%0 Journal Article
%T Research to classrooms: a co-designed curriculum brings All of Us data to secondary schools.
%A Stark LA
%A Fenker KE
%A Krishnan H
%A Malone M
%A Peterson RJ
%A Cowan R
%A Ensrud J
%A Gamboa H
%A Gayed C
%A Refino P
%A Tolk T
%A Walters T
%A Crosby Y
%A Baskir R
%J J Am Med Inform Assoc
%V 0
%N 0
%D 2024 Jul 9
%M 38981117
%F 7.942
%R 10.1093/jamia/ocae167
%X OBJECTIVE: We describe new curriculum materials for engaging secondary school students in exploring the "big data" in the NIH All of Us Research Program's Public Data Browser and the co-design processes used to collaboratively develop the materials. We also describe the methods used to develop and validate assessment items for studying the efficacy of the materials for student learning as well as preliminary findings from these studies.
METHODS: Secondary-level biology teachers from across the United States participated in a 2.5-day Co-design Summer Institute. After learning about the All of Us Research Program and its Data Browser, they collaboratively developed learning objectives and initial ideas for learning experiences related to exploring the Data Browser and big data. The Genetic Science Learning Center team at the University of Utah further developed the educators' ideas. Additional teachers and their students participated in classroom pilot studies to validate a 22-item instrument that assesses students' knowledge. Educators completed surveys about the materials and their experiences.
RESULTS: The "Exploring Big Data with the All of Us Data Browser" curriculum module includes 3 data exploration guides that engage students in using the Data Browser, 3 related multimedia pieces, and teacher support materials. Pilot testing showed substantial growth in students' understanding of key big data concepts and research applications.
CONCLUSIONS: Our co-design process provides a model for educator engagement. The new curriculum module serves as a model for introducing secondary students to big data and precision medicine research by exploring diverse real-world datasets.