; (3) providing students with opportunities to explore and analyze real data sets from contexts that are meaningful to the students, which may include— (A) missing or incorrect values; (B) quantities of data that require choice and use of appropriate technology; (C) multiple data sets that require choices about which data are relevant to the current problem; and (D) data of various types including quantities, words, and images; (4) taking a school or district-wide approach to professional development in mathematical modeling and statistical modeling; (5) engaging rural local agencies; (6) supporting research on effective mathematical modeling and statistical modeling teaching practices, including problem- and project-based learning, universal design for accessibility, and rubrics and mastery-based grading practices to assess student performance; (7) designing and developing pre-service and in-service training resources to assist educators in adopting transdisciplinary teaching practices within mathematics and statistics courses; (8) coordinating with local partners to adapt mathematics and statistics teaching practices to leverage local natural, business, industry, and community assets in order to support community-based learning; (9) providing hands-on training and research opportunities for mathematics and statistics educators at Federal laboratories, institutions of higher education, or in industry; (10) developing mechanisms for partnerships between educators and employers to help educators and students make connections between their mathematics and statistics projects and topics of relevance in today’s world; (11) designing and implementing professional development courses and experiences, including mentoring for educators, that combine face-to-face and online experiences; (12) reduce gaps in access to learning opportunities for students from groups historically underrepresented in STEM; (13) provide support and resources for students from groups historically underrepresented in STEM