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A technical infrastructure for primary education data that contributes to data standardization
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
2024 (English)In: Education and Information Technologies: Official Journal of the IFIP technical committee on Education, ISSN 1360-2357, E-ISSN 1573-7608, Vol. 29, p. 21045-21061Article in journal (Refereed) Published
Abstract [en]

There is a significant amount of data available about students and their learning activities in many educational systems today. However, these datasets are frequently spread across several different digital services, making it challenging to use them strategically. In addition, there are no established standards for collecting, processing, analyzing, and presenting such data. As a result, school leaders, teachers, and students do not capitalize on the possibility of making decisions based on data. This is a serious barrier to the improvement of work in schools, teacher and student progress, and the development of effective Educational Technology (EdTech) products and services. Data standards can be used as a protocol on how different IT systems communicate with each other. When working with data from different public and private institutions simultaneously (e.g., different municipalities and EdTech companies), having a trustworthy data pipeline for retrieving the data and storing it in a secure warehouse is critical. In this study, we propose a technical solution containing a data pipeline by employing a secure warehouse—the Swedish University Computer Network (SUNET), which is an interface for information exchange between operational processes in schools. We conducted a user study in collaboration with four municipalities and four EdTech companies based in Sweden. Our proposal involves introducing a data standard to facilitate the integration of educational data from diverse resources in our SUNET drive. To accomplish this, we created customized scripts for each stakeholder, tailored to their specific data formats, with the aim of merging the students’ data. The results of the first four steps show that our solution works. Once the results of the next three steps are in, we will contemplate scaling up our technical solution nationwide. With the implementation of the suggested data standard and the utilization of the proposed technical solution, diverse stakeholders can benefit from improved management, transportation, analysis, and visualization of educational data.

Place, publisher, year, edition, pages
Springer, 2024. Vol. 29, p. 21045-21061
Keywords [en]
Data standard, Data pipeline, Secure data pipeline, Educational data, Primary education, Technical infrastructure, SUNET drive
National Category
Information Systems, Social aspects
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-129073DOI: 10.1007/s10639-024-12683-2ISI: 001208963600002Scopus ID: 2-s2.0-85191712850OAI: oai:DiVA.org:lnu-129073DiVA, id: diva2:1854834
Funder
Linnaeus UniversityAvailable from: 2024-04-28 Created: 2024-04-28 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 Messias

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