Data science is ever-present in modern life. The need to learn with and about data science is becoming increasingly important in a world where the quantity of data is constantly growing, where one’s own data are often being harvested and marketed, where data science career opportunities are rapidly increasing, and where understanding statistics, data sources, and data representation is integral to understanding STEM and the world around us. Museums have the opportunity to play a critical role in introducing the public to data science concepts in ways that center personal relevance, social connections and collaborative learning. However, data science and statistics are difficult concepts to distill and provide meaningful engagement with during the brief learning experiences typical to science museums. This Pilot and Feasibility study brings together data scientists, data science educators, and museum exhibit designers to consider these questions:
What are the important data science concepts for the public to explore and understand in museum exhibits?
How can museum exhibits be designed to support visitors with diverse backgrounds and experiences to engage with these data science concepts?
What principles can shape these designs to promote broadening participation in data science specifically and STEM more broadly?
This Pilot and Feasibility project combines multidisciplinary expert convening, feasibility testing, and early exploratory prototyping around the focal topic of data science exhibits. Project partners, TERC, the Museum of Science, Boston, and The Tech Interactive in San Jose will engage in an iterative process to develop a theoretical grounding and practical guidance for museum practitioners. The project will include two convenings, bringing together teams of experts from the fields of data science, data science education and museum exhibit design. Prior to the first convening, an initial literature summary and a survey of convening participants will be conducted, culminating in a preliminary list of big ideas about data science. Periodically, participants will have the opportunity to rank, annotate and expand this list, as a form of ongoing data collection. During the convenings, participants will explore the preliminary list, share related work from the three disciplines, engage with related data science activities in small groups, and work together to build consensus around promising data science topics and approaches for exhibits. Participant evaluation will allow for iterative improvement of the convenings and the capture of missed points or overlooked topics. After each convening, museum partners will create prototypes that respond to the convening conversations. Prototypes will be pilot tested (evaluated) with an intentionally recruited group of families that includes both frequent visitors and those who are less likely to visit the museum; diversity in terms of race, languages and dis/ability will be reflected in selection. Pilot data collection will consist of structured observations and interviews. Results from the first round of prototyping will be shared with convening participants as a way to modify the list of big ideas and to further interrogate the feasibility of communicating these ideas in an exhibit format. Results from the convenings and from both rounds of prototyping will be combined in a guiding document that will be shared on all three partner websites, and more broadly with the informal STEM learning field. The team will also host a workshop for practitioners interested in designing data science exhibits, and present at a conference focused on museum exhibits and their design.
The goal of FLIP (Diversifying Future Leadership in the Professoriate), an NSF INCLUDES Design and Development Launch Pilot, is to address the broadening participation challenge of increasing the diversity of the future leadership in the professoriate in computing at research universities as a way to achieve diversity across the field. According to the 2016 CRA Taulbee Survey, only 4.3% of the tenure-track faculty at PhD-granting universities are from underrepresented minorities. This challenge is important to address because diverse faculty contributes to academia in the following critical ways: serve as excellent role models for a diverse study body, bring diverse backgrounds to the student programs and policies developed by the department, and bring diverse perspectives to the research projects and programs. Further, the focus is on research universities, because in practice, key national leadership roles, such as serving on national committees that impact thefield of computing, often come from research universities.
The shared purpose and broad vision of the FLIP launch pilot is to increase faculty diversity in computing at research universities by increasing the diversity of PhD graduates from the top producers of computing faculty. The focus is on four underrepresented groups in computing: African Americans; Hispanics; Native Americans and indigenous peoples; and Persons with Disabilities. The long-term goal is to pursue this vision through strategic partnerships with those institutions that are the top producers of computing faculty and organizations that focus on diverse students in STEM, as well as partnerships that collectively adopt proven strategies for recruiting, graduating, and preparing a diverse set of doctoral students for academic careers. The purpose of the pilot is to establish a unified approach across the different partners that will build upon proven strategies to develop novel practices for increasing the diversity of the PhD graduates from key institutions, thereby increasing the faculty diversity in computing at research universities. For the pilot, FLIP will focus on recruitment and admissions and professional development for current PhD students.
DATE:
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TEAM MEMBERS:
Valerie TaylorCharles IsbellJeffrey ForbesUniversity of Chicago