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Supporting Theory Building in Design-Based Research through Large Scale Data-Based Models

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Title: Supporting Theory Building in Design-Based Research through Large Scale Data-Based Models
Language: English
Authors: Golnaz Arastoopour Irgens, Ibrahim Oluwajoba Adisa, Deepika Sistla, Tolulope Famaye, Cinamon Bailey, Atefeh Behboudi, Adenike Omalara Adefisayo
Source: International Educational Data Mining Society. 2024.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 8
Publication Date: 2024
Sponsoring Agency: National Science Foundation (NSF), EDU Core Research (ECR)
National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Contract Number: 2024965
2031175
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Elementary Education
Descriptors: Learning Theories, Learning Analytics, Electronic Learning, Elementary School Students, Models, Educational Research, Data Use
Abstract: Although the fields of educational data mining and learning analytics have grown significantly in terms of analytical sophistication and the breadth of applications, the impact on theory-building has been limited. To move these fields forward, studies should not only be driven by learning theories, but should also use analytics to in form and enrich theories. In this paper, we present an approach for integrating educational data mining models with design-based research approaches to promote theory-building that is informed by data-based models. This approach aligns theory, design of the learning environment, data collection, and analytic methods through iterations that focus on the refinement and improvement of these components. We provide an example from our own work: the design and development of a digital learning environment for elementary-school (ages 8 to 13) children to learn about artificial intelligence within sociopolitical contexts. The project is driven by a critical constructionist learning framework and uses epistemic network analysis as a tool for modeling learning. We conclude with how this approach can be reciprocally beneficial in that educational data miners can use their models to inform theory and learning scientists can augment their theory-building practices through big data models. [For the complete proceedings, see ED675485.]
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED675688
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Golnaz+Arastoopour+Irgens%22">Golnaz Arastoopour Irgens</searchLink><br /><searchLink fieldCode="AR" term="%22Ibrahim+Oluwajoba+Adisa%22">Ibrahim Oluwajoba Adisa</searchLink><br /><searchLink fieldCode="AR" term="%22Deepika+Sistla%22">Deepika Sistla</searchLink><br /><searchLink fieldCode="AR" term="%22Tolulope+Famaye%22">Tolulope Famaye</searchLink><br /><searchLink fieldCode="AR" term="%22Cinamon+Bailey%22">Cinamon Bailey</searchLink><br /><searchLink fieldCode="AR" term="%22Atefeh+Behboudi%22">Atefeh Behboudi</searchLink><br /><searchLink fieldCode="AR" term="%22Adenike+Omalara+Adefisayo%22">Adenike Omalara Adefisayo</searchLink>
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  Data: Although the fields of educational data mining and learning analytics have grown significantly in terms of analytical sophistication and the breadth of applications, the impact on theory-building has been limited. To move these fields forward, studies should not only be driven by learning theories, but should also use analytics to in form and enrich theories. In this paper, we present an approach for integrating educational data mining models with design-based research approaches to promote theory-building that is informed by data-based models. This approach aligns theory, design of the learning environment, data collection, and analytic methods through iterations that focus on the refinement and improvement of these components. We provide an example from our own work: the design and development of a digital learning environment for elementary-school (ages 8 to 13) children to learn about artificial intelligence within sociopolitical contexts. The project is driven by a critical constructionist learning framework and uses epistemic network analysis as a tool for modeling learning. We conclude with how this approach can be reciprocally beneficial in that educational data miners can use their models to inform theory and learning scientists can augment their theory-building practices through big data models. [For the complete proceedings, see ED675485.]
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      – SubjectFull: Learning Analytics
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      – SubjectFull: Electronic Learning
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      – SubjectFull: Data Use
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