Jekyll2026-09-28T16:44:43+00:00https://corelab.berkeley.edu/feed.xmlCoRE LabWe explore how computing practices (e.g., programming, data analysis and visualization, computer simulation, GIS mapping) are changing the ways that young people learn and communicate about our world.Making Climate Justice Count2026-08-18T00:00:00+00:002026-08-18T00:00:00+00:00https://corelab.berkeley.edu/news/2026/08/18/climate-bookMichelle was honored to provide the Foreword to our colleagues Asli Sezen-Barie, Marie K. Stapleton, and Hosun Kang’s just released book, Making Climate Justice Count: Teaching with Data Stories for Action. The book features case studies, practical strategies, and a strong overarching framework for considering how students can engage deeply with climate data throughout the science curriculum.

Check out the book here!

]]>CalCoREHow to be ‘Choosy’: Wrangling Big Datasets2026-07-26T00:00:00+00:002026-07-26T00:00:00+00:00https://corelab.berkeley.edu/blog/choosy“How to be Choosy” provides a framework for reducing the size of very large datasets (with too many rows of columns) according to pedagogical goals. It describes technical methods for reducing dataset size using CODAP or computational notebooks (Jupyter or CoLab), provides interactive templates and practice activites, and offers a DIY guide to help educators and curriculum designers make large datasets manageable for classroom instruction.

Intended Audience & Use Case: These resources are designed for K-12 and undergraduate statistics and data science instructors, as well as curriculum designers. Though some of the ideas and activities might be useful for students as well, we recommend providing them with additional scaffolding and support than is available here.

Available Resources

Associated Publications The collection accompanies the Teaching Statistics publication by Wilkerson et al. (2025) called How to be Choosy: Wrangling Big Datasets for the Classroom.

]]>CalCoREMoDa: Models and Real-World Data2026-07-26T00:00:00+00:002026-07-26T00:00:00+00:00https://corelab.berkeley.edu/blog/modaThe MoDa (Modeling + Data) project combines agent-based simulation, domain-specific block-based programming, and data visualization tools to support students’ reasoning about complex environmental phenomena. By building and running simulations and then comparing them to real-world datasets, students can test, validate, and refine their scientific theories.

Intended Audience & Use Case: Curriculum units are designed for middle school science educators; the project is also producing more generalized tools and frameworks for curriculum developers and education researchers.

Project Resources & Platforms

Associated Publications The design of the original MoDa tool is described in Fuhrmann, T., Rosenbaum, L. F., Eloy, A., Wagh, A., Henrique, B.*, Blikstein, P., & Wilkerson, M. H. (2026). Designing MoDa: A new tool for science learning through data-based computational modeling. International Journal of Child-Computer Interaction, 48, 100819. https://doi.org/10.1016/j.ijcci.2026.100819

The design of the new MoDa tool, which integrates quantitative data analysis support, is described in Wagh, A., Wilkerson, M. H., Eloy, A., Fuhrmann, T., & Bliksteain, P. (2026). Putting the data in MoDa: Integrating agent-based modeling, quantitative data analysis, and teacher responsibity to investigate complex phenomena. Proceedings of the 20th Annual Meeting of the International Society of the Learning Sciences. Irvine, CA, USA.

Findings from our work with MoDa include:

  • Wagh, A., Rosenbaum, L. F., Fuhrmann, T., Eloy, A., Blikstein, P., & Wilkerson, M. (2025). Toward Ontological Alignment: Coordinating Student Ideas with the Representational System of a Computational Modeling Unit for Science Learning. Cognition and Instruction, 43(1–2), 1–32. https://doi.org/10.1080/07370008.2024.2427400
  • Fuhrmann, T., Rosenbaum, L. F., Wagh, A., Eloy, A., Blikstein, P., & Wilkerson, M. H. (2026). Modality matters: Exploring and capturing students’ mechanistic reasoning during computational modeling in science education. In N. Graulich, M. Haskel-Ittah, & R. Wahyu Bachtiar (Eds.), Exploring mechanistic reasoning in science education: Understanding phenomena (pp. 197–219). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-10246-1_11
  • Fuhrmann, T., Rosenbaum, L., Wagh, A., Eloy, A., Wolf, J., Blikstein, P., & Wilkerson, M. H. (2024). Right but wrong: How students’ mechanistic reasoning and conceptual understandings shift when designing agent-based models using data. Science Education, 109(1), 3-26. https://doi.org/10.1002/sce.21890

The MoDa project is what inspired the Rivulet set of tools, see more here.

]]>CalCoRERivulet: Python Notebook Tools for Educational Data Retrieval2026-07-26T00:00:00+00:002026-07-26T00:00:00+00:00https://corelab.berkeley.edu/blog/rivuletThe Rivulet project provides teachers, curriculum designers, and educational researchers with Python notebooks and CODAP plugins that streamline querying and fetching environmental datasets from public API endpoints. These notebooks act as automated wrangling pipelines, translating complex database APIs into clean, structured data that highlight “signature” scientific patterns.

Intended Audience & Use Case: Designed for science educators and curriculum developers. It simplifies the data acquisition process, allowing designers to easily extract environmental time-series datasets (including local air, oceanographic, water quality, and other data) to build custom, region-specific investigations without writing code from scratch or drowning in sparse or ambiguously useful data.

Available Jupyter Notebook Resources

Next Generation Tools in CODAP

  • Rivulet Next CODAP Plugins: Experimental browser-based data exploration tools that load data directly into CODAP without requiring Jupyter runtimes.

Associated Publications The motivation and approach to developing Rivulet tools is described in: Wilkerson, M. H., Eloy, A., & Fitzmaurice, H. L. (2026). Rivulet: A framework and tools for fetching custom, pedagogically generative scientific datasets. Proceedings of the 20th International Conference of the Learning Sciences (ICLS ’26).

]]>CalCoREShow Your Work! Computational Notebooks for Educators2026-07-26T00:00:00+00:002026-07-26T00:00:00+00:00https://corelab.berkeley.edu/blog/syw“Show Your Work!” is a suite of free, web-based introductory Jupyter Notebooks designed for K-12 educators and curriculum designers with little to no prior programming experience. Built on learning sciences principles, the project introduces computational notebooks as epistemic tools, letting teachers experience firsthand what it feels like to conduct notebook-based computational data investigations in specific subject domains. Each notebook introduces a “bundle” of data and statistics ideas, code, and content that focus on specific kinds of subject area analyses such as time series analysis, descriptive statistics, spatial analysis, and more.

Intended Audience & Use Case: Designed for K-12 teachers, teacher educators, and curriculum developers. It can be used as a professional development resource or curriculum guide to help educators envision how to integrate Python and R notebooks into science, math, and humanities classrooms.

Available Notebook Modules

]]>CalCoREWriting Data Stories: Integrating Data into Middle School Science2026-07-26T00:00:00+00:002026-07-26T00:00:00+00:00https://corelab.berkeley.edu/blog/wds“Writing Data Stories” is an NSF-funded research project that integrates computational data analysis into middle school science classrooms. The project teaches students to construct “syncretic data stories”—multimodal projects that blend academic statistical analysis of scientific datasets with personal narrative and social reflection.

Intended Audience & Use Case: Designed for middle school science and statistics teachers, as well as educational curriculum developers. It provides ready-to-use curriculum structures that scaffold data literacy by linking data analysis directly to real-world socioscientific issues.

Available Resource Types

]]>CalCoREWDS: DataBytes Collection2026-07-25T00:00:00+00:002026-07-25T00:00:00+00:00https://corelab.berkeley.edu/blog/wds-databytesDataBytes are quick, bite-sized classroom activities (designed to take 30 minutes or less) that encourage students to interpret and analyze data visualizations related to everyday scientific issues. The resources include several specific lessons focused on visualizations sourced from news media and scientific agency reports, as well as a general framework and DIY guide for teachers to create their own DataBytes activities.

Intended Audience & Use Case: Designed for middle school math and science teachers seeking quick warm-ups or introductory data literacy exercises. It acts as an easy “on-ramp” for incorporating data-centric discussions without requiring extensive class time.

DIY Discussion Framework

To implement these in class, review the official DataBytes Discussion Structure Guide (Google Doc).

Example Databytes Activities

]]>CalCoREWDS: Data Launchpads Collection2026-07-25T00:00:00+00:002026-07-25T00:00:00+00:00https://corelab.berkeley.edu/blog/wds-launchpadsData Launchpads are highly interactive documents built inside the Common Online Data Analysis Platform (CODAP) using the Story Builder plugin. Launchpads act as scaffolded “on-ramps” for students exploring complex public datasets. Each launchpad features built-in background information, multimedia context-setters, data activators, and guided tutorials on graphs, maps, and filtering.

Intended Audience & Use Case: Designed for middle and high school math, science, and social studies educators. They serve as guided classroom demonstrations or initial structured activities to help students build confidence in data analysis tools prior to self-directed inquiry.

Available Data Launchpads

Associated Publications Learn more about the Story Builder plugin in: Wilkerson, M. H., Finzer, W., Erickson, T., & Hernandez, D. (2021). Reflective Data Storytelling for Youth: The CODAP Story Builder. Proceedings of Interaction Design and Children, 503–507. https://doi.org/10.1145/3459990.3465177

]]>CalCoREWDS: Exploration Units Collection2026-07-25T00:00:00+00:002026-07-25T00:00:00+00:00https://corelab.berkeley.edu/blog/wds-unitsWriting Data Stories Exploration Units are comprehensive, 2-3 week long curriculum units designed to build deep data literacy and statistical inquiry skills in middle school classrooms. Centered around authentic socioscientific datasets, these units provide bilingual (English/Spanish) student handouts, slides, and teacher lesson guides.

Intended Audience & Use Case: Designed for science educators, as well as curriculum developers. They provide standard-aligned (NGSS and GAISE II) unit frameworks to help students construct computational data arguments about real-world social and ecological challenges.

Access the Resource Folder

Associated Publications Writing Data Stories units are part of the research reported in several papers including: Lanouette, K., Cortes, K. L., Lopez, L., Bakal, M., & Wilkerson, M. H. (2024). Exploring Climate Change Through Students’ Place Connections and Public Data Sets. Science Scope, 47(3), 18-25. http://doi.org/10.1080/08872376.2024.2340444

Reigh, E., Escudé, M., Bakal, M., Rivero, E., Wei, X., Roberto, C., Hernandez, D., Yada, A., Gutierrez, K. G., & Wilkerson, M. H. (2022). Mapping racespace: Data stories as a tool for environmental and spatial justice. In K. Lanouette & K. Headrick Taylor (Eds.), Bank Street Occasional Paper Series #48: Learning Within Socio-Political Landscapes: (Re)imagining Children’s Geographies. http://doi.org/10.58295/2375-3668.1452

]]>CalCoREChoosy: Billboard Hot 100 Data Wrangling Demo2026-07-24T00:00:00+00:002026-07-24T00:00:00+00:00https://corelab.berkeley.edu/blog/choosy-bh100This interactive data wrangling resource features a curated Billboard Hot 100 dataset along with guided workflows demonstrating how to filter, sort, and slice long, case-heavy datasets (with too many records) to fit specific lesson objectives. It helps educators find the right sub-selections of music charts from the full database for the kinds of investigations they want to do.

To Learn More: This resource is part of How to be Choosy, a published guide that helps educators learn how to convert too-large datasets into ones that are both manageable, and that preserve the pedagogically useful parts of the “real world.” Read the full paper here

Intended Audience & Use Case: This resource is designed for K-12 and undergraduate statistics and data science instructors, as well as curriculum designers. Though some of the ideas and activities might be useful for students as well, we recommend providing them with additional scaffolding and support than is available in these resources.

Access the Resource

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