Third SIAM-NSF Workshop on Modeling across the Curriculum
Recognizing a national science, technology, engineering, and mathematics (STEM) workforce need in the areas of data science and modeling with data, the Society For Industrial and Applied Mathematics (SIAM) will conduct a two-day workshop pertaining to modeling across the curriculum. This will be the third SIAM workshop pertaining to modeling across the curriculum and will have a special focus on: (1) incorporating computational and statistical data models into undergraduate and K-12 curricula; (2) models related to Business-Industry-Government, including big data considerations, and (3) conducting education research to inform the STEM community regarding best and emerging practices for developing and implementing these curricular updates. The approximately forty (40) participants in the workshop will include experts in STEM education and social science research methods and computational and statistical data science practitioners as well as mathematical sciences modeling practitioners and department leaders. An outcome of the project will be to disseminate the knowledge that will be gained from the workshop to help improve student learning and success to foster a national goal of increasing the number of highly qualified students who will enter the United States STEM workforce.
Participants at the workshop will begin examining the following research questions: (a) Why should data science, data-enabled science, and modeling with data, including both statistical and computational data models, be incorporated into undergraduate and K-12 curricula, what should be included, and how should this be implemented?, and (b) In line with the first question, what should be the role of modeling and computation with respect to problems related to Business-Industry-Government? Underlying goals of the workshop are to: (i) use the expertise and diversity of participants to supplement and refine these research questions; (ii) develop detailed plans for curricula and professional development materials which will then be implemented in subsequent work of the participants; (iii) promote research activities to foster a better understanding of issues related to developing, implementing, and engaging students and faculty in data-based statistical and computational modeling; (iv) provide a professionally prepared, summary of conclusions document from the meeting; and (v) use the variety of mechanisms available to SIAM to disseminate the workshop findings and recommendations to the larger mathematics and statistics communities.