K-12 Data Tools: A Review

Pimentel, D. R., Horton, N. J., & Wilkerson, M. H. (2022). Tools to support data analysis and data science in k-12 education. Commissioned paper for the National Academy of Sciences, Engineering, and Medicine, Board on Science Education, Committee on Science Investigations and Engineering Design for Grades 6-12. Washington, DC. [PDF]

Together with Danny Pimentel (Stanford) and Nick Horton (Amherst), I has the opportunity to prepare this commissioned paper as part of the National Academies’ Foundations of Data Science in K-12 Workshop. See the abstract below; in addition to the report itself Nick has made a number of supplementary materials and resources available at this site.

Abstract. There has been a proliferation of tools for teaching data analysis and data science
at the middle and high school levels. While a few frameworks for systematically
exploring the affordances and constraints of such tools exist, most work has only
explored one or a few tools at once, or has not focused on K-12 usage. In this paper,
we blend first-hand comparative analysis methods and supplemental literature review
to conduct a systematic analysis of several common data analysis software packages
in use at the K12 level. Using an adaptation of a framework proposed by McNamara
(2019), we grouped the tools into related genres. Spreadsheets, while familiar and
accessible to many, lacked many desirable features. Visual tools (e.g., CODAP, Social
Explorer, iNZight) lower the barrier for data exploration, but may not easily support
more advanced statistical tests. Scripting tools (e.g., Python, Pyret, R) provide great
extensibility but with increased degree of difficulty. Looking across tools and genres,
our analysis suggests that these genres boast complementary strengths depending
on students’ developmental and investigative needs. We make recommendations for
the design and use of tools, notably highlighting the importance of working across
different tool types as a part of data practice.

Foundations of Data Science for K-12

I had the privilege to work with Nick Horton and an amazing Planning Committee on the recent Workshop exploring the Foundations of Data Science for Students in Grades K-12, funded by the Valhalla Foundation and organized by the National Academies for Science, Engineering, and Medicine. The event was held Sept 13-14 in Washington, D. C. and online. You can find the video playlist and meeting materials here. Some important themes that were discussed throughout the event was the importance of exploring context, bias, and the social narratives built into data; investigating what sorts of approaches to data ought to be integrated across the curriculum vs what is deserving of its own course or deeper trajectory of study; and how can the emerging field of Data Science learn from the mistakes of its disciplinary predecessors (and from scholarship exploring education more generally), in order to ensure a coherent, inclusive, and ethical data science education.

Learning from Interpretations of Innovation

Wilkerson, M. H., Shareff, R. L.*, & Laina, V.* (in press). Learning from “interpretations of innovation” in the co-design of digital tools. To appear in M-C. Shanahan, B. Kim, M. A. Takeuchi, K. Koh, A. P. Preciado-Babb, & P. Sengupta (Eds.), The Learning Sciences in Conversation: Theories, Methodologies, and Boundary Spaces. Routledge. [PDF][Routledge]

Learning Sciences researchers often design alongside the learners and other stakeholders they seek to support – involving them early and often in the conceptualization, development, and testing of learning environments (DiSalvo, 2016; Druin, 2002). This is done to preempt technical or pragmatic issues with design, to address problems of practice, and to build capacity for institutional change. However, designers often run into a more foundational issue: stakeholders hold different expectations about what types of learning a given design is meant to support (Könings et al., 2005; Könings e al., 2014; Wilkerson, 2017). These “interpretation(s) of innovation” (Fishman, 2014, p. 117) reveal different underlying goals and epistemologies held by designers, learners, and other stakeholders. In other words, they reveal which kinds of learning stakeholders expect or value, and whether those kinds of learning appear to be supported by the environment or not.

In this chapter, we argue that designers ought to (a) invite, attend to, and learn from different interpretations of designed innovations and (b) respond by expanding the designed environments to support more varied uses. We contend that this is especially needed when designed tools and environments are intended to introduce an audience to new or unfamiliar epistemic practices, such as those making use of digital tools.

Then, we describe two methodological approaches we have developed to engage in this type of collaborative design. The first, longitudinal tool interviews, involves conducting repeated task-based design interviews with learners over extended periods of time. These interviews invite active negotiation of what kinds of learning a digital tool should support. The second, backward conjecture mapping, engages stakeholders from diverse educational contexts with the same digital tool, in an effort to support a variety of applications. Both approaches provide opportunities for researchers to renegotiate their understanding of tool design, for learners and educators to experience new epistemological orientations and knowledge-building strategies, and for both parties to expand their conceptualizations of what is possible when digital tools and practices are introduced into formal learning environments.

Presentations at ISLS 2022

We’ll be sharing early results and designs from two projects at the upcoming Annual Meeting of the International Society for the Learning Sciences!

From the Writing Data Stories project (NSF IIS-1900606):

Reigh, E.^, Escudé, M.*, McBride, C.^, Wei, C.*, Bakal, M.*, Roberto, C.*, Rivero, E.*, Wilkerson, M. H., & Gutiérrez, K. (2022). Paths through Data: Successes and Future Directions in Supporting Student Reasoning about Environmental Racism. Short paper to appear in Proceedings of the 2022 Annual Meeting of the International Society for the Learning Sciences (ISLS 2022). ISLS: San Diego, CA.

Roberto, C.*, Wei, C.*, Rivero, E.*, & Wilkerson, M. H. (2022). Student Participation in Sociocritical Data Literacy: Shapes, Trends, and Future Directions From a Middle School Science Unit. Short paper to appear in Proceedings of the 2022 Annual Meeting of the International Society for the Learning Sciences (ISLS 2022). ISLS: San Diego, CA.

Wei, C.*, McBride, C.^, Bakal, M.*, Roberto, C.*, Bhargava, P., and Wilkerson, M. H. (2022). Storytelling with Data: A Syncretic Approach that Brings Together Social Justice with Middle School Science. In J. Polman, I. Tabak, & T. Tran (Orgs.), Cultivating Critical, Justice-Oriented Data Literacies in a Post-Truth World. Structured poster session to appear in Proceedings of the 2022 Annual Meeting of the International Society for the Learning Sciences (ISLS 2022). ISLS: San Diego, CA.

Wei, C.*, McBride, C.^, & Wilkerson, M. H. (2022). Storytelling with Data in the Third Space: leveraging students’ syncretic literacies for scientific investigation and social change. In C. Matuk, A. Amato, & I. Davidesco (Orgs.), Data Storytelling in the Classroom. Structured poster session to appear in Proceedings of the 2022 Annual Meeting of the International Society for the Learning Sciences (ISLS 2022). ISLS: San Diego, CA.

From the Access to Sustainability (A2S) project (NSF DRL-2010413):

Wagh, A., Fuhrmann, T., Eloy, A., Wolf, J., Bumbacher, E., Wilkerson, M. H., & Blikstein, P. (2022). Strategies towards Designing for Sustained Engagement in Computational Modeling in Science Classrooms. Poster to appear in Proceedings of the 2022 Annual Meeting of the International Society for the Learning Sciences (ISLS 2022). ISLS: San Diego, CA.

Eloy, A., Wolf, J., Wagh, A., Fuhrmann, T., Bumbacher, E., Wilkerson, M., & Blikstein, P. (2022). A2S: Designing an integrated platform for computational modeling & data analysis for sustained investigations in science classrooms. Interactive Workshop to appear in Proceedings of the 2022 Annual Meeting of the International Society for the Learning Sciences (ISLS 2022). ISLS: San Diego, CA.

Presentations at AERA 2022

Several presentations from the Writing Data Stories project, as well as other research projects exploring data science education and computing education in science classrooms, will be appearing at AERA 2022!

Wilkerson, M. H., Stokes, D., Lee, H. S., Reigh, E. V., Escudé, M. E., Rivero, E., & Gutiérrez, K. (accepted). A Framework for Exploring Self, Community, Histories, and Futures Through Data. In Miller, K., Yoon, S. (Chairs) & Rubin, A. (Discussant), Data Literacy in Context: Culturally Oriented and Place-Based Learning through Data. Symposium to be presented at AERA 2022, San Diego, CA.

Escudé, M. E., Reigh, E. V., Bakal, M., Rivero, E., Wilkerson, M. H., & Gutiérrez, K. (accepted). Developing Spatial-Making Repertoires Through Sociocritical Data Stories. In Lee, S. (Chair), Designing for Dignity Affirming Experiences: Leveraging Embodied Learning Towards Equity in Interaction. Symposium to be presented at AERA 2022, San Diego, CA.

Koyuncu, B., & Wilkerson, M. H. (accepted). Examining the Influence of Tool Selection on Curriculum Design for Data Science Education. Poster to be presented at AERA 2022, San Diego, CA.

Wagh, A., Fuhrmann, T., Eloy, W., Bumbacher, E, Wilkerson, M. H., & Blikstein, P. (accepted). Lessons from Co-designing with Science Teachers to Support Sustained Computational Modeling in Middle School Classrooms. Roundtable paper to be presented at AERA 2022, San Diego, CA.

Rivero, W., Wei, X., Bhargava, P., Zheng, H., Wilkerson, M. H., & Gutiérrez, K. (accepeted). Syncretic Data Reasoning: Youth Leveraging Everyday Knowledges to Expand their Reasoning around Data. Poster to be presented at AERA 2022, San Diego, CA.

Humanistic K12 Data Science (ER)

New article with Victor Lee (Stanford University) and Kathryn Lanouette (College of William & Mary):

Lee, V.♢, Wilkerson, M. H.♢, & Lanouette, K. ‡ (2021). A call for a humanistic stance toward K-12 data science education. Online First in Educational Researcher. doi: 10.3102/0013189X211048810

There is growing interest in how to better prepare K–12 students to work with data. In this article, we assert that these discussions of teaching and learning must attend to the human dimensions of data work. Specifically, we draw from several established lines of research to argue that practices involving the creation and manipulation of data are shaped by a combination of personal experiences, cultural tools and practices, and political concerns. We demonstrate through two examples how our proposed humanistic stance highlights ways that efforts to make data personally relevant for youth also necessarily implicate cultural and sociopolitical dimensions that affect the design and learning opportunities in data-rich learning environments. We offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.

Data Preparation and Variability (MTL)

New paper in Mathematical Thinking and Learning that explores students’ opportunities to engage with variability when working with complex public datasets:

Wilkerson, M. H., Lanouette, K., & Shareff, R. L. (2021). Exploring variability during data preparation: A way to connect data, chance, and context when working with complex public datasets. Mathematical Thinking and Learning. doi: 10.1080/10986065.2021.1922838

Data preparation (also called “wrangling” or “cleaning”)—the evaluation and manipulation of data prior to formal analysis—is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular statistics explorations involve data that are derived from observations that students record themselves or that reflect familiar, relatively closed systems. In contrast, pre-constructed public datasets are much larger in scope and involve temporal, geographic, and other dimensions that complicate inference and blur boundaries between “signal” and “noise.” As a result, students have fewer opportunities to consider sources of variability in such datasets. Due to these constraints, we argue that data preparation becomes an important site for students to reason about variability with public data. Through analyses of repeated task-based interviews with five pairs of adolescent participants, we find that specific actions during data preparation, such as filtering data or calculating new measures, presented opportunities to engage leaners with variability as they prepared and analyzed several public socioscientific datasets. More broadly, our study highlights some changes to theory and curriculum in statistics education that are necessitated by a focus on “big data literacy”.

ICLS & IDC 2021

We will be presenting early work emerging from our Writing Data Stories project at the International Conference for the Learning Sciences and at ACM-SIGCHI’s Interaction, Design, and Children conference! Look our for these two talks:

ISLS 2021: Session C-55. June 8, 9am Pacific

Lopez, M. L.*, Roberto, C. *., Rivero, E.*, Wilkerson, M. H., Bakal, M., & Gutiérrez, K. (2021). Curricular reorganization in the third space: A case of consequential reasoning around data. To appear in Proceedings of the 2021 Annual Meeting of the International Society for the Learning Sciences (ISLS 2021). Bochum, Germany: ISLS.

IDC 2021: Presentation time TBA, June 28-30

Wilkerson, M., Finzer, W., Erickson, T., & Hernandez, D.* (2021). Reflective data storytelling for youth: The CODAP Story Builder. Works-in-progress paper to appear in Proceedings of the 20th ACM SIGCHI Interaction Design and Children Conference (IDC ’21). Worldwide online, 24-30 June.

ILE Computational Thinking SI

I’m happy to announce that a special issue of Interactive Learning Environments I co-edited with Cynthia D’Angelo of the University of Illinois at Urbana Champaign and Breanne Litts of Utah State University. This special issue features a number of studies that explore the integration of computing and computational thinking into specific disciplines and practices. We argue that while the recent proliferation of top-down frameworks for defining computational thinking have helped educators envision what computing integration looks like at a planning level, such efforts are subject to missing rich engagements with computing unless they are complemented by rich, detailed examples of learning with computing in practice. Building on Kafai and colleagues’ (2019) call for more attention and explication regarding the theoretical spaces from which computational thinking is explored, so too should there be more attention granted to the role that spaces of learning play in shaping what counts as meaningful computational learning. Check out our editorial, here:

Wilkerson, M. H., D’Angelo, C., & Litts, B. (Eds.) (2020). Stories from the field: Locating and cultivating computational thinking in spaces of learning. [Special Issue] Interactive Learning Environments, 28(3), 264-271.

The entire special issue has some great examples of both the nuance and power of computational integration when it is complemented by grounded research and analysis.

ICLS 2020 Presentations

We’re excited to be a part of the upcoming International Conference of the Learning Sciences. While we won’t have a chance to interact with colleagues in person, we’re excited to still have a chance to share progress on our most recent work with “socio-critical data literacies.” As we develop our virtual presentations, we’ll link to them here! They include:

Wilkerson, M. H., Roberto, C.*, & Bulalacao, N.* (accepted/conference online). Debugging data: Diagnosing, evaluating, and repairing data for analysis. In Y. Kafai (Org.) & J. Danish (Disc.), Turning bugs into learning opportunities: Understanding debugging processes, perspectives and pedagogies. To appear in Proceedings of the 14th International Conference for the Learning Sciences (ICLS 2020). Nashville, TN, USA: ISLS.

Lanouette, K.‡, Rivero, E.*, Barton, J.*, Bulalacao, N.*, Lopez, M. L.*, Cortes, K.*, Roberto, C.*, Gutiérrez, K., Wilkerson, M. H., Lee, H., Stokes, D.*, Finzer, W., Erickson, T., Petrosino, T., Haldar, L. (accepted/conference cancelled). Writing data stories: Reauthoring scientific data through syncretic computational investigations in middle school science. In C. Matuk & S. Yoon (Orgs.) and J. Polman (Disc.), Data literacy for social justice. To appear in Proceedings of the 14th International Conference for the Learning Sciences (ICLS 2020). Nashville, TN, USA: ISLS.

Lopez, M. L.*, Wilkerson, M. H., & Gutiérrez, K. (accepted/conference online). Contextualizing, historicizing, and re-authoring data-as-text in the middle school science classroom. In G. Arastoopour Irgens, S. Knight, & A. Wise (Org.) Data literacies and social justice: Exploring critical data literacies through sociocultural perspectives. To appear in Proceedings of the 14th International Conference for the Learning Sciences (ICLS 2020). Nashville, TN, USA: ISLS.