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.
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]
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.
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 scientiﬁc and mathematical systems: First elaborating their understandings of a given system through constructing a computer model, then ‘‘debugging’’ that knowledge by testing and reﬁning the model. While such activities have been shown to support science learning, difﬁculties building and using computational models are common and reduce learning beneﬁts. Drawing from Collins and Ferguson (Educ Psychol 28(1):25–42,
1993), we conjecture that a major cause for such difﬁculties 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. ﬁfth 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 ﬁndings, and share implications for research and curricular design.
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.
Gravel, B. & Wilkerson, M. H. (2017). Integrating computational artifacts into the multi-representational toolkit of physics education. In D. Treagust, R. Duit, & H. E. Fischer (Eds.), Multiple Representations in Physics Education. Springer. pp. 47-70. [PDF][Springer]
Computational artifacts can serve as important components of the multi-representational toolkit of physics. But like any representation, the meanings of computational models are far from transparent: they are embedded within social, symbolic, and material contexts. In this chapter, we present case studies of two different learning communities that each worked to adopt a participant-generated computational artifact as a shared representational tool that they used to communicate and reason about physical systems. In one, collaborating physicists and mathematicians used a Mathematica notebook to explore the behavior of liquid crystals. In the other, a fifth grade science class used a student-generated computer simulation to reason about the processes of evaporation and condensation. We show how both groups: (1) developed a shared understanding of the computational artifact as a representational tool, (2) leveraged the artifact to focus their attention on their respective goals, and (3) discussed the strengths and limitations of the architecture of the computational environment relative to those goals. We highlight similarities and differences in how professionals and students took up these computational artifacts as shared representations, and discuss instructional implications given the increasingly computational and multi-representational focus of K-12 science education.
Check out some preliminary findings from our interviews with students at the 2017 Interaction, Design, and Children conference at Stanford University!
Wilkerson, M. & Laina, V. (2017). Youth reasoning with interactive data visualizations: A preliminary study. Works-in-Progress paper to appear in Proceedings of the 16th ACM SIGCHI Interaction Design and Children Conference (IDC ’17). Stanford, CA. doi: 10.1145/3078072.3084302
New chapter out about the design of SiMSAM, and the different types of input (and theoretical advancements) that resulted from our work with different audiences. “Teachers, Students, and After-School Professionals as Designers of Digital Tools for Learning” uses Sandoval’s (2014) conjecture mapping to trace the influence of multiple stakeholder groups during the process of iterative design. You can find a prepublication version of the chapter here (this may still have typos or other differences from the final published version). There are lots of other great chapters that explore tensions and resonances between Participatory Design and the Learning Sciences, and processes of learning environments design, in the book, available from Routledge.
We will be presenting two full papers at CSCL 2017:
Wilkerson, M., Shareff, B.*, Gravel, B., Shaban, Y.*, & Laina, V.* (2017). Exploring computational modeling environments as tools to structure classroom knowledge building. Full paper to appear in Proceedings of the 12th International Conference on Computer Supported Collaborative Learning (CSCL 2017). Philadelphia, PA.
Walkoe, J., Wilkerson, M., & Elby, A. (2017). Technology-mediated teacher noticing: A goal for classroom practice, tool design, and professional development.Full paper to appear in Proceedings of the 12th International Conference on Computer Supported Collaborative Learning (CSCL 2017). Philadelphia, PA.
We will be presenting emerging findings from the DataSketch study at the Tenth Annual Meeting of the International Collaboration for Research in Statistical Reasoning, Thinking, and Learning (SRTL 2017) in Rotorua, New Zealand. We’ll also be presenting a review of the major themes and findings from the 2016 Youth, Learning, and Data Science Summit.