.jpg)
DS4E Brings Together Education Leaders to Define Key Data Science Learning Outcomes and Prepare Students for an AI-Driven Future
Focus group participants work to group learning outcomes into strands
On June 28th, 2024, Data Science 4 Everyone brought over 100 education leaders together at the University of Chicago to begin building a national learning progression for K-12 data science and data literacy. This event marked the culmination of five months of collaboration by 11 focus groups, working alongside national educator associations such as NCTM (math), NSTA (science), NCSS (social studies), and CSTA (computer science). The goal was to discuss, identify, and rank priority learning outcomes for data education that students should learn by high school graduation.
Participants in these focus groups included representatives from Fortune 500 companies, state education agencies from 12 states, higher-education faculty across disciplines, undergraduate data science programs, schools of education, curriculum developers, teacher educators (from math, social studies, and the sciences), and a group of exceptional undergraduate students. Each focus group was invited to present the top ten data science learning outcomes their group developed in response to the question: What essential data science and data literacy skills should all students know before graduating from high school?
In addition to presenting their individual lists of learning outcomes, each focus group spoke to the challenges they faced in creating these lists and offered their thoughts on design recommendations. The participants were advised to listen to commonalities and differences during the presentations and to take note so that those discussions could be used in the writing of these learning outcomes.
Opening Remarks
The focus group convening kicks off with a message from University of Chicago Provost Katherine Baicker
Before the focus group presentations began, Zarek Drozda, Executive Director of Data Science 4 Everyone, set the stage by reminding everyone present that their goals were not only to identify the most important data science learning outcomes but also to socialize the idea of data science education and emphasize the importance of these skills for all students, regardless of their chosen career path.
“It’s not just about the workforce. I can’t emphasize enough how there are civic, daily life, and life-long learning goals that are attached to all of these ideas.”
-Zarek Drozda, Executive Director of DS4E
University of Chicago Provost Katherine Baicker echoed this sentiment in her opening remarks, and made it clear that she believes data science skills are basic life skills that should be taught to all students: “I taught data science and causal inference in public health. And it was kind of galling to me that doctors have to take calculus but don’t have to take statistics. Because as consumers of information, being able to understand all of the facts that come our way requires an understanding of what data can tell us, what it can’t tell us, how it can be manipulated, and how to actually extract reliable information from it.”
She also commended the group for setting the goal of teaching students basic data skills early in their educational careers and noted how rare this kind of collaboration between K-12 educators and higher education is:
"We didn't really have a unified approach to [teaching data science], which is why the work that you're all doing is so important and so exciting."
-University of Chicago Provost Baicker
David Uminsky, Executive Director of the University of Chicago Data Science Institute, also commended the focus groups’ efforts to bring as many diverse viewpoints into the creation of the learning standards as possible and noted how this representation translates directly into accessibility:
“It took many decades after the founding of [computer science] to think ‘maybe we should make this field more broadly accessible.’ So this group in this room right now, which I’m very excited to be a part of and support in any way as a University of Chicago data science leader, is not waiting too long to think about the rigorous way we can broaden and increase access to data science education.”
NCTM President-Elect Latrenda Knighten addresses the focus groups
Latrenda Knighten, President-Elect of NCTM and a veteran classroom teacher, closed out the opening remarks section of the event and underlined the importance of making data science and data literacy skills interdisciplinary:
“We really want to make sure we foster that sense of interdisciplinary learning, we want to help students see the connections between different subjects and have them apply that knowledge in a lot of different contexts. Because we want them to realize that unpacking this world [of data science] allows them to uncover patterns, trends, correlations, and other useful information and use that data to make sense of the world.”
-NCTM President-Elect Latrenda Knighten
She added that teaching these skills must begin early so that students will have the best chance to succeed, whether they are interested in pursuing a career in STEM or not. “This is not a course that we’re creating for the kids that can’t handle calculus. These are things we really need people to do to keep the world running, but we have to provide our students from the beginning with the foundation.”
Key Themes from Focus Group Presentations
The student focus group presents their top learning outcomes
As the focus group presentations got underway, several key themes emerged, including the need to teach data science to students maturing in the age of AI. There was a strong consensus among the focus groups on the importance of preparing students for an AI-driven future, which includes understanding AI concepts, ethical considerations, and practical applications.
Arshi Mahajan, a first-year student at Dartmouth studying Data Science and Environmental Studies and a member of the student focus group, spoke to the importance of ensuring students are future-ready: “One of the classes that I took this past semester really encouraged us to see AI as more of a tool rather than something that’s taboo, and I think that really helped me because right now a lot of students are using AI, but not in a responsible manner. They don’t really understand how it can be used to supplement education, and as a result, they’re depriving themselves of enhancing their education with the use of AI.”
Jennifer Sayed, a researcher at Southern Methodist University and a member of the social studies - educator focus group, emphasized that students should be able to recognize inherent biases in AI and examine how AI is culturally and politically situated.
"For a functioning democracy, we as social studies teachers actually have a responsibility to make sure that our students are data literate and that they can engage in democracy, they can take civic action, that they are empowered to do these things through data literacy and data science."
Making Math Relevant
Another theme that emerged from several of the focus group presentations was the need to teach math that is both relevant and necessary in students’ lived experiences. Deidre Richardson, State Mathematics Coordinator at the New Jersey Department of Education and a member of the policy focus group, framed this as a golden opportunity for data science to grab the attention of students who may not consider themselves “math people.”
“This is an opportunity to make math relevant, taking that one step further and engaging students with datasets that are personally meaningful for them, allowing them to make sense of their world, to develop a sense of purpose, and ultimately to advocate for social change.”
Dustin Purdy, a math teacher at Dwight Global and a member of the educator - math focus group, echoed that sentiment:
“To engage our students we need to do things that are meaningful to them. So the idea of having some kind of activation, where students can activate for a claim that’s important to their community. And in order to connect with our students, we have to have this environment that’s welcoming, they have to feel a sense of belonging.”
Inclusion of Student Voices
The student focus group (from left to right: Kenzo Hubert, Joseph Gomez, Julius Cervantes, Omry Brewster, Arshi Mahajan, Nadia Bishop, Kaylin Hernandez)
One unique element of this event was the inclusion of student voices in these discussions. Kailyn Hernandez, a senior at Duke University majoring in Public Policy and a member of the student focus group, advocated for centering allowing students to self-direct their own research so that they would have more inherent investment in their work.
When asked how we can better center students in this conversation of data science education, Kailyn responded:
“Let’s not underestimate students and let's let them ask their own questions and pursue their own research.”
Arshi Mahajan echoed this sentiment, emphasizing the importance of aligning educational outcomes with students' future aspirations:
“As students, we’re always trying to look towards the future and answer that question: what are we doing after college, so we tried to create a balance between career outcomes and practical application.”
-Arshi Mahajan, Student Focus Group Participant
Creating Access and Equity
Many focus groups also spoke to how data science skills can be used to create access and equity for K-12 students. Yan Sun, Business Development Advisor at Cargill and a member of the industry focus group, emphasized that K-12 curriculum should consider different career paths of students and the fact that even students who aren’t interested in pursuing higher education need to know basic data literacy and data science skills.
Focus groups field questions from other participants in the audience
“I personally mentored a colleague in our company. He did not have a college degree, but interestingly he’s a data scientist in Cargill right now because he participated in an apprentice program.”
-Yan Sun, Business Development Advisor, Cargill
Jonathan Winstead, Principal Engineer at Collins Aerospace and also a member of the industry focus group, added that in crafting these recommendations, educators and curriculum designers need to be mindful of the digital divide and ensure that students who don’t have access to computers are still able to engage with and learn basic data literacy skills. “We assume that all students have access to the same tools and technology, but I work with a STEM program within NSBE (National Society of Black Engineers), and we teach a lot of kids that don’t have consistent internet, they’re sharing computers, some don’t have computers, some only have Chromebooks.”
Winstead suggested industry may be the ones to correct this issue.
“I think we also need to put an emphasis on the digital divide and particularly on what can industry do to help improve that situation.”
-Jonathan Winstead, Principal Engineer at Collins Aerospace
Early Education in Data Science
Several focus groups also emphasized the importance of teaching data science skills to students early in their academic careers. Brian Wright, Quantitative Foundation Associate Professor of Data Science Undergraduate Programs at the UVA School of Data Science, noted that many of the students that take his machine learning courses don’t necessarily have the foundational data science skills necessary to do creative and intuitive work in the field.
“What they don’t have is intuition about where data is generated, where it comes from, the different types of questions you can and can’t ask [AI]. How do you know which question to critically evaluate when you see a data table? That process can be learned more effectively in K-12 education and should come before technical courses.”
Future Directions
Focus groups work to pare down learning outcomes after the conclusion of the presentations
At the close of the presentation, participants were asked to review all of the presented learning outcomes at their tables and group these outcomes into strands identified at a previous NSF workshop. Each table then modified and incorporated feedback into their top 10 outcomes, particularly focusing on any outcomes they identified as missing from their list.
Moving forward, DS4E will focus on refining these learning outcomes based on the feedback received during the event. A draft public list of the learning outcomes voted on and refined by the focus groups at the close of the event is set to be released in August to the general public. During this period, DS4E will gather broader input from educators, parents, school leaders, and faculty across the country. This participatory approach aims to ensure the learning outcomes are relevant, practical, and widely accepted.
This work will culminate at the Data Science Education K-12: Research to Practice Conference, which will be held in San Antonio, Texas February 17-19, 2025. Through collaborative sessions, innovative formats, and a supportive community, this inaugural conference will offer a dynamic atmosphere for educators, researchers, curriculum developers, and school leaders to exchange ideas and co-create the future of data science education in K-12. The call for proposal submissions to the conference is open until August 1, and proposals can be submitted here.
The DS4E focus group convening marked a significant and revolutionary milestone in the journey towards creating a national learning progression for K-12 data science and data literacy. The collaborative efforts of education leaders, industry representatives, higher education faculty, students and other stakeholders have laid a strong foundation for future progress. As the initiative moves forward, the continued engagement and input from a broad range of stakeholders will be vital to ensuring that all students are prepared for a data-driven world.
In the words of Provost Katherine Baicker, “I’m really excited about this project because of the transformative effect I think it can have on the education of generations to come.”