{"id":1031,"date":"2025-12-13T20:40:09","date_gmt":"2025-12-13T20:40:09","guid":{"rendered":"https:\/\/research.ced.ncsu.edu\/esteemhub\/?page_id=1031"},"modified":"2026-06-08T14:47:08","modified_gmt":"2026-06-08T14:47:08","slug":"tech-tools","status":"publish","type":"page","link":"https:\/\/research.ced.ncsu.edu\/esteemhub\/resources\/tech-tools\/","title":{"rendered":"Tech Tools"},"content":{"rendered":"\n\n\n\n
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\n \"Square\n <\/div>\n <\/div>\n \n
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CODAP<\/h3>\n \n\n \t \t

CODAP (Common Online Data Analysis Platform) is an easy\u00ad-to-\u00aduse data analysis environment designed for grades 5 through 14. CODAP can be used across the curriculum to help students summarize, visualize, and interpret data, advancing their skills to use data as evidence to support a claim.<\/p>\n\t \t \n

Visit the website \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"Tuva\n <\/div>\n <\/div>\n \n
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Tuva<\/h3>\n \n\n \t \t

Tuva provides an interactive data literacy platform designed to make data analysis engaging and accessible. With a vast collection of datasets and intuitive tools, Tuva helps learners build statistical reasoning skills through hands-on exploration.<\/p>\n\t \t \n

Visit Tuva \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"Tableau\n <\/div>\n <\/div>\n \n
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Tableau<\/h3>\n \n\n \t \t

Tableau Public is a free, powerful data visualization tool that allows users to create, share, and explore interactive charts and dashboards. Educators and students can use it to analyze real-world data, develop critical thinking skills, and communicate findings through compelling visual storytelling. By making data visualization more accessible, Tableau Public helps foster a deeper understanding of statistics and data science.<\/p>\n\t \t \n

Explore Tableau Public \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"RossmanChance\n <\/div>\n <\/div>\n \n
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Rossman\/Chance Applet Collection<\/h3>\n \n\n \t \t

Large collection of applets to help teachers visualize statistical concepts and problems with students.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"PHET\n <\/div>\n <\/div>\n \n
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Interactive Simulations for Science and Math<\/h3>\n \n\n \t \t

Data fluency is taught across science, statistics, and data science\u2014each with its own language, practices, and emphases. To bridge these disciplines, we convened a small group of educators and researchers to collaboratively define and prioritize learning goals. The emerging consensus framework now guides our simulation designs for data fluency, ensuring our tools support instruction across multiple educational contexts.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"Stats\n <\/div>\n <\/div>\n \n
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Ditch the calculator with these easy-to-use applets!<\/h3>\n \n\n \t \t

Quick graphs of distributions and apps to help skip the manual data entry on the calculator. Here, simply copy and paste your real-world data in to get great visual representations for your analysis.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"Art\n <\/div>\n <\/div>\n \n
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The Art & Science of Learning from Data<\/h3>\n \n\n \t \t

Explore statistical concepts in an interactive way. Use the apps to construct graphs, obtain summary statistics, find probabilities, get confidence intervals or fit linear regression models. Take screenshots or download graphs of your data.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"RStudio\n <\/div>\n <\/div>\n \n
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Beginner introduction to using R and RStudio<\/h3>\n \n\n \t \t

From help with installation to introductory lessons, coding help, and publishing your work with R Markdown, this site will help you learn R step-by-step.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"Math\n <\/div>\n <\/div>\n \n
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Math Medic AP Statistics Activity Collection<\/strong><\/h3>\n \n\n \t \t

Aligned to College Board Course and Exam Description (CED), these DESMOS lessons help students build conceptual understanding in AP Statistics.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"StatKey\n <\/div>\n <\/div>\n \n
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Statistics: Unlocking the Power of Data<\/strong><\/h3>\n \n\n \t \t

Apps to help teach statistics, and built specifically for the textbook “Statistics: Unlocking the Power of Data<\/strong>” by Lock, Lock, Lock, Lock, and Lock.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

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\n \"BOAST\n <\/div>\n <\/div>\n \n
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Book Of Apps for Statistics Teaching<\/h3>\n \n\n \t \t

The apps in this collection are the work of undergraduate students majoring in Statistics and Data Science at Penn State University. These students took part in the\u00a0BOAST<\/strong><\/em>\u00a0program between 2017 and 2024. (BOAST = Book Of Apps for Statistics Teaching). The program includes training in developing R Shiny applications focused on enhancing teaching and learning of statistical and data science objectives. This is followed by a full-time summer research experience developing the apps in this collection throughout the months of June and July.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n<\/div>\n\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":72,"featured_media":0,"parent":59,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"ncst\/default-header","ncst_dynamicHeaderData":"{}","ncst_content_audit_freq":"","ncst_content_audit_date":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-1031","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/1031","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/comments?post=1031"}],"version-history":[{"count":7,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/1031\/revisions"}],"predecessor-version":[{"id":1501,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/1031\/revisions\/1501"}],"up":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/59"}],"wp:attachment":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/media?parent=1031"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}