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.]