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https://www.youcubed.org/#

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Inspire ALL Students with Open, Creative Mindset Mathematics

Our Mission

Our main goal is to inspire, educate and empower teachers of mathematics, transforming the latest research on maths into accessible and practical forms. We know from research how to teach mathematics well and how to bring about high levels of student engagement and achievement but research has not previously been made accessible to teachers.

Jo Boaler

Dr Jo Boaler is the Nomellini & Olivier Professor of Education at Stanford University. Former roles have included being the Marie Curie Professor of Mathematics Education in England, and a mathematics teacher in London comprehensive schools. She is author of 18 books, numerous articles and a White House presenter on women and girls. Her latest book is called: Limitless Mind: Learn, Lead and Live without Barriers and is published by Harper Collins. She co-founded www.youcubed.org to give teachers, parents and students the resources they need to excite students about mathematics. She is currently co-leading a K-12 Data Science Initiative and was named as one of the 8 educators "changing the face of education" by the BBC.

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Inspire ALL Students with Open, Creative Mindset Mathematics Our main goal is to inspire, educate and empower teachers of mathematics, transforming the latest research on maths into accessible and practical forms. We know from research how to teach mathematics well and how to bring about high levels of student engagement and achievement but research has not previously been made accessible to teachers.

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https://info.flipgrid.com/

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We express ourselves in different ways. On Flip, you can use our simple and fun built-in camera to record your thoughts, add effects and share within your Flip group as video, audio only or text in your web browser or mobile app.

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Flip is a video discussion and sharing app, free from Microsoft.

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Flip (formerly Flipgrid) is a video discussion and video sharing app, free from Microsoft, built for classrooms and beyond. Learn more.

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https://code.org/

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Learn computer science. Change the world.

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Every student in every school deserves the opportunity to study computer science.

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https://apcentral.collegeboard.org/courses/ap-computer-science-principles

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What Is AP Computer Science Principles?

AP Computer Science Principles introduces students to foundational concepts of computer science and challenges them to explore how computing and technology can impact the world.

Why We Created the Program

We designed AP Computer Science Principles with the goal of creating leaders in computer science fields, attracting those who are traditionally underrepresented in computer science, and engaging them with essential computing tools and multidisciplinary opportunities.

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Explore essential teacher resources for AP Computer Science Principles, including course materials, exam details, and course audit information.

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http://lineardigressions.com/episodes/2017/7/16/from-software-engineering-to-data-science

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From Software Engineering to Data Science

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From Software Engineering to Data Science - Linear Digressions

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Data scientists and software engineers often work side by side, building out and scaling technical products and services that are data-heavy but also require a lot of software engineering to build and maintain. In this episode, we'll chat with a Friend of the Pod named Walt, who started ou

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http://www.globalgoals.org/

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In 2015, world leaders agreed to 17 Global Goals (officially known as the Sustainable Development Goals or SDGs). It's now five years on, and we have more work than ever to do. These goals have the power to create a better world by 2030, by ending poverty, fighting inequality and addressing the urgency of climate change.

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https://hdsr.mitpress.mit.edu/pub/wkhg4f7a/release/2

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The American Statistical Association (ASA) is committed to supporting and enhancing statistics and data science education at all levels, from early grades to the PhD. Attitudes about statistics and mathematics form early, so it is critical to ensure that students in the elementary grades have positive and meaningful statistics and data science education experiences. Provided with the appropriate resources and support, elementary educators will be able to create data-rich learning environments and establish the foundation for future success. This is the motivation for the focus on grades K-6 for the current education initiative, which in the United States is kindergarten (age 5) through sixth (age 11 to 12) grade levels. In this article we explore some of the ASA's achievements and current efforts to inspire future data scientists by expanding elementary education resources.

Keywords: K-12 education, Common Core, mathematics, statistics, learning resources

"How do we ensure that students have flexible problem-solving skills to tackle future problems using data with techniques and technology that may not yet exist?" This question was posed in an editorial introducing a special issue of The American Statistician, the focus of which was the changes required for undergraduate education to keep up with a new world of data and increased demand for statisticians (Horton & Hardin, 2015). The same question is perhaps more important to ask of the K-12 (kindergarten to 12th grade) curriculum, the earlier stages of the education system when students make decisions about the subjects they're interested in, and when the basis of future understanding is laid out. The American Statistical Association's (ASA) answer to ensuring that students are prepared for the world they will enter is to begin data science education in kindergarten.

A recent consensus report from the National Academies of Sciences, Engineering, and Medicine (2018) identified a key goal of undergraduate data science education: "to give all students the ability to make good judgments, use tools responsibly and effectively, and ultimately make good decisions using data." The committee defined these fundamental abilities as "data acumen," skills that all people need to be educated citizens in a data-driven world. However, beginning to pursue data acumen in undergraduate education is not enough. Complementary goals, starting when students enter school for the first time at age 5 to 6, are necessary to make the undergraduate goal a reality. All students should begin to develop data acumen then, so that they can conclude their secondary education with the skills and abilities necessary to continue their education and ultimately be successful in careers requiring data insights, and be responsible global citizens. As stated in the Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): "Students need to begin at an early age to become data-savvy, whether working with small data sets or large, messy data sets, traditional data, or non-traditional data such as text or images. Most future jobs will require some knowledge of statistics and data analytics" (Bargagliotti & Franklin, et al., in press). The number of jobs that require data scientists of some kind is growing exponentially, and to fulfill this future need, students need to begin learning as soon as they can.

With the current lack of positive experiences with data science in the early school system, too often, students in undergraduate school arrive with a dislike (and even fear) of statistics and mathematics, and sometimes this attitude is well-established by the time children get to middle school. The earlier that these skills and ideas are presented to students in a positive light, the more likely they will become more interested in these fields and pursue them in the future. Fundamental additions and changes are needed in early data science education to ensure that students are not turned off from these essential fields.

The ASA was formed in 1839 in Boston-a city burgeoning with educational, technical, and professional opportunities at the time. What started with just five founders soon began to attract hundreds of members. The ASA had ties to the statistical work of the U.S. government, particularly the Census Bureau, which incited a movement led by then-ASA president Francis A. Walker (1883-1897) to expand the association nationwide. Today, the ASA's membership exceeds 17,000 professionals in academia, government, research, and business. It consists of more than 70 chapters, nearly 30 sections, 17 journals, and six yearly conferences. Throughout all the growth and change, the ASA's mission remains the same: to promote the practice and profession of statistics.

The association developed a strategic plan to achieve its mission. The plan lays out three overarching themes: (1) Enhancing the Diversity and Breadth of our Association, (2) Increasing the Visibility of our Profession, and (3) Ensuring the Future of our Profession. Education is a key component of the third theme, and we would argue it is a cornerstone of ASA's vision to realize a world that relies on data and statistical thinking. The important work needed to reach this vision begins in kindergarten classrooms across the United States.

The ASA has a longstanding commitment to providing educational resources and supporting K-12 education, though in the past efforts have concentrated on middle and high school levels (ages 11-18). The ASA's presidential initiatives have generated important contributions to further the themes in the strategic plan. For example, 2016 ASA president Jessica Utts created an initiative to provide information on careers in statistics to students taking the Advanced Placement Statistics course, as well as other high school students, teachers, counselors, and parents.

Utts charged a working group to curate and develop resources for ASA members to use in outreach activities with K-12 students. As part of the initiative, an ASA web page was created to address these areas and more. Outreach is essential to help make students aware of the opportunities available to them, as well as encourage them to pursue data science by providing essential information. As students engage in the formal curriculum in their classrooms, they ask questions like "What does a data scientist do?" and "How do you prepare for a career as a data scientist?" Members of the ASA are able to provide authentic answers to these questions and can partner with teachers to guide students throughout their education.

Continuing with Utts's educational focus, 2017 ASA president Barry Nussbaum (2018) had an initiative called the House of Statistics-Explore, Engage, and Learn. Nussbaum's efforts focused on middle school students and educators, and his working group developed the website http://www.houseofstatistics.org/. This website has videos, links to resources, games, and more. A collaborative effort with Laber Labs, this site introduces statistics and data science to mainly middle school students using entertaining and engaging educational videos and games. Continuing the goal to have initiatives build on earlier work, the team working on the current K-6 initiative will add content to the site.

These previous efforts focused on middle and high school students, but we know that the mission of promoting the practice and profession of statistics must begin before middle school. It is essential that we introduce children to concepts associated with analyzing data and statistical literacy at an earlier age. The ASA will collaborate with teachers on this initiative to develop learning environments that support their teaching practices, so we can be good partners to educators and integrate resources into the existing curriculum.

We have described some of the ongoing ASA student-centered activities, but there is a lot more going on-too much to list in this article. In this section, we want to highlight activities that will inform the 2020 ASA initiative on K-6 education.

Because K-12 education is such an important part of ASA's mission, the ASA board of directors created the special position of K-12 statistical ambassador to lead the charge. Christine Franklin was appointed in 2016 and continues to guide the creation of professional development materials for educators and teachers, conduct workshops, engage in outreach to the STEM educational community, and much more. ASA members will be working with Franklin in 2020 to expand her efforts with the early grades.

For many years now, the ASA has sponsored a special workshop for middle and high school teachers, which is held at the Joint Statistical Meetings (JSM). Since JSM takes place in late summer, it is possible for teachers to attend the workshop. This workshop is called Meeting Within a Meeting (MWM) and focuses on statistics for middle and high school mathematics and science teachers. Teachers engage in active learning to augment their ability to teach statistics within the Common Core State Standards (CCSS) (2010) and the Next Generation Science Standards. Attendees can earn professional development credit by attending the MWM. The ASA is exploring options for expanding the MWM to include a K-6 component.

Data challenges are a great way to introduce students to the fun of data science and statistics. The ASA sponsors several data challenges suitable for high school students. The annual Fall Data Challenge for high school and college students hosted at This is Statistics typically focuses on a call to address real problems affecting our society. The 2019 Fall Data Challenge used data from the U.S. Department of Housing and Urban Development (HUD) and asked the students to develop insights into ways to address homelessness in Los Angeles, New York City, and Seattle. A spring competition-Statsketball- keeps the excitement going throughout the school year. This contest uses statistics to make predictions about the NCAA basketball tournament. What's Going On in This Graph? is a weekly feature produced by the New York Times Learning Network and the ASA. This feature asks middle and high school students to look at graphs and to become critical consumers of these visualizations. In 2020, we are exploring the option of developing data challenges appropriate for K-6 students. The theme for JSM 2020 is Everyone Counts: Data for the Public Good, and we hope to continue this theme beyond JSM by engaging students in challenges to advance their understanding of data and expose them to its numerous capabilities.

A goal of the 2020 initiative is to make the list of K-12 resources on the ASA website more comprehensive. Federal statistical agencies such as the Census Bureau and the Bureau of Labor Statistics (BLS) have web pages with resources for educators, like the BLS's Kids Page, which contains games, career information, and how-tos for kids, as well as resources for teachers. The ASA also hosted the Census at School project, which provides an opportunity for students to analyze census-related data and problems. Adding these resources and more to the ASA website will help these future researchers and their teachers learn about available data sources.

"Classify objects into given categories; count the numbers of objects in each category and sort the categories by count" (CCSS). This describes one of the Kindergarten Measurement and Data standards from the Common Core State Standards for Mathematics. This standard illustrates that primary students are in fact data scientists; they are learning to use "group by." The ASA's overarching goal for this initiative is to support teachers and caregivers as they begin the data science education of children as young as 5 and 6.

The ASA has already presented some ideas for the new K-6 initiative that work in conjunction with current educational outreach efforts and the development of a resource repository. But the new initiative is also starting projects that go into new territory, beyond the existing efforts.

One of these projects is a collaboration with the Stats+Stories podcast team. Wendy Martinez, the current ASA president, joined John Bailer, the co-creator of Stats+Stories and the chair of the Department of Statistics at Miami University in Oxford, Ohio, for a podcast during which they were interviewed by two middle school students. The ASA hopes to have many more conversations with students to understand their thinking and what they want to learn more about. Though this project currently focuses on early middle school students, the ASA hopes to expand it in the future to include even younger students in elementary grades.

Research suggests that fun and informational games are effective resources for teaching and learning at all levels (Qian & Clark, 2016). To help celebrate Mathematics and Statistics Awareness Month (MSAM) and as part of this presidential initiative, the ASA launched a game development challenge for professionals to design games for students to play. As the challenge encouraged creativity, the game could be a board game, card game, video game, or Shiny app like the Correlation Game. Although the initiative was focused on K-6, the game could be designed for any age level to play.

The Correlation Game, STEM Jeopardy, and the Laber Lab video games highlighted in the 2020 MSAM poster are great examples, but more educational games are needed. The ASA is asking participants to work with colleagues or independently to generate ideas for types of games. A panel will judge all the submissions and prizes will be awarded. Submissions will be accepted until December 31, 2020. Go to https://bit.ly/32166I7 to respond to the challenge. The competition calls for current professionals to not only become more aware of the next generation of data scientists but also create resources for students to actually use.

The BLS publishes employment projections 10 years into the future. The latest publication from the BLS projects employment (in the United States) for detailed occupations out to 2028. We thought it would be interesting to look at the list of the fastest growing occupations because educating job seekers for employment 10 or more years from now needs to start in the early grade levels.

According to these projections, a statistician is one of the fastest growing professions with the number of jobs increasing by 35 percent. Some related occupations, such as data scientist and operations research analyst, are also projected to increase-by 31 percent and 25 percent, respectively. The estimated increases in employment for these fields highlight the critical need to ensure a pipeline of individuals prepared for careers in statistics and data science. We are convinced that an effective pipeline begins with elementary students. Learning mathematics and statistics is a developmental process, so we want elementary students to have appropriate, positive early learning experiences.

The ASA will continue to devote resources and time to educational outreach and development efforts, as this is one of its strategic goals. We have outlined just some of the possible ideas for our K-6 initiative; the President's Corner in the August 2020 issue of the AMSTAT News provides more details and links to resources (Martinez, 2020). We are always looking for new ideas and resources, and we welcome any you might have. We are particularly interested in hearing from our international colleagues because ensuring our future through education should be a global priority. Feel free to email Donna LaLonde at ASA ( donnal@amstat.org) with your ideas.

The authors have nothing to disclose.

We would like to thank Xiao-Li Meng for giving us this opportunity to describe important efforts to foster statistics education at early ages and for his patience as we revised this article. We thank the reviewers for their insightful comments, especially the reviewer who did a wonderful job making edits. The comments resulted in a much-improved publication.

For more on all of the educational resources and opportunities, see the ASA website.

More details and highlights of Christine Franklin's efforts are also available on the web site.

Bargagliotti, A., & Franklin, C., et al. (in press). Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II).

Horton, N. J., & Hardin, J. S. (2015). Teaching the next generation of statistics students to "think with data." The American Statistician, 69, 4, 259-265. https://doi.org/10.1080/00031305.2015.1094283

Martinez, W. (2020, August 1). Does anyone still used colored chalk? AMSTAT News. https://magazine.amstat.org/blog/2020/08/01/colored-chalk-new-school-year/

National Academies of Sciences, Engineering, and Medicine. (2018). Data science for undergraduates: Opportunities and options. Washington, DC: The National Academies Press. https://doi.org/10.17226/25104

National Governors Association Center for Best Practices, Council of Chief State School Officers. (2010). Common Core State Standards Mathematics. http://www.corestandards.org/Math/

Nussbaum, B. D. (2018). Statistics: Essential now more than ever. Journal of the American Statistical Association, 113(522), 489-493. https://doi.org/10.1080/01621459.2018.1463486

Qian, M., & Clark, K. (2016). Game-based learning and 21st century skills: A review of recent research. Computers in Human Behavior, 63, 50-58. https://doi.org/10.1016/j.chb.2016.05.023
This article is © 2020 by Wendy L. Martinez and Donna LaLonde. The article is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode), except where otherwise indicated with respect to particular material included in the article. The article should be attributed to the author identified above.

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Data Science for Everyone Starts in Kindergarten: Strategies and Initiatives from the American Statistical Association · Issue 2.3, Summer 2020

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The American Statistical Association (ASA) is committed to supporting and enhancing statistics and data science education at all levels, from early grades to the PhD. Attitudes about statistics and mathematics form early, so it is critical to ensure that students in the elementary grades have positive and meaningful statistics and data science education experiences.

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https://www.techlearning.com/news/how-to-teach-data-science-in-k-5

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(Image credit: Domino Data Lab)

It's never too early to start teaching data science to kids.

That's the message from the National Science Teaching Association. In honor of April being Mathematics and Statistics Awareness Month, the organization has partnered with the Domino Data Lab to release a classroom-ready data science lesson plan for K-5 educators. The lesson plan supports Florence the Data Scientist and Her Magical Bookmobile, a new children's ebook from Domino Data Lab written by Ryan Kelly and illustrated by Mernie Gallagher-Cole, which is geared to children aged 6-8 and free for educators and their students.

Tricia Shelton, NSTA's chief learning officer, discusses how to utilize the new resources and best practices for elementary school teachers to teach data science.

Get Students Thinking About Data Science Early

"If we can get kids to start thinking about how they can collect data, how they can analyze data, how they can harness the power of data to develop solutions to problems in the classroom setting early on, then as they progress up through K-12 education, we can expand on that developing knowledge and skill set," Shelton says. "They can start to tackle more complex problems, more societally pressing problems. And even though kids might not be the ones who are solving those problems, there's power in the classroom when they can act and think like scientists and engineers, as those in the STEM field do."

Starting with young students allows teachers to build those multidisciplinary competencies over time and exposes students to potential opportunities later in life. "They can see themselves in those careers and in the places outside of school where they could have an impact," she says.

Utilize Free Resources for Teaching Data Science

NSTA's recently released data science lesson plan walks educators through how to teach students to use data to predict the length of their shadows. For example, there is advice for encouraging students to wonder and speculate about the length of their shadow and record their predictions. Then the lesson plan describes how to have them actually measure shadow length at various times of the day using common classroom items such as chalk and measuring tape. The students can then compare data, look for patterns in the data, and create graphs to predict shadow length in the future.

At the conclusion of the lesson there are tips for incorporating Florence The Data Scientist and Her Magical Bookmobile. The book is a fun way to have children extend the lesson and further explore the possibilities of data science.

The lesson is designed to show students how to recognize patterns and make predictions about shadow length and to realize that the same type of thinking and data analysis can be used to make predictions in all kinds of examples in the real world. "And this is what data scientists do," Shelton says

This lesson on shadow length is part of NSTA's Daily Do, a collection of Next Generation Science Standards-aligned and grade-specific sensemaking tasks that educators can use to better engage students in scientific thinking.

You Don't Need to Be an Expert to Teach Data Science

Teaching data science can be intimidating, even for a seasoned teacher. "They say to themselves, 'What is data science? How could I possibly tackle data science in my classroom?'" Shelton says. But the Daily Do and the shadow lesson are designed to help educators navigate the process.

"What this lesson does is it shows them within their own data policy standards that there are actually pieces of the science and engineering practices," Shelton says. "It is showing teachers a piece of the standards that in many areas is a little bit underutilized, and then giving them the guidance to be able to support that kind of reasoning and thinking in analyzing the data in order to make predictions. The lesson itself has a lot of guidance for the teacher, so that they feel comfortable in entering into this realm."

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(Image credit: Domino Data Lab) It's never too early to start teaching data science to kids. That's the message from the National Science Teaching Association . In honor of April being Mathematics and Statistics Awareness Month, the organization has partnered with the Domino Data Lab to release a classroom-ready data science lesson plan for K-5 educators.

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https://informationisbeautiful.net/

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Distilling the world’s data, information & knowledge into beautiful infographics & visualizations

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https://www.ashg.org/publications-news/trainee-newsletter/the-decolonization-of-dna-day/

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Author: Latrice Landry, PhD

National DNA Day, designated as April 25th by the 108th U.S. Congress, celebrates the DNA double helix discovery of 1953 and the completion of the human genome project as two of the most significant scientific accomplishments in the past 100 years. ASHG has a long tradition of celebrating DNA day through a variety of individual and collective efforts. However, 2020 marks the first year of the De-Colonize DNA Day celebration, an effort to raise awareness and stimulate discussion about the societal and political implications of DNA. Here we share some information about the movement to de-colonize DNA and some highlights of the event and the day.

De-colonize DNA Day was held on April 24, 2020 as a twitter conference preceding the DNA day activities. The conference, organized by @kstsosie [Krystal Tsosise], @LMGLandry [Latrice Landry] and @doublehelixhist [Jerome de Groot], sought to re-frame narratives of #DNA. The reframing was achieved through critical discussions of the impacts of genomics and DNA on society as a whole, with a keen focus on communities who are underrepresented in genomics. The twitter social media platform allowed genetic professionals to engage a broader audience around the impact of DNA in society.

The schedule featured 8 speakers who each spent 30 minutes introducing their topic through text and photo-based tweets.

Following the presentations, panelists Elizabeth Nelson, Cesar Fortes-Lima, Jonathan Jackson, Nayda Ali, Diego Hernandez, Ray Allen, Tyler Tully, Jason Williams, Joseph Yracheta and Kevin Keys engaged the presenters in a question and answer session for 30 minutes, capturing takeaways with the hashtag, #DecolonizeDNA. The all-day event (lasting from 9 a.m. to 5 p.m. EST) not only garnered 461 tweets, 885 followers and 800 likes but fostered a dynamic discussion about DNA and its impact on identity and health across the public sphere. One audience member reflected, "I came away with an increased sense of responsibility to correct the issues...and to decolonize the field I hope to be a part of in the near future." Another audience member noted "I'm blown away by how amazing the talks and topics were, the engagement by the panelists and twitter audience, and the steps being taken to #DecolonizeDNA and research." In addition to the high caliber of the presentations, it is also important to emphasize that these presentations highlighted ideas, questions and research that are relatively absent in the larger mainstream genetics scientific discourse. As a genetics society, we have made it our mission to make the benefits of genetic research real for all. As we seek to achieve that goal, it is imperative that we continue to make strides in not only diversifying our genetic workforce, but also elevating the voices and research of those diverse audiences in our scientific ecosystem. For more information on this topic, search the #DecolonizeDNA hashtag on twitter, where you can see all the presentations and discussions.

NOTE: Many of the individuals mentioned in the schedule have graduate or professional degrees. For more information about the scholarly work highlighted in the presentations, please see the links provided within the De-Colonize DNA Day presentations using the hashtag #DecolonizeDNA.

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National DNA Day, designated as April 25th by the 108th U.S. Congress, celebrates the DNA double helix discovery of 1953 and the completion of the human genome project as two of the most significant scientific accomplishments in the past 100 years.

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https://west.edtrust.org/data-equity-walk-toolkit/

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What is a Data Equity Walk?

A Data Equity Walk is a 45-90 minute activity for any size audience - high school and college students, teachers, district leaders, community members, or others - to engage with education data and discuss equity issues. Participants dive into data that shows education outcomes and exposes gaps between groups of students. The data usually show district or school performance across different measures like student achievement and school climate.

Participants explore the data individually before collectively discussing implications and identifying solutions to address disparities and improve outcomes.

Data Equity Walk participation does not require prior experience with data and is geared toward all audiences.

Create Your Own Data Equity Walk

Depending on what resources you're looking for, click on the links below to access editable planning documents as well as ideas to inspire the creation of your own Data Equity Walk. Remember, these are just optional resources to help you - there's no right or wrong way of doing this!

"I thought this was a fantastic way to get folks engaged with the data and talking together about concrete needs and potential solutions for our students to promote equity." -Southern CA participant

"I learned how to advocate for the work that needs to be done in our community." -Central CA participant

"This is a great method for allowing participants to absorb and react to equity data in an interactive way - better than a sit-down presentation." -Southern CA participant

"The Data Equity Walk ... was eye opening and elicited a thoughtful discussion about education policy, practice, and the role we can all play in improving outcomes for more students in California...it was exciting to hear passionate personal stories of what brings us all to this work despite the challenges." -Northern CA participant

"I learned where to obtain data from different sources." -Central CA participant

"It can be difficult to fully engage participants in the research process and this method was safe, inviting, and had a community-building component to it." -Southern CA participant

"It was truly inspirational to be among organizations and students interested in learning more about the educational inequity that proliferates within California schools. As a teacher, I was motivated by the interest I saw from [students] through the deep and critical conversations during the equity walk." -Northern CA participant

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Data Equity Walk Toolkit - The Education Trust - West

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What is a Data Equity Walk? A Data Equity Walk is a 45-90 minute activity for any size audience - high school and college students, teachers, district leaders, community members, or others - to engage with education data and discuss equity issues. Participants dive into data that shows education outcomes and exposes gaps between groups of students.

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https://edtechmagazine.com/k12/article/2021/03/working-toward-digital-equity-k-12-education

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Technology Solutions That Drive Education

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Students Face Future Disadvantages Without Digital Equity

When a lack of digital equity exists, students' job preparedness also suffers.

According to " The Future of Jobs Report 2020," published in October by the World Economic Forum, critical thinking, problem-solving, self-management and resilience are the top skills companies will seek in applicants, looking ahead to 2025. Emerging technologies and increased automation make it necessary to learn these skills at an early age.

When students lack devices, they also lose valuable opportunities to hone the critical thinking and analytical skills that are inherent to technology use. Consequently, they are at a disadvantage when entering some of the fastest-growing job markets, such as data analytics and AI specialist fields.

This doesn't mean every student needs to major in a STEM field. However, with multiple industries adopting data science and computer science as a part of their work, students need to learn how these and other skill sets integrate across subjects. To authentically engage in these types of activities, students and teachers need to be creating with, not just consuming, technology. However, based on WEF data, even that won't be enough. Students will need to integrate information from multiple sources to innovate on existing concepts to meet the demands of increased automation, and also understand others with different backgrounds and actively engage with them.
RELATED: Technology in the classroom promotes equity in education.
For students to build this skill set, educators will need appropriate professional development. Professional development won't just help educators learn how to blend technology and instruction in a meaningful way. It will help teachers of all grades and disciplines create learning environments where students can practice and build the technology skills needed for the future.

However, professional development isn't the only component necessary to support digital equity across the nation.

Forethought Helps Districts Plan for Digital Equity

One of the most advantageous paths for achieving digital equity is strategic long-term planning. The process of purchasing technology and getting it into students' hands is a long game with a domino effect. If you give a user technology, they're going to need professional development. Once they get that professional development, they're going to need more advanced professional development. If you give a user technology and professional development, they're going to need IT staff support.

With long-term strategic planning, IT leaders can consider the additional needs that accompany new technology. Then, they can ensure their technology is sustainable and equitable. Because many schools went one-to-one overnight, district leaders are looking at ways to support, repair and replace the increased technology.
EXPLORE: Streamline one-to-one computing deployments in the era of remote learning.
Educators, instructional staff and IT staff must work together to align pedagogy and technology to the district's goals for teaching and learning. Integrating technology into a district's or school's purchasing plan can help make the systems that support teaching more effective and efficient. When this happens, it can result in more time and energy for staff to help students reach a deeper level of knowledge and build the skills they'll need in their careers, since educators will be able to anticipate what technology will be available from year to year, for example.

By thinking about it from classroom and digital learning perspectives, educators can use the technology to transform their learning environments.

Build Support for Digital Equity Through Advocacy

Having a plan to increase digital equity does not always mean the resources are available or in place to execute that plan.

Educators and IT leaders should advocate to the superintendent for the budget and resources they need to meet technology goals and achieve equity. In addition to telling community and family stories of the impact of technology on teaching and learning, it's critical to have statistics and other data to support any technology-related requests.

Because digital equity reaches into multiple facets of K-12 education, it is important to bring other stakeholders and experts into the room where decisions are being made. For example, special education departments often have valuable insights into student data privacy, because this issue is among their longstanding top priorities. Leaning on their departmental expertise can help districts create a full-fledged and sustainable digital equity plan that takes into account regulatory requirements.

District leaders can also work with companies to advocate for educational resources from their partners. Intel, for instance, has released many resources for digital learning and continues to work with its partners to help bring more to the table for school administrators.

Schools can also advocate to state and local governments. While the funding earmarked for education in the American Rescue Plan Act of 2021 is available to schools through 2024, districts should be thinking about what they will need in 2025, how to prepare for it now and how to maintain it for all students moving forward.

Joining organizations like CoSN can help schools advocate for funding and digital equity. Educators can be involved at the national level and guide those organizations that are planning and having serious conversations about digital equity, whether at the local, state or national level.

This article is part of the "ConnectIT: Bridging the Gap Between Education and Technology" series. Please join the discussion on Twitter by using the hashtag.
Brought to you by:

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Working Toward Digital Equity in K-12 Education

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When a lack of digital equity exists, students' job preparedness also suffers. According to " The Future of Jobs Report 2020," published in October by the World Economic Forum, critical thinking, problem-solving, self-management and resilience are the top skills companies will seek in applicants, looking ahead to 2025.

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https://www.youcubed.org/data-big-ideas/

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Arnold, P., & Franklin, C. (2021). What Makes a Good Statistical Question?. Journal of Statistics and Data Science Education, 29(1), 122-130.

Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D., (2020). Pre-K - 12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education. Retrieved from: https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf

K-12 Computer Science Framework Steering Committee. (2016). K-12 Computer Science Framework. ACM. Retrieved from: https://k12cs.org/

Seehorn, D., & Clayborn, L. (2017, March). CSTA K-12 CS Standards for All. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (pp. 730-730).

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Arnold, P., & Franklin, C. (2021). What Makes a Good Statistical Question?. Journal of Statistics and Data Science Education, 29(1), 122-130. Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D., (2020). Pre-K - 12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education.

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https://www.youngdatascientists.org/bridges-to-data-science

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Bridges to Data Science — Young Data Scientists League

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This open resource outlines modern data science and artificial intelligence (AI) concepts and maps them to their corresponding roots in K-12 Common Core Math Standards and the international PISA 2021 Mathematics Framework.

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https://magenta.tensorflow.org/coconet

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Coconet: the ML model behind today’s Bach Doodle

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Have you seen today’s Doodle? Join us to celebrate J.S. Bach’s 334th birthday with the first AI-powered Google Doodle. You can create your own melody, an…

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https://www.artnome.com/art-analytics

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Yesterday was Mark Rothko's birthday, and I wanted to celebrate by sharing some stats from the Artnome database on one of my favorite artists.

Born on September 26th, 1903, Mark Rothko would be 115 years old today. Though he tragically cut his own life short at the age of 63, Rothko left us 836 known paintings that were created over a 46-year span. This translates to roughly 18 paintings a year, more than one per month. Of course, no artists' production is a constant; there are fat years and lean years of production.

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Yesterday was Mark Rothko's birthday, and I wanted to celebrate by sharing some stats from the Artnome database on one of my favorite artists. Born on September 26th, 1903, Mark Rothko would be 115 years old today. Though he tragically cut his own life short at the age of 63, Rothko left us 836 known paintings that were created over a 46-year span.

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https://www.turing.ac.uk/research/interest-groups/ai-arts

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Introduction

The cultural heritage sector is experiencing a digital revolution driven by the growing adoption of non-invasive, non-destructive imaging and analytical approaches generating multi-dimensional data from entire artworks. The ability to interrogate this wealth of data is essential to reveal an artist's creative process, the works' restoration history, inform strategies for its conservation and preservation and, importantly, present artwork in new ways to the public.

However, the availability of such rich datasets poses a major challenge: traditional approaches are not adequate to inspect the currently available wealth of data, so there is a strong drive and need to explore and adopt automated tools to interrogate these emerging large multi-dimensional datasets. This interest group intends to facilitate the cross-pollination of ideas between stakeholders in the area of data science and in cultural heritage institutions, with the overarching goal to advance the area of artificial intelligence for art.

Explaining the science

Techniques such as 'macro X-ray fluorescence' (MA-XRF) scanning, hyper-spectral imaging (HSI), and traditional (digital) imaging such as x-ray radiography (XRR) and infrared imaging (IRR) have enabled the heritage sector to greatly expand the amount of data they can use to understand an artist's creative process, develop conservation and preservation techniques, investigate art history and present artwork in new ways.

However, the ability to interrogate such multi-dimensional rich datasets calls for new supervised, semi-supervised and unsupervised machine learning approaches capable of ingesting multi-modal datasets in order to tackle a number of relevant challenges arising in the domain of art investigation. These range from:

Revealing the distribution of materials present within a painting's layers

Identification, characterisation, and visualisation of relevant sub-surface features present within a painting's layers (not always obvious by simple manual inspection of the data) such as preparatory sketches, pentimenti, or earlier concealed designs

See group organiser Miguel Rodrigues's talk at CogX 2019, detailing examples of challenges that can be addressed by machine learning technology in the area of art investigation:

Aims

Bringing together experts crossing various fields such as machine learning, computer vision, art history, art conservation, and cultural heritage, this research interest group aims to explore how emerging AI techniques can shape the general area of art investigation, including art history, conservation and preservation, and art presentation. It will encompass a number of activities including:

Shaping an interdisciplinary scientific community cutting across areas that are traditionally siloed in the UK

Serving as a platform to scope cross-disciplinary collaborations, form research consortia, and outreach activities such as research seminars, research workshops, and public oriented events

Serving as a platform to build AI capability for cultural heritage institutions, contributing to the visibility of UK museums and galleries.

Acting as a beacon of cross-disciplinary and co-created research within the UK and beyond.

Over the long-term, the 'AI for Arts' interest group has the ambition to also catalyse activities in the wide area of AI for the creative industries, facilitating collaborations in other disciplines beyond visual and fine arts, such as poetry and fiction, music, design and performance.

Talking points

How can AI and data science techniques reveal a painting's creative process (including an artist's palette, materials, and methods), reveal a painting's restoration history, or inform strategies for conservation and preservation?

Challenges: This requires new computational approaches capable of leveraging emerging imaging and analytical techniques such as macro x-ray fluorescence (MA-XRF) and hyper-spectral imaging (HSI) to deliver the distribution of materials present in a painting.

Example output: Machine learning algorithms capable of ingesting multi-dimensional datasets to deliver the distribution of materials present in a painting.

How can AI and data science techniques enable the visualisation of relevant sub-surface features present within a painting's layers such as preparatory sketches, pentimenti, or earlier concealed designs?

Challenges: This also requires new computational approaches capable of leveraging multi-modal multi-dimensional datasets to deliver visualisations of such features.

Example output: Machine learning algorithms delivering visualisations of sub-surface features support new public experiences in galleries, museums or other cultural heritage institutions.

How can AI and data science techniques be made available to GLAM (galleries, archives, libraries and museums) institutions through a UK-wide research infrastructure?

Challenges: Cultural institutions increasingly require advanced AI and data science techniques to be applied to their collections, to enrich and analyse them. This will require an engineering effort in order to expose them as a service, in order to reach all institutions, large and small, across the UK.

Example output: Image processing and feature extraction, pattern matching and advanced analytics techniques delivered as a service to institutions exposing their data following known standards.

Andre Piza, [email protected]

Representatives

Owen Hopkin - Arts Council England
Tonya Nelson - Arts Council England
Max Saunders - University of Birmingham
Lindsey Askin - University of Bristol
Maja Maricevic - British Library
Torsten Reimer - British Library
Dominic Oldman - British Museum
Diana Tanase - British Museum
Caroline Bassett - University of Cambridge
Carola Bibiane Schönlieb - University of Cambridge
Brigitta Zics - University College London
Drew Hemment - University of Edinburgh
David Murray-Rust - University of Edinburgh
Fabrizio Nevola - University of Exeter
Arran Rees - University of Leeds
John Stell - University of Leeds
Lukas Nohrer - University of Manchester
Nick Bryan-Kinns - Queen Mary University of London
Mark Sandler - Queen Mary University of London
Catherine Higgitt - National Gallery
Marika Spring - National Gallery
David De Roure - University of Oxford
Seth Giddings - University of Southampton
Sunil Manghani - University of Southampton
Sean Hand - University of Warwick
Steve Ranford - University of Warwick

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The cultural heritage sector is experiencing a digital revolution driven by the growing adoption of non-invasive, non-destructive imaging and analytical approaches generating multi-dimensional data from entire artworks. The ability to interrogate this wealth of data is essential to reveal an artist's creative process, the works' restoration history, inform strategies for its conservation and preservation and, importantly, present artwork in new ways to the public.

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https://www.sciencedirect.com/science/article/pii/S0166361521000543

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https://www.nationalgeographic.com/animals/article/scientists-plan-to-use-ai-to-try-to-decode-the-language-of-whales

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On a crisp spring morning in 2008, Shane Gero overheard a pair of whales having a chat. Gero, a Canadian biologist, had been tracking sperm whales off the Caribbean island nation of Dominica when two males, babies from the same family, popped up not far from his boat. The animals, nicknamed Drop and Doublebend, nuzzled their enormous boxy heads and began to talk.

Sperm whales "speak" in clicks, which they make in rhythmic series called codas. For three years Gero had been using underwater recorders to capture codas from hundreds of whales. But he'd never heard anything quite like this. The whales clicked back and forth for 40 minutes, sometimes while motionless, sometimes twirling their silver bodies together like strands of rope, rarely going silent for long. Never had Gero so desperately wished he understood what whales were saying. He felt as if he were eavesdropping on brothers wrestling in their room. "They were talking and playing and being siblings," he says. "There was clearly so much going on."

Over the next 13 years, Gero, a National Geographic Explorer, would record and get to know hundreds of sperm whales. But he kept coming back to a revelation that struck him as he'd listened to Drop and Doublebend: If humans were ever to decode the language of whales, or even determine if whales possessed something we might truly call language, we'd need to pair their clicks with the context. The key to unlocking whale communication would be knowing who the animals are and what they're doing as they make their sounds.

One of humanity's most enduring desires is the enchanting notion that we might one day converse with other species. In the years since Gero's insight, and partly because of it, the potential to bridge this communications gap has grown less fanciful. On Monday, a team of scientists announced that they have embarked on a five-year odyssey to build on Gero's work with a cutting-edge research project to try to decipher what sperm whales are saying to one another.

Such an attempt would have seemed folly even just a few years ago. But this effort won't rely solely on Gero. The team includes experts in linguistics, robotics, machine learning, and camera engineering. They will lean heavily on advances in artificial intelligence, which can now translate one human language to another without help from a Rosetta Stone, or key. The quest, dubbed Project CETI (Cetacean Translation Initiative), is likely the largest interspecies communication effort in history.

Already, these scientists have been at work building specialized video and audio recording devices. They aim to capture millions of whale codas and analyze them. The hope is to expose the underlying architecture of whale chatter: What units make up whale communication? Is there grammar, syntax, or anything analogous to words and sentences? These experts will track how whales behave when making, or hearing, clicks. And using breakthroughs in natural language processing-the branch of artificial intelligence that helps Alexa and Siri respond to voice commands-researchers will attempt to interpret this information.

Tune into Secrets of the Whales, a Disney+ original series from National Geographic that premieres Earth Day, April 22, 2021, and hear about the secret culture of killer whales in our podcast, Overheard at National Geographic.

Nothing like this has ever been attempted. We've trained dogs to respond to our commands, and dolphins have learned to mimic human whistles. We've taught chimpanzees and gorillas to use sign language and bonobos to answer questions by tapping symbols on a keyboard. An elephant in Seoul, named Koshik, can even speak a few words in Korean- really.

But the goal isn't to get whales to understand humans. It's to understand what sperm whales say to one another as they go about their lives in the wild.

'They sound like Morse code'

The project started with another marine biologist and with a simple conceit: Great advances often come when top experts from different rapidly advancing disciplines collaborate.

David Gruber is also a National Geographic Explorer, but his interests have long crossed traditional boundaries. The City University of New York professor of biology and environmental science has used submarines to examine coral reefs. But he's also discovered a biofluorescent sea turtle in the Solomon Islands, found that schools of flashlight fish use their glowing light to coordinate movements, studied the molecules that make catsharks and some eels appear to glow, and built a camera to mimic one shark's view of the world. He once teamed up with a roboticist to develop a delicate six-tentacled de vice that lets researchers pick up jellyfish without harming them.

Linguists contend that even the most intelligent non-human animals lack a communication system that could be called language. But could whales prove an exception?

In 2017, while a fellow at Harvard University's Radcliffe Institute, Gruber, a diver, became fascinated with sperm whales, the largest toothed whales, after reading a book about free divers who study them. One day while listening to whale codas on his laptop, another Radcliffe fellow, Shafi Goldwasser, happened by.

"'Those are really interesting-they sound like Morse code,'" Gruber recalls Goldwasser saying. She had been hosting lectures for a group of Radcliffe Fellows on machine learning, a subfield of artificial intelligence that employs algorithms to find and predict patterns in data. Today machine learning drives everything from search engines, to home robot vacuums like Roomba, to autonomous vehicles. She urged Gruber to share the clicks with her Radcliffe group.

That group included some unusually sharp computer minds. Goldwasser is a computer scientist and one of the world's foremost experts in cryptography. Michael Bronstein, chair of machine learning at Imperial College London, created a machine learning company that he later sold to Twitter to detect fake news. The group was intrigued by Gruber's presentation. Could machine learning help humans understand animal communication?

Gruber saw an opportunity. He'd spent an eclectic career trying to get people to embrace the magic of the oceans by focusing on things he found remarkable, such as corals, biofluorescence, and jellyfish. Maybe this was the project that could ignite the public's imagination, inspiring people to revel in the mystery and wonder of the sea. "I'd had this idea that if I could get people to fall in love with jellyfish, they could fall in love with anything," Gruber says. "But there's something about whales that really taps into human curiosity."

Gruber needed to talk to someone who understood the whales. So he looked up Gero, founder of the Dominica Sperm Whale Project, which tracks whale family dynamics, and sent him an email. Gero agreed to hear Gruber out.

Linguists contend that even the most intelligent non-human animals lack a communication system that could be called language. But could whales prove an exception? Human language evolved at least partly to mediate social relationships, and Gero has shown that sperm whales lead complex social lives. (Read more about whales' cultures.)

Sperm whales have the animal kingdom's biggest brains, six times larger than ours. They live in female-dominated social networks and exchange codas in a type of staccato duet, especially when near the surface. They segregate into clans of hundreds or thousands, which identify themselves using different click codas. In a sense, clans speak different dialects. The whales also identify one another by specific click patterns, which they appear to use like names. And they learn their codas much as humans learn language, by babbling clicks as juveniles until they pick up their family's repertoire.

Through the years, Gero has identified hundreds of individuals from two large clans off Dominica. He can recognize many on sight, through unique markings on their flukes. By analyzing DNA from whale poop and skin samples, he has identified grandmothers, aunts, brothers, and sisters.

And he has kept detailed records, including thousands of exhaustively annotated recordings of clicks that described who was speaking, which clan they belonged to, who they were with, and what they were doing at the time.

That was more than enough for a test. Applying AI techniques to some of Gero's audio, Gruber's machine learning colleagues trained a computer to identify individual sperm whales from their sounds. The computer was right more than 94 percent of the time.

Excited, Gruber put together a working group to expand on this promising result. In addition to Gero and Gruber's Radcliffe computer colleagues, there is whale biologist Roger Payne, a MacArthur Award winner, who had popularized the mesmerizing songs of humpbacks in the 1960s and 1970s, helping to ignite the "Save the Whales" movement. There is Robert Wood, a Harvard roboticist who, with Gruber, constructed the jellyfish handler and whose lab has built self-folding origami and an insect-sized flying drone. And there is Daniela Rus, another MacArthur recipient and director of computer science and artificial intelligence at the Massachusetts Institute of Technology.

They agreed that, for the first time, humans might finally have the tools to begin to more thoroughly understand what animals are saying-even beings that live mostly in darkness and hunt squid a thousand feet below the surface of the sea.

The fact that these animals rely almost exclusively on acoustic information might even simplify the task. At a restaurant a few blocks from Harvard Yard, the team sketched plans for a new Apollo program, one focused on translating speech from aliens of the deep. At one point someone even suggested their work, if successful, might provide a framework for conversing with extraterrestrial life. "I kept looking around waiting for someone to laugh and saw nothing but a lot of nods," Gruber says.

Machine learning could spur breakthroughs

That doesn't mean the odds are in the scientists' favor.

We've learned a lot in the last few decades about the unique ways animals communicate. Prairie dogs vary their calls depending on whether they're being approached by hawks, coyotes, or people. They'll even produce different sounds if the person they see is tall or short, or wearing white or red. Some monkey species make distinct alarm sounds for specific dangers. They screech differently when leopards approach than they do at the sight of an eagle.

Increasingly, animal communication discoveries are assisted by AI. Through machine learning, researchers in 2016 decoded call differences between Egyptian fruit bats squabbling over food and those fighting over resting spots. Rats and mice communicate far above the range of human hearing. By transforming those sounds into sonograms and running the images through artificial neural networks loosely inspired by human brain circuitry, scientists in 2019 linked different sounds to different behaviors, such as fleeing danger or trying to attract a mate. Researchers dubbed their algorithm "DeepSqueak."

These insights are now possible because breakthroughs in machine learning have come at a lightning clip in the last decade as algorithms get more sophisticated and computer processing power explodes.

Some computer learning is "supervised," meaning scientists give algorithms examples annotated by humans to train them. By analyzing thousands of pictures labeled "cats," for example, algorithms can learn to recognize cats in other photographs.

But neural networks can find patterns in things like language without an initial assist from humans. By feeding a network millions of stories from Google News along with phrases with missing elements-"To _ or not to be"-that network was able to build a mathematical model for the language. That model then learned associations between words, for example, that "Paris" is to "France" as "Rome" is to "Italy." Such models are now a cornerstone of natural language processing, used, for example, to predict if a restaurant review on Yelp is negative or to detect spam email.

But the challenges are many. Machine translation is possible for humans in part because word associations are usually similar across languages; "moon" and "sky"

relate to each other the same way as the French words "lune" and "ciel." "With whales, the big question is whether any of this stuff is even present," says Jacob Andreas, a natural language processing expert at MIT and a Project CETI team member. "Are there minimal units inside this communication system that behave like language, and are there rules for putting them together?"

To find out, the team expects to use a host of techniques. For example, one deep network approach takes random stabs at outlining a system of rules for language. Then it checks to see if "units" of conversation meet those rules. If they don't, it makes tweaks and tries again. Computers perform "this process of tweaking-and-validating rules very quickly, repeating it thousands or millions of times to produce a set of rules that do a good job of explaining data," Andreas says.

Of course, progress depends on researchers gathering enough data. Machine learning requires vast amounts of information, but Gero's recordings only number in the thousands. Finding patterns in whale speak will likely require tens of millions of codas, perhaps more.

Plus, as Gero suspected with Drop and Doublebend, scientists believe they'll need to match communication with behavior. Is there a specific coda that shows up prior to hunting, or a sequence that gets made when whales decide to mate?

"It's the cocktail party problem," Gruber says. Scatter a few microphones around a party, and they'll pick up snatches of conversation. But watch people-tracking who touches someone's arm, who scans the room for better company-"and the whole scene starts to make more sense," Gruber says.

Revolutionizing the study of animal communication

On Monday the team unveiled the major steps it's taking toward that end. CETI leaders have established a partnership with Dominica to deploy more whale-monitoring technology in the country's waters. CETI also has been designated a Ted Audacious Project, which has linked the effort with eight major philanthropic donors interested in tackling bold ideas. The team also has received funding from the National Geographic Society.

CETI researchers already have spent a year developing a massive array of sophisticated high-resolution underwater sensors that will record sound 24 hours a day across a vast portion of Gero's whale study area. Three of these listening systems, each attached to a buoy at the surface, will drop straight down thousands of feet to the bottom, with hydrophones every few hundred meters.

This is about listening to the whales in their own setting, on their own terms. It's the idea that we want to know what they're saying-that we care.

National Geographic's Exploration Technology Lab and Wood, the Harvard roboticist who is also a National Geographic Explorer, helped design a new iteration of video camera that attaches to whales with suction cups. This camera, unlike previous versions, can withstand pressure at the depth where whales hunt, take images in near-total darkness, and record high-quality audio.

Rus, at MIT, is working on additional robotics, helping develop aerial, floating, and underwater drones that can unobtrusively record sound and video. She recently helped build a swimming robot that travels silently, mimicking the undulating tail movements of reef fish.

"We want to know as much as we can," Gruber says. "What's the weather doing? Who's talking to who? What's happening 10 kilometers away. Is the whale hungry, sick, pregnant, mating? But we want to be as invisible as possible as we do it."

Outside experts say CETI could revolutionize elements of wildlife research. Janet Mann, a Georgetown University professor who has studied dolphins in Australia for decades, says the project could be "groundbreaking for sperm whales, but also for the study of other animal communication systems as well."

Michelle Fournet, an acoustic ecologist at Cornell University, says the project addresses a key difficulty of animal research. People, including scientists, tend to see human-like patterns in animal behavior. "We see a humpback waving its pectoral fin and think they're saying hello," she says. But humpbacks are usually just being aggressive. Artificial intelligence can weed out our biases and more accurately find meaning in communication and behavior, Fournet says.

For the CETI researchers, much of the value will be in the journey of discovery itself. The Apollo mission put people on the moon, but along the way humans invented calculators, Velcro, and transistors, and they helped launch the digital age that makes this project possible. Even if CETI never cracks the sperm whale code, researchers are bound to make significant advancements in machine learning, animal communication, and our understanding of one of the world's most mysterious creatures.

And, years from now, if the structure of sperm whale vocalizations becomes clearer, the team may attempt to communicate with the whales-not to hold an interspecies dialogue but to see if the whales respond predictably. The goal would be to validate the team's assessment of sperm whale communication.

"The question comes up: What are you going to say to them? That kind of misses the point," Gero says. "It assumes they have a language to talk about us and boats or the weather or whatever we might want to ask them about."

Gruber agrees. "It's not about us talking to them," he says. "This is about listening to the whales in their own setting, on their own terms. It's the idea that we want to know what they're saying-that we care."

The National Geographic Society, committed to illuminating and protecting the wonder of our world, funded Explorers David Gruber, Shane Gero, and Robert Wood. Learn more about the Society's support of ocean Explorers.

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Groundbreaking effort launched to decode whale language

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On a crisp spring morning in 2008, Shane Gero overheard a pair of whales having a chat. Gero, a Canadian biologist, had been tracking sperm whales off the Caribbean island nation of Dominica when two males, babies from the same family, popped up not far from his boat.

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https://theabcsofdatascience.com/

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The ABCs ofData Science For future Data Scientists by current Data Scientists H is for Histogram Data Science is one of the greatest fields in the world and makes our lives much better everyday. What if you had some Data Scientists take some of the most common terms and explain it to you like you [...]

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https://www.ventusky.com/

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Ventusky - Live Weather Forecast & Radar Maps

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See the weather like never before – live weather maps powered by the most accurate models.

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http://www.dear-data.com/theproject

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Over the fifty-two weeks, the collecting of data about our lives became a kind of ritual. We would spend the week noticing and noting down our activities or thoughts, before translating this information into a hand-drawn visualization.

On the front of the postcard there would be a unique representation of our weekly data, and, on the other side (in addition to the necessary postage and address), we would squeeze in detailed keys to our drawings: the code to enable the recipient to decipher the picture, and to fantasize about what had happened to her new friend the week before.

We prefer to approach data in a slower, more analogue way. We've always conceived Dear Data as a "personal documentary" rather than a quantified-self project which is a subtle - but important - distinction. Instead of using data just to become more efficient, we argue we can use data to become more humane and to connect with ourselves and others at a deeper level.

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On the front of the postcard there would be a unique representation of our weekly data, and, on the other side (in addition to the necessary postage and address), we would squeeze in detailed keys to our drawings: the code to enable the recipient to decipher the picture, and to fantasize about what had happened to her new friend the week before.

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Dr. Canner's Presentation with Resources

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Dr. Canner's Presentation with Resources

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Case studies and examples to help students recognize and address "bull" in data.

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For those who prefer video, this case study is described in the April 5th lecture of our Spring 2017 course.
This case study provides both a great example of how Fermi estimation can help you spot bull in the wild and of how bull need not be strictly false to be highly misleading.

On December 27th, 2016, Fox ran a story about food stamp fraud in the US, and framed this as a possible reason to eliminate the food stamp program (now known as SNAP).

Seventy million dollars! Wow, that's a lot of money. Sounds like a disastrously run program, right? Maybe even worth canceling, given that we're wasting government funds on scammers "including a state lawmakers and even a millionaire"?

Well, this is where Fermi estimation comes in handy. First of all, you may not know exactly how expansive the food stamp program is, but you could probably estimate that about 10% of Americans are on food stamps - or at least that it's closer to 10% than to 1% or 100% (It's actually around 15%). Second, you're probably aware there are about 300 million people in the country. So around 30 million people are on food stamps. The actual number is about 45 million, but our estimate is plenty close for Fermi purposes.

If you are unfamiliar with the US food stamp program, you may not have a good idea of the average benefit paid to recipients annually. Still you could probably guess that it's closer to $1000 than to $100 or $10,000. (In fact, it's about $1500).

At this point, you've got enough information to see what's wrong with Fox's argument. Using your Fermi estimates, the US invests approximately

30,000,000 people * $1000/person = $30,000,000,000

in its food stamp program. That means that the fraction lost to fraud is

$70,000,000/$30,000,000,000 = 0.0023,

or around 0.2 percent. The actual fraction turns out to be less than 0.1 percent, but your Fermi estimate is plenty good to see what's going on. If there is any substantive inefficiency in the food stamp program, it certainly isn't fraud.

We don't know the average amount stolen by each fraudulent user, but even if SNAP frauds are receiving no more than the average legitimate recipient, fraudulent actors are a tiny fraction of SNAP benefit recipients. I'm assuming that Fox News is correctly reporting a $70 million dollar loss to food stamp fraud. But the notion that this might provide a motivation for canceling the program is utter bull. It would take an exceptionally punitive mindset to starve nine hundred and ninety-nine people in an effort to safeguard ourselves against one petty crook.

Update: Philip Bump at the Washington Post noticed the same flaw in the argument, and presents additional context in this post.

Another update: It looks like we were too generous in assuming that Fox News was accurate with its $70 million figure. The Department of Agriculture demanded a correction to the story, and Fox acquiesced. Amusingly, the $70 million figure that Fox used to argue that the foodstamp program is inefficient is far lower than the actual loss to fraud.

We can imagine the original writing procecss going something like this:

"Hey dude, what's a big, big number?"
"I dunno, how about [Dr. Evil voice] one million dollars?"
"No, even bigger."
"How about 77 million dollars?"
"Yeah, that's more like it! Thanks bro."

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For those who prefer video, this case study is described in the April 5th lecture of our Spring 2017 course. This case study provides both a great example of how Fermi estimation can help you spot bull in the wild and of how bull need not be strictly false to be highly misleading.

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https://concord-consortium.github.io/codap-data/

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To open a CODAP Example document, click on the title or drag the "Embeddable Link" into CODAP. To open a new CODAP document, click here

© Copyright 2017 The Concord Consortium. All rights reserved.

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To open a CODAP Example document, click on the title or drag the "Embeddable Link" into CODAP. To open a new CODAP document, click here © Copyright 2017 The Concord Consortium. All rights reserved.

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https://www.societyforscience.org/research-at-home/large-data-sets/

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Research at Home: Large Data Sets - Society for Science

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Mountains of data are at your fingertips and can be analyzed in new ways for your at-home research project Locate a data set that interests you, see how others students have used large data sets in their research, and learn about current scientific studies fueled by big data. Here you will find data…

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https://datanuggets.org/resources/data/

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Datasets: Collection of websites that offer freely available online data.

Long Term Ecological Research (LTER) Datasets - Funded by the NSF. Data can be searched through their portal.

Environmental Data Initiative (EDI) - Contains data and metadata from publicly funded research. Data can be searched through their portal.

Dryad Lab - collection of free, openly-licensed, high-quality, hands-on, educational modules for students to engage in scientific inquiry using real data. Data contributed to the Dryad Digital Repository directly from scientists who collected it.

Climate Change and Phenology Datasets

Paleoclimatology datasets from NOAA - Over 10,000 available paleoclimatology datasets derived from natural sources-such as tree rings, ice cores, corals, and ocean and lake sediments. Students can use these datasets to study ancient climates, prior to the widespread availability of instrumental records.

Lilac flowering phenology: Excel file of long-term trends of flowering time in two species of lilac. Also available here.

Project BudBurst: Hosted by Chicago Botanic Gardens participants make careful observations of the timing of leafing, flowering, and fruiting phases of plants throughout the year.

Ice cover duration for Michigan lakes: Collected by Michigan State scientists showing long-term trends of ice cover in Gull and Fair Lakes.

Nature's Notebook: A national plant and animal phenology observation program. Students can contribute their phenology data as citizen scientists.

ebird: A global bird observation program through Cornell University, students can become citizen scientists and enter data on local bird counts

New Zealand air temperature data (1981-2015)

Hubbard Brook Experimental Forest Datasets

Amazon Large Datasets Repository

USGS Lake Erie Biological Station - Western Basin Trawl Survey

Data USA - Visualization of US Public Data

Google Public Data Explorer

Data & Data Visualization Tools: Collection of websites that offer free platforms for data visualization.

DataClassroom - our partner on Digital Data Nuggets, activities where studies easily explore large datasets and make beautiful graphs, develop their data literacy abilities, do statistics, and more.

FieldScope by BSCS - interactive online platform that provides citizen scientists with the means to collect and analyze data. Emphasizes spatial citizen science datasets. Allows users to visualize data both spatially and graphically.

Common Online Data Analysis Platform (CODAP) by Concord Consortium - easy-to-use web-based data analysis platform, geared toward middle and high school students, and aimed at teachers and curriculum developers. Designed to help students summarize, visualize and interpret data, advancing their skills to use data as evidence to support a claim.

Tuva Labs - online research-based tools and inquiry-based tasks enhance students' learning and application of essential mathematical, statistical, and probability concepts. Subscription necessary to access materials.

Harvard Forest Schoolyard LTER Program - A platform developed to easily visualize the data collected by students at the Harvard Forest. The graphing tool can be found here. The data can be downloaded here.

Serenity - easy-to-use web-based data analysis interface for data analytics in R, built using Shiny.

Laboratory for the study of exoplanets (ExoLab) - Free, online astronomical laboratory. Based on cutting-edge astronomy research and provides students with data from telescopes. Designed to increase study data literacy, while engaging them in the search for habitable worlds beyond earth.

NASA Earth Observations (NEO) - Here you can browse and download imagery of satellite data from NASA's constellation of Earth Observing System satellites. Over 50 different global datasets are represented with daily, weekly, and monthly snapshots, and images are available in a variety of formats including JPEG, PNG, Google Earth, and GeoTIFF.

Data USA - Visualization of US Public Data

TerraScope - worldwide environmental and social demographic data in a manipulative digital platform that can drive inquiry investigations (TerraPopulus datasets synthesized from a variety of sources for broad application).

iDigBio - large set of digitized natural history collections, along with educational resources and ideas for classroom use.

TinkerPlots - software for dynamic data exploration

StatKey - collection of web-based statistics apps, written to pair with the statistics textbook "Statistics: Unlocking the power of data" by Lock^5. Has datasets available or you can upload your own.

Dryad Lab - collection of free, openly-licensed, high-quality, hands-on, educational modules for students to engage in scientific inquiry using real data.

Using Data in the Classroom Modelling

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Datasets: Collection of websites that offer freely available online data. Long Term Ecological Research (LTER) Datasets – Funded by the NSF. Data can be searched through their portal.

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https://data.world/datasets/students

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There are 51 students datasets available on data.world.

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Find open data about students contributed by thousands of users and organizations across the world.

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https://chartio.com/learn/charts/bubble-chart-complete-guide/

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What is a bubble chart?

A bubble chart (aka bubble plot) is an extension of the scatter plot used to look at relationships between three numeric variables. Each dot in a bubble chart corresponds with a single data point, and the variables' values for each point are indicated by horizontal position, vertical position, and dot size.

The example bubble chart above depicts the points scored per game by teams in the regular season of the National Football League in 2018. Each bubble represents a single team's performance. A bubble's horizontal position notes the average points scored against that team each game, and the vertical position notes the average points scored by that team each game. Each bubble's size indicates the number of wins earned by each team, with larger bubbles corresponding to higher win rates. (Ties are worth half a win.)

From the plot, we can see that there is a lot more variance in points scored by teams than by their opponents, but there's no particularly strong correlation between the two. Instead, the main takeaway from the plot comes from the third variable: as teams score more points and allow fewer points from their opponents (towards the upper left), they will earn more victories, as one might naturally expect.

The name "bubble chart" is sometimes used to refer to a different chart type, the packed circle chart. This is a completely different chart type that will be discussed briefly towards the end of the article.

When you should use a bubble chart

Like the scatter plot, a bubble chart is primarily used to depict and show relationships between numeric variables. However, the addition of marker size as a dimension allows for the comparison between three variables rather than just two. In a single bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z), as well as an overall three-way comparison. It would require multiple two-variable scatter plots in order to gain the same number of insights; even then, inferring a three-way relationship between data points will not be as direct as in a bubble chart.

The three scatter plots above show the same data as the original example bubble chart. While it is easier to get the specific win counts for each team from this series of plots, the relationship between all three variables is not as clearly stated as in the bubble chart.

Example of data structure

A bubble chart is created from a data table with three columns. Two columns will correspond with the horizontal and vertical positions of each point, while the third will indicate each point's size. One point will be plotted for each row in the table.

Best practices for using a bubble chart

Scale bubble area by value

One easy mistake that can be made is to scale the points' diameters or radii to the third variable's values. When this kind of scaling is performed, a point with twice the value of another point will end up with four times the area, making its value look much larger than is actually warranted.

Instead, make sure that the bubbles' areas correspond with the third variable's values. In the same scenario as above, a point with twice the value of another point should have sqrt(2) = 1.41 times the diameter or radius so that its area is twice the smaller point's.

Depending on how you are creating your bubble chart, you may need to scale your data to account for how data values are mapped to point sizes. Many visualization tools will automatically match value to area, but be careful of those cases where value is matched to diameter or radius instead.

Limit number of points to plot

Bubble charts are commonly drawn with transparency on points since overlaps are a much easier occurrence than when all points are a small size. This overlapping also means that there are limitations to the number of data points that can be plotted while keeping a plot readable.

Without transparency, the smaller data point would not have been visible against the larger ones.

There aren't any hard guidelines for whether a dataset is appropriate for a bubble chart or not, but it's a point to be aware of when creating a bubble chart. If there appears to be too much overplotting, then it might be worth thinking about a way to summarize the data or choose a different chart type to represent your data. Reducing bubble size can help provide some physical separation between points, but doing so will also make it more difficult to read values from bubble sizes.

Include a legend

As another tip, it's recommended to include a legend or other key on your plot to show how different bubble sizes correspond with values of your third variable. It is fairly easy to evaluate and compare values based on horizontal or vertical lengths and positions, thanks to the tick marks on the axes. A key for bubble sizes serves the same purpose as those tick marks for the third variable.

If you are using a visualization application with interactive capabilities, it can be a good idea to turn on the feature so that values are visible when individual points are selected or hovered over. For print, it is a good idea to label key points to improve a bubble chart's communication abilities.

Present a clear trend

If you are thinking about using a bubble chart to present information to other people, make sure that it is able to present a clear trend with its use of point size as an indicator of value. When developing your chart, experiment with the order in which variables are plotted. The two most important variables or the most important relationship should end up on the vertical and horizontal axes. Avoid using a bubble chart if the third variable does not contribute significantly to the story told by the chart, and use additional, simpler plots instead.

Incorporating negative values

If a variable takes negative values, then it cannot be directly assigned to point size as an encoding: after all, how can a shape have a negative area? Additional information needs to be encoded into shape size in order to indicate negative values. For example, you might have filled circles indicate positive values and unfilled circles indicate negative values. As another alternative, you might have positive points in one color, and negative points in a distinct, different color.

Of course, it's a good idea to check that such encodings make sense in the first place: the variable might be better off assigned to one of the positional axes instead! Try to avoid encoding negative values with bubbles unless it truly adds value to the plot.

The bubble chart is, of course, built upon the scatter plot as a base, just with the addition of a third variable through point size. It's worth mentioning, however, that third variables can be added to scatter plots through other point encodings. Most common among these is color. When we have a categorical third variable (taking discrete values that may or may not be ordered), we can assign a distinct hue to each category of points. It is actually possible to use hue as a fourth variable in conjunction with point size, but this should be used carefully since it can result in information overload - the earlier cautions regarding presenting a clear trend are magnified greatly with a fourth variable.

Color can also be used as an encoding for numeric variables. If we have a color palette where colors have a continuous relationship (e.g. light to dark), we can use color to indicate value for a third variable, rather than size. Note that perception of value based on color has similar limitations as using size, so a legend is just as necessary when using color as it is for point size.

Bubble map

If the two positional variables represent geographical coordinates (i.e. latitude and longitude), we can overlay bubbles over a map in the background and get a bubble map. A bubble map is an interesting extension of the scatter map that can help with the latter's potential issues with overplotting. If a scatter map would have so many points in a region that their number would not be easily visible, we might swap them out with a single bubble that reports the total number of points within the region.

While this example from Craigslist's rental search doesn't have the standard bubble scaling, it still demonstrates how bubbles can help summarize potentially densely-packed points.

Packed circle chart

Packed circle charts (aka circular packing, bubble cloud) are a chart type that can look like a bubble chart on its surface. While bubbles in a packed circle chart indicate numeric values or frequencies like before, this is the only variable present: the bubbles are clustered together in a dense arrangement without any real positional axes.

In a way, you can think of a packed circle chart as a bar chart made of discs. However, this exposes the packed circle chart's weaknesses: like the bubble chart, it is difficult to get exact values or a ranking from the unordered bubble sizes. You'll usually be better off sticking with a bar chart, lollipop chart, or dot plot to convey information due to their use of position to encode value. The one advantage that packed circles have is that, if there are lots of groups to plot, the circular packing can be much more compact than showing each category in a long line. However, you could also lump smaller values into an "other" group to reduce space in a more conventional chart.

Most often, circular packing tends to appear in a hierarchical context, where smaller circles are placed inside larger circles to show how a whole is divided into parts at multiple levels of division. Even here, the circular form for proportions is somewhat inefficient compared to other chart types like the treemap, so the circular packing chart's advantage is firmly in aesthetics rather than practicality.

Visualization tools

Depending on the visualization method, bubble charts are sometimes considered their own chart type, but at other times are created through an overarching scatter plot option. Be careful about how certain tools interpret values to be encoded by point size: if they interpret values as dot radii or diameters, you will need to perform transformations in order to make sure point sizes are representative of true values.

The bubble chart is one of many different chart types that can be used for visualizing data. Learn more from our articles on essential chart types, how to choose a type of data visualization, or by browsing the full collection of articles in the charts category.

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A bubble chart (aka bubble plot) is an extension of the scatter plot used to look at relationships between three numeric variables. Each dot in a bubble chart corresponds with a single data point, and the variables' values for each point are indicated by horizontal position, vertical position, and dot size.

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https://www.lib.ncsu.edu/teaching-and-learning-datasets

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The NCSU Libraries provides access to datasets for use in teaching, learning, and research. Sage Research Methods Datasets and Data Planet are only available to NC State faculty, students, and staff. All other resources are public.

Datasets for Teaching and Practicing

Sage Research Methods Datasets - This collection of practice datasets contains over 120 datasets using data from real research. The collection is designed to support the teaching and learning of data analysis techniques and research methods.

Tableau Sample Data Sets - A changing sample of datasets for use in teaching and learning.

Kaggle Datasets - A collection of datasets for predictive modeling and machine learning.

CORGIS Datasets Project - Real-world datasets for subjects such as politics, education, literature, and construction.

Collections of Datasets

Data-Planet - A collection of statistical datasets from public, private, and commercial sources. The Data-Planet Search Guide provides information on how to use the collection.

DataHub.io - A collection of datasets that includes lists of countries, populations, geographic boundaries, economic data, and more.

re3data.org - registry of research data repositories

Web of Science Data Citation Index - access research data from multiple disciplines worldwide

Open Data

National/International

Awesome Public Datasets - This curated list of datasets is arranged by discipline; the majority of the datasets are free.

Data.gov - The home of the U.S. Government's open data.

FedStats - This site provides access to the full range of official statistical information produced by the U.S. Government without having to know in advance which Federal agency produces which particular statistic.

UNdata - a portal for the United Nations' statistical datasets.

North Carolina
FOR MORE INFORMATION OR ASSISTANCE, MEET WITH A LIBRARIAN OR ASK US.

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The NCSU Libraries provides access to datasets for use in teaching, learning, and research. Sage Research Methods Datasets and Data Planet are only available to NC State faculty, students, and staff. All other resources are public. Sage Research Methods Datasets - This collection of practice datasets contains over 120 datasets using data from real research.

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https://gseacademic.harvard.edu/~willetjo/nces.htm

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The website at the National Center for Education Statistics () is remarkable. Public-use NCES datasets, with electronic codebooks and data-analysis systems, are available free. Some datasets can be downloaded directly on-line, while others are sent to you on a CD-ROM in the mail, on request.

NCES also provides prompt and friendly user-support for its data products. Information about each dataset is provided on the web, often along with summary statistics. Each dataset has a support person to respond to your phone or email questions. For most datasets, NCES also maintains a database of FAQs, technical reports, substantive findings, users' names and affiliations, etc. It even gives away software (called DAS) for obtaining summary statistics interactively from the datasets or for conducting statistical analyses that take account of the complex survey sample design (called AM).

In the following list, I provide annotated links to the most useful NCES datasets in the assessment of educational progress, early childhood development, elementary and secondary education, postsecondary education, and international education.

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The website at the National Center for Education Statistics () is remarkable. Public-use NCES datasets, with electronic codebooks and data-analysis systems, are available free. Some datasets can be downloaded directly on-line, while others are sent to you on a CD-ROM in the mail, on request.

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https://www.inaturalist.org/projects/global-insects

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This project simply provides an easy way to visualize global insect stats on iNaturalist. Project banner image from https://www.inaturalist.org/observations/24989861

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Where is computer science Taught in California?

Find out about computer science access in your community by using the CS Equity Dashboard. Click on your county or enter your district or school below.

Please note this data is currently being updated and may be out-of-date. If you spot any inaccuracies, please send us an email.

Take action to advocate for computer science
education equity in your school now!

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Computer Science for California - The Data

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California is falling behind in computer science education and the data to prove it has some shocking statistics. Learn more about the current state.

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Stella Fayer, Alan Lacey, and Audrey Watson

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Science, technology, engineering, and mathematics (STEM) occupations: past, present, and future

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Science, technology, engineering, and mathematics (STEM) occupations: past, present, and future