In this theory paper, we integrate literature from different fields. We argue that efforts to expand engineering education research through data analytics need to be grounded in the established literature and understanding of student development. We discuss the opportunities and challenges associated with using data analytics to examine engineering students' experiences and outcomes. We suggest that engineering schools should enhance data infrastructure, along with data governance policies, to foster a culture of collaboration among units and divisions, and better utilize existing student data sources through greater data integration. We also suggest that engineering education researchers equip themselves with knowledge on data science, in addition to knowledge about different types of student experiences, and actively explore a wider range of data sources for research. Thereby, we envision a new research landscape with expanded data sources, integrated data systems, and new analytical techniques to enable predictive analysis and more actionable findings. © American Society for Engineering Education 2020.
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