Practical Approaches to Advance K-12 Data Literacy

Susan Meabh Kelly
Highly Qualified Teacher (NY and CT): Earth Science, 7-12 and Physics, 7-12
Graduate Student, PhD in Curriculum and Instruction, University of Connecticut

In October 2015, a panel of big data analysts and educators convened in order to articulate the types of activities data-literate people engage in and envision how this might be taught in K-16 classrooms. By the end of the thought-provoking three-day workshop sponsored by IBM and Education Development Center’s Oceans of Data Institute (ODI), all panelists signed a call to action to position data literacy at the core of education, “integrated throughout K-16 education nation-wide and around the world,” advocated for fostering “curiosity, skepticism, and persistence in the classroom,” and collectively defined data literacy:

The data-literate individual understands, explains, and documents the utility and limitations of data by becoming a critical consumer of data, controlling his/her personal data trail, finding meaning in data, and taking action based on data. The data-literate individual can identify, collect, evaluate, analyze, interpret, present, and protect data. (ODI, 2016)

I will use the definition and vision from this 2015 workshop as reference as I outline suggested research priorities and next steps to advance data literacy. I will orient suggestions around K-12 science education to align with my expertise and experience in this arena. This includes nearly two decades of experience as a secondary public school Earth science, physics, and science research teacher, participation and partnerships associated with agency-sponsored efforts to support K-12 use of archived and real-time data, and geoscience research at NASA and NOAA laboratories. These suggestions will privilege pathways to inform practical and broad integration of data literacy education in K-12 settings, as I value supportive working conditions for K-12 teachers and equitable access to national STEM education reform innovations and initiatives.

Clarity of Terms and Goals
Science teachers witness a parade of reform movements over the course of their careers, many of which originate outside of the school community. Terms and goals that are unclear can foster confusion and impede realization of classroom integration. As one example, in recent decades scientific inquiry has been recognized as a critical pathway to science literacy, yet despite publication of several voluminous and informative documents (American Association for the Advancement of Science, 1993; National Research Council, 1996, National Research Council, 2000), has been plagued by confusion within the K-12 science education community (Settlage, 2003; Barrow, 2006). Overlapping and parallel STEM initiatives can further complicate science teachers’ reception and understanding of new reform efforts. In order to help deflate confusion about data literacy, it would be helpful to develop and publish a freely available resource to facilitate understanding as to how data literacy compares with other initiatives K-12 teachers may have previously encountered (e.g. computational [National Research Council, 2010; National Research Council, 2011] and critical thinking [Tama, 1989]; statistics [Schield, 2019], computer science [K-12 Computer Science Framework, 2016], and quantitative [Steen (Ed.), 2001] literacies). Since most science teachers do not have full access to research literature, nor time to thoughtfully discern nuances of simultaneous initiatives, a matrix may serve as a valuable foundational reference from which to collectively advance. The results from the data literacy skills survey may serve as a valuable springboard for matrix development (ODI, 2016).

K-12 Context
Science teachers are increasingly pressed to give account of progress on a variety of school, district, state, and national reform efforts – some of which is measured by tests (National Research Council, 1999; National Research Council, 2011) or require annual documentation (English & Lachlan-Hache, 2015). Most recently, many states have adopted or adapted common science standards – the Next Generation Science Standards (National Research Council, 2013; National Science Teaching Association, 2020). In response to accountability mandates, science assessments have already been developed, administered, and reported in many states – despite paucity of NGSS-congruent instructional resources (Achieve, Inc., n.d.; Smith, 2020). This status is further challenged by the significantly greater amount of time (hours each week) allotted for teaching in U.S. teachers’ work week schedules compared to their international counterparts (Darling-Hammond & Burns, 2017). While review of proposals to NSF DRK-12 do not require -- nor privilege -- activities that are designed to support state science standards, choosing to orient science data literacy activities within the challenging confines within which teachers must function may increase the likelihood that activities will be embraced and used.  In this way, research activities can – by design -- amplify the requisite broader impact component (NSF, 2020), while advancing data literacy in the United States. Comparing data literacy skills survey results (ODI, 2016) with the Next Generation Science Standards can help identify fertile areas of convergence from which the data literacy initiative may more easily germinate and grow. This overlap may be communicated through a diagram, such as the one that had been developed and popularly shared to highlight commonalities and differences between math, English language arts and the then new national science standards (Stage et al., 2013).

Practice of Data Science
Mathematics and computational thinking – skills that had been identified as critical to data literacy (ODI, 2016) – are recognized as one of the eight NGSS practices, but are largely absent from current NGSS-congruent exemplar activities (Achieve, Inc., n.d.; NGSS Lead States, 2013). As mathematics and computation as practiced publicly unfolded as the 2020 global pandemic emerged, education researchers with NGSS expertise have come to appreciate the potential of classroom integration of authentic science research -- research that leverages mathematical and computational skills to make sense of and respond to pressing societal issues (Lee & Campbell, 2020). This is a divergence from what the science education research community has popularly produced and encouraged -- activities that center on settled and familiar scientific phenomena (e.g. static cling in dryers, production of sound, sudden collapse of metal cylinders) (Achieve, Inc., n.d.; NGSX, 2020). Since not all education researchers have contemporary experience applying mathematics and computation in science lab or field research, those looking to advance K-12 science data literacy can help bridge this gap by recruiting team members outside the education research community. Practicing scientists, graduate students, as well as teachers who have engaged in research as part of graduate STEM programs and/or federal STEM database-centered investments (e.g. Jacoby, 1998; NASA, 2020) are well-positioned to contribute insight about software, publicly available data, methods, and emerging areas of research. Through establishing professionally diverse partnerships and networks, such as those initiated during the 2017 American Geophysical Union meeting (Kelly & Thompson, 2018; science + education collaboratory, 2020) and by National Public Radio (Garcia, 2020), pathways for K-12 students to develop data literacy skills as they are meaningfully and actually practiced may be more easily recognized and advanced.

Equity: Data Literacy for All
In order to engage students from many communities, it would be valuable for teachers to have access to data literacy activities that reflect a variety of authentic contexts (Potvin & Hasni, 2014). Opportunity to solicit or gauge students’ interests in advance can help identify topics that are meaningful and compelling to students. Establishing a partnership with science teachers can also help education researchers identify potential pathways to engage students. Finally, through leveraging open source software and public data, education researchers can support broader, sustainable, and more equitable access to data literacy activities.


 

References

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American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York, NY: Oxford University Press.

Barrow, L. H. (2006). A brief history of inquiry: From Dewey to standards. Journal of Science Teacher Education, 17, 265-278.

Darling-Hammond, L. & Burns, D. (2017). Empowered educators study findings [Meeting Presentation]. National Center for Education and the Economy Empowered Educators National Meeting, Washington, D.C. Retrieved from: http://www.tvworldwide.com/events/ncee/170606/default.cfm?id=16952&type=flv&test=0&live=0      

English, D. & Lachlan-Hache, L. (2015). Uncommon measures: Using teacher portfolios in educator evaluation. Washington, D.C.: American Institutes for Research. Retrieved from: https://www.air.org/sites/default/files/Uncommon-Measures-Teacher-Portfolios-Nov-2015.pdf 

Garcia, X. (2020). Introducing: Science Friday Summer Institute. Science Friday. Retrieved from: https://www.sciencefriday.com/articles/science-friday-summer-institute-2020/

Jacoby, S. (1998). NOAO educational outreach. NOAO Newsletter, 53. Retrieved from: https://www.noao.edu/noao/noaonews/mar98/node5.html

Kelly, S. & Thompson, M. (2018, April). Science + Education Collaboratory: An innovative communication mechanism to amplify impact [Poster].  National Alliance for Broader Impact Summit, Providence, Rhode Island.

Lee, O. & Campbell, T. (2020). What science and STEM teachers can learn from COVID-19: Harnessing data science and computer science through the convergence of multiple STEM subjects. Journal of Science Teacher Education.

Merzdorf, J. (2019, May 19).  NASA education program fosters climate of discovery. NASA Goddard Institute for Space Sciences. Retrieved from: https://www.giss.nasa.gov/research/news/20190509/

NGSS Lead States. (2013). Next Generation Science Standards: For states, by states. Washington, D.C.: The National Academies Press.

NGSX. (2020). Becoming a next-gen science teacher. Next Generation Science Exemplar Program. Retrieved from: https://www.ngsx.org/programs

National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, D.C.: The National Academy Press.

National Research Council. (2011). Report of a Workshop of Pedagogical Aspects of Computational Thinking. Washington, D.C.: The National Academies Press.

Oceans of Data Institute (2016). Building global interest in data literacy: A dialogue. Waltham, MA: Education Development Center, Inc. Retrieved from: http://oceansofdata.org/sites/oceansofdata.org/files/ODI%20Data%20Literacy%20Report_0.pdf

Potvin, P., & Hasni, A. (2014). Interest, motivation and attitude towards science and technology at K-12 levels: A systematic review of 12 years of educational research. Studies in Science Education, 50(1), 85–129.

science + education collaboratory (2020). About. Retrieved from: https://www.sciencepluseducation.com/about

Schield, M. (2019).  Statistical literacy: A study of confounding [Conference Paper]. Joint Statistical Meetings, Denver, Colorado. Retrieved from: http://www.statlit.org/pdf/2019-Schield-ASA.pdf

Settlage, J. (2003, January). Inquiry’s allure and illusion: Why it remains just beyond our reach [Conference Paper]. Annual meeting of the National Association for Research in Science Teaching, Philadelphia, PA.

Smith, P.S. (2020). What does a national survey tell us about progress toward the vision of NGSS? Journal of Science Teacher Education, 31 (6), 601-609.

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Steen, L. A. (Ed.). (2001). Mathematics and democracy: The case for quantitative literacy. Report prepared by the National Council on Education and the Disciplines. Retrieved from: https://www.maa.org/sites/default/files/pdf/QL/MathAndDemocracy.pdf

Tama, M. C. (1989). Critical thinking: Promoting it in the classroom. ERIC Digest. Retrieved from: https://files.eric.ed.gov/fulltext/ED306554.pdf