Education, Learning Analytics, Learning Health Systems
Data Mining, Data Munging, Data Quality, Data Visualization, Machine Learning, Predictive Modeling, Psychometrics, Statistical Inference, Statistical Modeling, Statistics
Relevant Projects:

NSF, IES, Microsoft Education, Amazon Web Services


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Andrew Krumm

Assistant Professor

Michigan Medicine, Learning Health Sciences

My research examines the ways in which individuals and organizations use data to improve. Quality improvement and data-intensive research approaches are central to my work along with forming equitable collaborations between researchers and frontline workers. Prior to joining the Department of Learning Health Sciences, I was the Director of Learning Analytics Research at Digital Promise and a Senior Education Researcher in the Center for Technology in Learning at SRI International. At both organizations, I developed data-intensive research-practice partnerships with educational organizations of all types. As a learning scientist working at the intersection of data-intensive research and quality improvement, my colleagues and I have developed tools and strategies (e.g., cloud-based, open source tools for engaging in collaborative exploratory data analyses) that partnerships between researchers and practitioners can use to measure learning and improve learning environments.

This is an image that my colleagues and I, over multiple projects, developed to communicate the multiple steps involved in collaborative data-intensive improvement. The “organize” and “understand” phases are about asking the right questions before the work of data analysis begins: “co-develop” and “test” are about taking action following an analysis. Along with identifying common phases, we have also observed the importance of the following supporting conditions: a trusting partnership, the use of formal improvement methods, common data workflows, and intentional efforts to support the learning of everyone involved in the project.