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

Data Repurposing in ZDM

New paper coming out in ZDM: International Journal of Mathematics Education!

Wilkerson, M. H. & Laina, V. (In Press). Middle school students’ reasoning about data and context through storytelling with repurposed local data. To appear in ZDM: Mathematics Education.

Publicly-available datasets, though useful for education, are often constructed for purposes that are quite different from students’ own. To use these to investigate and model phenomena, then, students must learn how to repurpose the data. This paper reports on an emerging line of research that builds on work in data modeling, exploratory data analysis, and storytelling to examine and support students’ data repurposing. We ask: What opportunities emerge for students to reason about the relationship between data, context, and uncertainty when they repurpose public data to explore questions about their local communities? And, How can these opportunities be supported in classroom instruction and activity design? In two exploratory studies, students were asked to pose questions about their communities, use publicly-available data to explore those questions, and create visual displays and written stories about their findings. Across both enactments, we found that such opportunities emerged especially when students worked to reconcile (1) their own knowledge and experiences of the context from which data were collected with details of the data provided; and (2) their different emerging stories about the data with one another. We review how these opportunities unfolded within each classroom enactment at the level of group and classroom, with attention to facilitator support.

Commissioned Report: Data Use by Middle and Secondary Students in the Digital Age

Lee, V. & Wilkerson, M. H. (2018). Data use by middle and secondary students in the digital age: A status report and future prospects. 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]

Presentations at AERA & ICLS 2018

Thoma, S.*, Deitick, E.*, & Wilkerson, M. (2018). “It didn’t really go very well”: Epistemological framing and the complexity of interdisciplinary computing activities. Short paper to appear in Proceedings of the International Conference for the Learning Sciences (ICLS 2018). London, England: ISLS. [PDF]

Wilkerson, M., Lanouette, K.*, Shareff, R. L.*, Erickson, T., Bulalacao, N.*, Heller, J., St. Clair, N., Finzer, W., & Reichsman, F. (2018). Data moves: Restructuring data for inquiry in a simulation and data analysis environment. Poster to appear in Proceedings of the International Conference for the Learning Sciences (ICLS 2018). London, England: ISLS. [PDF]

Shareff, R. L*. & Wilkerson, M. H. (2018). Grounding computational modeling experience in fertile soil: A design project with middle school science teachers and students. In A. Wagh (Org.) & J. Kolodner (Discussant), Bridging computational modeling tools & practices into the existing structures of k-16 environments in science education. Symposium to be presented at the 2018 Annual Meeting of the American Educational Research Association. New York, NY, USA, April 13-17.

New SiMSAM paper in Instructional Science

Wilkerson, M. H., Shareff, R., Laina, V., & Gravel, B. E. (2017). Epistemic gameplay and discovery in computational model-based inquiry activities. Online first in Instructional Science. doi: 10.1007/s11251-017-9430-4 [PDF][Springer][Readcube]
In computational modeling activities, learners are expected to discover the inner workings of scientic and mathematical systems: First elaborating their understandings of a given system through constructing a computer model, then ‘‘debugging’’ that knowledge by testing and rening the model. While such activities have been shown to support science learning, difculties building and using computational models are common and reduce learning benets. Drawing from Collins and Ferguson (Educ Psychol 28(1):25–42,
1993), we conjecture that a major cause for such difculties is a misalignment between the epistemic games (modeling strategies) learners play, and the epistemic forms (model types) a given modeling environment is designed to support. To investigate, we analyzed data from a study in which ten groups of U. S. fth graders (n = 28) worked to create stop motion animations and agent-based computational models (ABMs) to discover the particulate nature of matter. Content analyses revealed that (1) groups that made progress—that is, that developed increasingly mechanistic, explanatory models—focused on elements, movement, and interactions when developing their models, a strategy well-aligned with both animation and ABM; (2) groups that did not make progress focused on sequences of phases, a strategy well-aligned with animation but not with ABM; and (3) struggling groups progressed when they received guidance about modeling strategies, but not when they received guidance about model content. We present summary analyses and three vignettes to illustrate these ndings, and share implications for research and curricular design.

CodeR4STATS and Interdisciplinary Computing at ICER 2017

Elise Deitrick presented our ongoing work exploring the integration of RStudio into high school statistics at the 2017 International Computing Education Research conference (ICER ’17).

Deitrick, E., Wilkerson, M., & Simoneau, E. (2017). Understanding student collaboration in interdisciplinary computing activities. In Proceedings of the 13th Annual ACM International Computing Education Research Conference (ICER 2017). ACM: New York, NY, USA.

Many students are introduced to computing through its infusion into other school subjects. Advocates argue this approach can deepen learning and broaden who is exposed to computing. In many cases, such interdisciplinary activities are student-driven and collaborative. This requires students to balance multiple learning goals and leverage knowledge across subjects. When working in groups, students must also negotiate this balance with peers based on their collective expertise.

Balance and negotiation, however, are not always easy. This paper presents data from a project to infuse computing into high school statistics using the R programming language. We analyze multiple episodes of video data from two pairs of students as they negotiated (1) the statistics and computing goals of an activity, (2) the knowledge needed to meet those goals, and (3) whose expertise can help achieve those goals. One pair consistently reached agreement along these dimensions, and engaged productively with both subject matter and computing. The other pair did not reach agreement, and struggled to accomplish their tasks. This work provides examples of productive and unproductive interdisciplinary computing collaborations, and contributes tools to study them.