus ID: 2-s2.0-85202576381OAI: oai:DiVA.org:lnu-131233DiVA, id: diva2:1880284
Available from: 2024-07-01 Created: 2024-07-01 Last updated: 2025-02-11Bibliographically approved
In thesis
1. Development of Visual Learning Analytic Tools to Explore Performance and Engagement of Students in Primary, Secondary, and Higher Education
Open this publication in new window or tab >>Development of Visual Learning Analytic Tools to Explore Performance and Engagement of Students in Primary, Secondary, and Higher Education
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Schools and educational institutions collect large amounts of data about students and their learning, including text, grades, quizzes, timestamps, and other activities. However, in primary and secondary education, this data is often dispersed across different digital platforms, lacking standardized methods for collection, processing, analysis, and presentation. These issues hinder teachers and students from making informed decisions or strategic and effective use of data. This presents a significant obstacle to progress in education and the effective development of Educational Technology (EdTech) products. Visual Learning Analytics (VLA) tools, also known as Learning Analytics Dashboards (LADs), are designed to visualize student data to support pedagogical decision-making. Despite their potential, the effectiveness of these tools remains limited. Addressing these challenges requires both technical solutions and thoughtful design considerations, as explored in Papers 1 through 5 of this thesis. Paper 1 examines the design aspects of VLA tools by evaluating higher education data and various visualization and Machine Learning (ML) techniques. Paper 2 provides broader insights into the VLA landscape through a systematic review, mapping key concepts and research gaps in VLA and emphasizing the potential of VLA tools to enhance pedagogical decisions and learning outcomes. Meanwhile, Paper 3 delves into a technical solution (data pipeline and data standard) considering a secure Swedish warehouse, SUNET. This includes a data standard for integrating educational data into SUNET, along with customized scripts to reformat, merge, and hash multiple student datasets. Papers 4 and 5 focus on design aspects, with Paper 4 discussing the proposed Human-Centered Design (HCD) approach involving teachers in co-designing a simple VLA tool. Paper 5 introduces a scenario-based framework for Multiple Learning Analytics Dashboards (MLADs) development, stressing user engagement for tailored LADs that facilitate informed decision-making in education. The dissertation offers a comprehensive approach to advancing VLA tools, integrating technical solutions with user-centric design principles. By addressing data integration challenges and involving users in tool development, these efforts aim to empower teachers in leveraging educational data for improved teaching and learning experiences.

Place, publisher, year, edition, pages
Växjö, Sweden: Linnaeus University Press, 2024. p. 98
Series
Linnaeus University Dissertations ; 532
Keywords
Visual learning analytics tool, learning analytics dashboard, systematic review, data standard, data management, human-centered design, scenario-based framework, intervention, educational data, primary education, higher education
National Category
Computer and Information Sciences Computer Sciences Human Computer Interaction
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Information and software visualization; Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-131834 (URN)10.15626/LUD.532.2024 (DOI)9789180821766 (ISBN)9789180821773 (ISBN)
Public defence
2024-09-13, Weber, Hus K, Växjö, 10:00 (English)
Opponent
Supervisors
Available from: 2024-08-20 Created: 2024-08-15 Last updated: 2024-08-21Bibliographically approved

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Mohseni, ZeynabMasiello, ItaloMartins, Rafael MessiasNordmark, Susanna

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