Researching Digital Learning Platforms: Foundations, Methods, and Policy Recommendations

A growing community of researchers, practitioners, and developers has been working to create a new approach to educational technology research—one that is grounded in the big data that platforms collect yet connects with theory, incorporates rigorous methodologies, and addresses pressing questions about responsible artificial intelligence (AI) in education. In this working paper, we observe that educational research regarding the use of technology has been accelerating rapidly, yet in most of this research, data from the platform is only used in a minimal sense—as a measure of usage or “dosage.” Advances in a smaller, less noticed subfield of educational research have been diving more deeply into the data that digital learning platforms (DLPs) collect. One area of advancement has been in connecting theory to DLP data streams; this paper gives examples of three types of theories that have been connected. A second area of advancement has been connecting rigorous methods to DLP data; this paper illustrates the range of methods that have been successfully employed. A third area of newer but important advancement is in using DLP data to study responsible use of AI. We conclude by recommending (a) bringing attention to the DLP-as-research-infrastructure movement as an important and growing subfield that is distinct from edtech research overall, (b) continuing to invite theorists and methodologists to this space so that DLP research can be theoretically-driven and methodologically-rigorous, and (c) engaging practitioners around problem definition so that this research field can produce the actionable insights that product developers, local and state education leaders, and educators really need.