His work is focused on the analysis of ecologically valid and “big” data (e.g. process data, open-ended assessment responses) to improve the education opportunities provided to all students, especially those from disadvantaged backgrounds. He collaborates with stakeholders to implement contemporary AI/ML data science methods into operational programs, expand access and usability of data, and support the creation of talent development programs to help expand the use of these programs in the field. Open science principles and practices inform much of his work.

An education researcher by training, John holds a Doctorate in Educational Leadership from UC Davis, a Master’s Degree in Sociocultural Anthropology from UC Davis, and a Bachelor’s Degree in Philosophy & Honors Studies from Central Connecticut State University. He is Co-Chair of the AI in Measurement Subcommittee of the National Council of Measurement in Education.