ER: A Humanistic Framework for K12 Data Science Education

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 in 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”.

Presentations at ICLS and 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: Stories from the Field

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.

JLS Situating Data Science

Check out Vol 29 No 1 of the Journal of Learning Sciences, a special issue entitled Situating Data Science: Exploring how Relationships to Data Shape Learning, co-edited by myself and Joseph Polman.

Overview. The emerging field of Data Science has had a large impact on science and society. This has led to over a decade of calls to establish a corresponding field of Data Science Education. There is still a need, however, to more deeply conceptualize what a field of Data Science Education might entail in terms of scope, responsibility, and execution. This special issue explores how one distinguishing feature of Data Science—its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded—suggests serious implications for learning and education. The learning sciences is uniquely positioned to investigate how such contextual embeddings impact learners’ engagement with data including conceptual, experiential, communal, racialized, spatial, and political dimensions. This special issue demonstrates the richly layered relationships learners build with data and reveals them to be not merely utilitarian mechanisms for learning about data, but a critical part of navigating data as social text and understanding Data Science as a discipline. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, agentive and socially aware Data Science Education.

Data Moves in TISE

Erickson, T., Wilkerson, M. H., Finzer, W., & Reichsman, F. (2019). Data Moves. Technology Innovations in Statistics Education, 12(1). Retrieved from https://escholarship.org/uc/item/0mg8m7g6

When experienced analysts explore data in a rich environment, they often transform the dataset. For example, they may choose to group or filter data, calculate new variables and summary measures, or reorganize a dataset by changing its structure or merging it with other information. Such actions background, highlight, or even fundamentally change particular features of the data, allowing different types of questions to be explored. We call these actions data moves. In this paper, we argue that paying explicit attention to data moves, as well as their purposes and consequences, is necessary for educators to support student learning about data. This is especially needed in an era when students are expected to develop critical literacy around data and engage in purposeful, self-directed exploration of large and often complex datasets.

Position and footing in clinical interviews; IJSE

Shaban, Y. & Wilkerson, M. H. (In Press). The co-construction of epistemological framing in clinical interviews and implications for research in science education. To appear in International Journal of Science Education. doi: 10.1080/09500693.2019.1620972

Science educators have shown that students’ framings—their expectations of what is going on—influence how they participate, and thus what science knowledge they reveal, in clinical interviews. This paper complements research that explores how interviewers are likely to affect student framings, by exploring how subtler interactions can lead students to change their framings, and thus their behavior, in unexpected ways during clinical interviews. We present data from interviews with two students, Sarah and Omar, as they reasoned about evaporation and condensation. Sarah demonstrated spontaneous and dramatic changes in how she participated over the course of the interview, whereas Omar demonstrated subtler changes that existing methods may not capture. These changes affected the nature of scientific knowledge and reasoning demonstrated by each participant, but could not be fully understood only in terms of interviewer behavior. We use the constructs of footing and positioning theory to examine how students participated during the interviews, and how this affected the ways they demonstrated scientific knowledge and reasoning about the interview topic. In both cases footing and positioning theory allowed us to better understand the dynamic ways students engage in the interview and the knowledge resources they reveal. This paper contributes new methods for analyzing complex interview dynamics, and suggests situations for which such methods are necessary.

Presentations at AERA, NARST, SRTL 2019

We will be presenting work at several conferences this year. Stay tuned to this page for updates on times and locations of each presentation. We are looking forward to everyone’s feedback!

Annual Meeting of the American Educational Research Association

Laina, V., & Wilkerson, M. (2019). Seeing things differently: A form and function analysis of student-generated dynamic data visualizations. PAper to be presented at the 2019 Annual Meeting of the American Educational Research Association (AERA).

Wilkerson, M. & Lanouette, K. (2019). Making data useful: A longitudinal examination of young adults’ developing data transformation processes. Poster to be presented at the 2019 Annual Meeting of the American Educational Research Association (AERA).

Wilkerson, M., Laina, B., Lopez, L., Shareff, R. L., Dogruer, D., & McEachen, W. (2019). Collaborative modeling with complex public datasets in the middle school classroom. To be presented as part of A. Pierson & D. Clark (Orgs.), Supporting modeling epistemologies in the science classroom. Structured poster session at AERA 2019.

Lopez, M. L., Laina, V., & Wilkerson, M. (2019). Agentive use of public quantitative data in scientific argumentation: A case study. Roundtable paper to be presented the 2019 Annual Meeting of the American Educational Research Association (AERA).

Bracho, C., Clark, M., Quan, T., & Wilkerson, M. (2019). Reimagining teacher identities: Activism, criticality, and resistance. Paper to be presented as part of K. Baker-Doyle & K. Vachon (Orgs.), ‘Lift ev’ry voice and sing’: Narratives of teacher educators transforming practices for social justice. Symposium at AERA 2019.

Meeting of the National Association for Research in Science Teaching

Lopez, L., Wilkerson, M., & Laina, V. (2019). Data as proxy: Sociomaterial supports and constraints on the use of data for epistemic agency. Paper to be presented at the 2019 Annual Meeting of the National Association for Research in Science Teaching (NARST).

11th Research Forum on Statistical Reasoning, Thinking, and Learning

Wilkerson, M., Shareff, B., & Lanouette, K. (2019). Learning to transform data: A longitudinal interview study. Paper to be presented at the Eleventh International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-11).

New NSF Grant: Writing Data Stories

We’re so excited to begin a new NSF funded project entitled “Writing Data Stories: Integrating Computational Data Investigations into the Middle School Science Classroom”. The project is a collaboration including Michelle Wilkerson and Kris Gutierrez at UC Berkeley, William Finzer and Natalya St. Clair at the Concord Consortium, Hollylynne Lee at North Carolina State University, Anthony Petrosino at the University of Texas and Austin. Special thanks to Vasiliki Laina and Lisette Lopez at UC Berkeley for their contributions to the proposal and related projects! The abstract is below; learn more hereContinue reading