In addition to quantitative accuracy, it is critical for learning analytics to consider design principles and methods of persuasion that convince educational leaders and students to do things differently. This requires a complementary approach that includes quantitative techniques, qualitative analysis, narrative, and design thinking.
John Whitmer is a Senior Fellow to the Institute for Education Statistics (IES), the largest federal education research agency in the United States. In this position he collaborates with IES stakeholders to implement data science into operational programs, expand access and usability of IES data, and support creation of an ongoing data science fellowship program.
Prior to this role, John led teams of data scientists, research scientists, and machine learning engineers in large educational assessment companies (ACTNext), edTech providers (Blackboard), and educational institutions (California State University & California Community Colleges). These teams worked on a variety of foundational research and production projects, such as automated writing assessment using natural language processing, predictive analytics using clickstream and background data, adaptive testing platforms, and business intelligence applications.
An educational researcher by training, John approaches these projects with a commitment to improving the lives of underserved and marginalized students. John holds a Doctorate in Educational Leadership from UC Davis and a Master’s Degree in Sociocultural Anthropology from UC Davis. He lives in Davis, California and enjoys hiking and farm-to-table cooking in his spare time. He can be reached at [email protected] and Twitter at @johncwhitmer.
Findings from two research studies at scale reveal the implications of learning analytics for designing courses in learning management systems to enhance student success.
Everyone from government agencies to mainstream press are looking for improvements in areas that typically fall under the umbrella of “student success.