Integrating Data Literacy Into K–12 Education · Issue 7.2, Spring 2025
In a world increasingly driven by data, the ability to understand, interpret, and make use of data has assumed a fundamental role. Yet despite its importance, data literacy remains conspicuously absent from the U.S. K–12 educational landscape. This article explores the obstacles in integrating data literacy into the educational system and proposes a multipronged approach to address these challenges.We contend that successful implementation of K–12 data literacy education necessitates comprehensive teacher preparation and professional development coupled with the integration of data literacy within curricula. Furthermore, the creation of suitable tools and data sets specifically tailored for K–12 learning is paramount. Finally, school and district leaders must champion the cause of data literacy, allocating resources and nurturing a culture that recognizes its value.Drawing inspiration from the success of large-scale initiatives such as the Moore-Sloan Data Science Environments and the National Writing Project, we present the components and conditions essential for successful advancement of data literacy education. The article concludes with a call to action inviting constituents from across various roles to unite in the endeavor of building a future where data literacy is recognized as a fundamental skill. We underscore that the widespread implementation of data literacy within K–12 education will demand sustained investment, customized local implementation, and the concerted efforts of multiple disciplines and stakeholder groups. By confronting these challenges head-on and harnessing the strategies outlined in this article, we can strive to ensure that all students are armed with the data literacy skills necessary to navigate an increasingly data-driven world.
SportsDataverse
Welcome to the SportsDataverse. We are an open-source sports data organization trying to make data and utilities more accessible for everyday users.
A first introduction to data science education in secondary schools: Teaching and learning about data exploration with <span style="font-variant:small-caps;">CODAP</span> using survey data
Abstract In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.
Living in Data: A Citizen's Guide to a Better Information Future (Paperback)
Jer Thorp’s analysis of the word “data” in 10,325 New York Times stories written between 1984 and 2018 shows a distinct trend: among the words most closely associated with “data,” we find not only its classic companions “information” and “digital,” but also a variety of new neighbors—from “scandal” and “misinformation” to “ethics,” “friends,” and “play.”To live in data in the twenty-first century
Understanding the Data in K-12 Data Science · Issue 6.2, Spring 2024
Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective, engaging, and, most critically, equitable is of paramount importance. This article presents a qualitative analysis of four curricula, focusing on the data used to introduce learners to the field of data science. The analysis uses a series of analytical lenses to evaluate the 296 distinct data sets used across the curricula and identifies trends and best practices in data set selection. The analysis includes using data collected from high school students about their interests and experiences with data to understand if and how contemporary data science curricula are tapping into students’ lived experiences to situate data science learning experiences. The findings show that the curricula use relatively recent and small data sets covering a range of topics and that learner involvement in data set selection is limited. Further, the analysis reveals gaps between the data sets used and students' self-reported interests. This work highlights the importance of data set selection, especially as it relates to supporting learners from historically excluded populations in technology fields. Finally, this article provides practical implications to assess existing curricula and advances our understanding of how to situate the field of data science in the interests, ideas, and values of today’s students.
Knowledge Creating Communities for teacher professional learning about data literacy
Data literacy is increasingly important to enable school-aged learners to make sense of a data-rich world. Educating teachers about how to develop their learners’ data literacy is of high priority. This paper presents qualitative analysis of the development of confidence and competence in teaching data literacy among teachers who are members of two Knowledge Creating Communities (KCC) encompassing 42 schools. Successful factors of the KCC were peer discussion and reflection on practice within a supportive environment, respect and reciprocity between teachers and university staff; input from experts in data, and access to high-quality educational resources including pre-prepared datasets.
ADOPTING DATA SCIENCE CURRICULA: A STUDENT CENTRIC EVALUATION
With the advent of data science as a new discipline with high demand for a skilled workforce, educators are increasingly recognizing the value of translating courses and programs that have been shown to be successful and sharing lessons learned in increasing diversity in data science education. In this paper, we describe and analyze our experiences translating a lower-division data science curriculum from one university, University of California, Berkeley’s Data8 course, to other settings with very different student populations and institutional contexts at, University of Maryland, Baltimore County and Mills College during Spring 2021. It is essential to motivate students to meaningfully take part in their journey into a data science career. We wanted to consider their perceptions and motivations to take foundation courses and next steps emerging from the foundation course. We evaluated how students were receiving the course and curriculum with the adaptations across the three institutions. Two identical surveys were administered at all three institutions, once at the beginning of the semester and then at the end to study the impact of the course and its variations on the students. The survey consisted of over 60 questions including demographics, understanding the motivations on why students were in the data science class, how they perceived data science, etc. Overall, UMBC and Mills both showed gains in students’ motivations over the semester, as seen by comparing the mean scores for questions asked at the beginning of the semester versus the end. We also saw gains in student perceptions towards Data Science at the end of the semester at UMBC and Mills. With the very large size of classes at Berkeley, we performed a weighted analysis from the UC Berkeley data with respect to race, gender, first-generation status, transfer status, and international student status drawing samples close to UMBC and MILLS. This weighted sample of UC Berkeley’s students shows a clear improvement across both sets of questions after taking the Foundations class. Our findings emphasized the importance of adapting courses and programs to existing curricula, student populations, cyberinfrastructure, and faculty and staff resources in the context of the institutions. Such adaptation can help students develop their understanding of data science career pathways and help hone their motivations which can lead to a more engaged workforce supporting the data science careers.
Academia: What Was "Digital Literacy"?
Short Shelf Lives Make For Bad Curricular Planning
Analyzing Baseball Data with R (3e)
Back in the late 1980s, as a college student, I would go to the NHL offices in downtown Montreal, and pick up their official end-of-season statistics package. This was something that was reserved for the media, but somehow I had the idea to ask, and they were kind enough to oblige. Since I was learning dBase, I then had to manually enter every piece of data in that package, probably some 20,000 discrete data values. Combining sports, numbers, and computers was a labour of love for me. Add in that in the 1980s, Pete Palmer and Bill James inspired the sabermetric revolution: my career path was set, as was that of thousands more.
Developing Competencies for the Future of Data and Computing: The Role of K-12
Skills in computing and data are critical for students to thrive in a data-driven world. While efforts to expand learning in these areas have grown, opportunities to participate are unevenly distributed and lack coherence across grades and subjects. Our new report identifies seven shared competencies for data and computing and how they align with existing STEM standards and classroom practice. Through addressing curriculum, teacher preparation, professional learning, technology access, assessment, and system-level coordination, the report calls for sustained, coordinated action to ensure that all students have meaningful opportunities to develop literacy in data and computing.
The Limits of Data
Policymakers want to make decisions based on clear data, but important factors are lost when we rely solely on data. A philosopher writes:
Introductory data science across disciplines, using Python, case studies, and industry consulting projects
Abstract Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.
Data Feminism
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to d...
Foundations of Data Science for Students in Grades K-12
On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled Foundations of Data Science for Students in Grades K–12. Speakers and participants explored the rapidly growing field of K-12 data science education, by surveying the current landscape, surfacing what is known, and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators. To support these conversations, four papers were commissioned and discussed during the workshop. This publication summarizes the presentations and discussion of the workshop.
