In 2017, the National Assessment of Educational Progress (NAEP) began its official transition to Digitally Based Assessment (DBA) format. The use of DBAs has enabled the recording of students’ interaction with assessment items (e.g., time on task, number of visits, response changes, interactions with graphics or interactive components), as well as with the test interface (use of support functions like drawing, etc.). Course participants will learn how to analyze NAEP Process Data using a process mining framework to understand students’ processes during the assessment. The course is designed for participants interested in using process data. The course is aimed at those with novice to advanced experience working with process data and a solid understanding of coding in R. Attendees will learn about NAEP assessment features, data manipulation and cleaning, sequence formation, which kinds of research questions can be addressed, and the analytic methods used in process mining approaches, specifically a) sequence clustering methods, b) business process mining algorithms, and c) natural language processing approaches.

Instructors:

Emmanuel Sikali, U.S Department of Education, National Center for Education Statistics

Ruhan Circi, American Institutes for Research

Juanita Hicks, American Institutes for Research

Burhan Ogut, American Institutes for Research