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.

 

New chapter in Multiple Representations in Physics Education

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.

DataSketch at IDC 2017

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 in Participatory Design for Learning

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.

Presentation at CSCL 2017

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.

DataSketch and YDS16 at SRTL 2017

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.

Presentations at AERA 2017

Work from the SiMSAM, DataSketch, and CodeR4STATS projects will be featured in presentations at AERA:

Wilkerson, M. H. & Laina, V.* (to appear). Designing to support data visualizations as an exploratory tool in science. Paper to be presented as part of M. Gresalfi (Org.) and D. Clark (Chair), Designing digital environments to support mathematical and scientific reasoning: Theoretical and disciplinary perspectives. AERA 2017.

Wilkerson, M. H., Deitrick, E.*, & Simoneau, E.^ (to appear). Integrating computational thinking in high school statistics through data modeling with R. Poster to be presented as part of B. Litts & M. Wilkerson (Orgs.), Stories from the field: Integrating computational thinking across curricular domains.

Wilkerson, M. H. (to appear). Using a drawing, animation, and simulation sequence to scaffold student production of scientific models. Poster to be presented as part of A. Karan & D. Clark (Orgs.), Supporting science as a modeling practice in the classroom through the lens of NGSS.

New Chapter in Helping Students Make Sense of the World Using NGSS Practices

We have a new chapter out about the “Using Mathematics and Computational Thinking” practice in the NGSS. The chapter features examples from our work using simulation and data visualization in classrooms. You can find a prepublication version of the chapter here (this may still have typos or other differences from the final published version). Or better yet, check out the whole book published by the National Science Teachers’ Association.

2016 Youth Data Science Summit

On August 11-12, the University of California, Berkeley’s Graduate School of Education and School of Information co-hosted the Youth Data Science Summit. Funded by the NSF Cyberlearning and Future Learning Technologies program, the Summit brought together researchers from different communities already active in this emerging field, to promote cross dialogue between those working in computer science/information visualization and in education. For more information, visit the summit website, join the Youth, Learning, and Data Science mailing list, and check out the event twitter feed. The event website will be updated to include videorecordings of panel and keynote presentations and resources shared at the workshop.