Summary

Over the past few decades, data and computing have come to permeate almost every aspect of society, shaping our daily lives and impacting communication, politics, business and industry, health, and national security. Broadly, an understanding of data and computing is increasingly required to engage in society in general and in a wide variety of professions including but not limited to careers in science, technology, engineering, and mathematics (STEM). Increasing the number of people with literacy in data and computing has the potential to enhance civic life, facilitate learning to participate in society, and expand opportunities to improve our world. Efforts to create change in curricula to increase data and computing literacy have been ad hoc and have raised questions about how best to incorporate these topics into the existing system, given an already crowded curriculum, a lack of adequate teacher preparation, and the many other challenges currently facing K–12 education.

In response to the need for a more coherent approach to the expansion of access to opportunities to learn about data and computing, the National Academies of Sciences, Engineering, and Medicine convened the Committee on Developing Competencies for the Future of Data and Computing, focusing specifically on K–12 education. The committee was charged with identifying the competencies needed for students to navigate and succeed in the changing data and computing landscape, and with describing the role that K–12 education can play in the development of these competencies. The committee’s full statement of task can be found in Chapter 1 (Box 1-1). The charge includes responding to these questions:

• What competencies and awareness are needed for learners to develop basic literacy in data and computing?
• How are the foundational competencies for these fields related to foundational competencies in other STEM fields?
• What should relevant learning experiences look like in practice and how might these experiences be tailored to meet students’ interests and lived experiences?
• What are the implications for K–12 curricula?

This report offers guidance to program and curriculum designers, schools, districts, and states as they work to respond to the increasing demands for greater attention to computing and data and to prepare the future workforce.

EFFORTS TO INCREASE DATA AND COMPUTING LEARNING IN SCHOOLS

Recognition of the importance of data and computing is not new. Learning about data has long had a place in science and mathematics curricula. Computing courses began to appear in some U.S. high schools in the 1980s. However, the explosion of computing, and with it, access to large datasets, has made these traditional approaches to incorporating data and computing inadequate. Over the last decade, there have been focused efforts to expand attention to both data and computing in the K–12 curriculum, and there has been consistent growth in opportunities for K–12 students to engage in learning experiences and courses with a focus on computing and data. However, access to such experiences remains uneven, varying between and within states, districts, and schools.

Recognizing the need to update the K–12 curriculum to better prepare children and youth for lives that are shaped by advances in computing and data, several professional organizations and nonprofits are working to provide students with more opportunities to develop knowledge and skills in these areas. Efforts range from producing educational standards documents that call for change to curricula and courses, to citing the need for pre- and in-service teacher professional development in data and computing. These ad hoc approaches have successfully highlighted the need for updating the K–12 curriculum; however, without coordination and alignment, such efforts may lead to inefficient or contradictory changes in the education system that cause them to fall short of their goals. Alignment of efforts is also helpful for addressing the many challenges that come with adding any new topic to existing K–12 curricula. Among these are finding space in the curriculum for new topics, and ensuring the time, resources, and capacity for professional development for teachers.

FOUNDATIONAL COMPETENCIES

Data and computing are frequently used together in solving problems or carrying out computing tasks. Advances in computing have allowed for the creation of increasingly massive datasets, while advances in the nature and magnitude of data have motivated new computational architectures and techniques to work with such data. Attention to both the commonalities of data and computing and the unique features of each is needed to help students develop proficiency. In addition, many of the competencies that students need in order to engage in computing and to understand and work with data overlap with concepts and practices used in science, mathematics, and other STEM disciplines. Identifying and building on the shared competencies for data and computing and highlighting the connections to concepts and practices of STEM disciplines that are already in the K–12 curriculum have the potential to make efforts to expand access to data and computing more coherent and practical.

The committee identified a shared set of core competencies associated with a range of fields related to data and computing. To develop these foundational competencies, the committee examined existing conceptual frameworks and standards documents for computer science, data science, mathematics, statistics, science, and engineering. The committee identified areas of overlap and divergence in these documents in order to create an initial set of potential competencies. Then, through examination of the research evidence related to each potential competency and through discussion, the committee outlined seven foundational competencies for data and computing (see Table S-1 for descriptions of each competency).

• Competency 1: Problem Posing and Problem-Solving Processes
• Competency 2: Producing and Working with Data
• Competency 3: Abstraction, Algorithmic Thinking, and Automation
• Competency 4: Probabilistic and Inferential Reasoning
• Competency 5: Models and Representations
• Competency 6: Technology and Society
• Competency 7: Data and Computing Systems

The foundational competencies identified by the committee build upon concepts and practices that are already present in K–12 curricula. Current frameworks and standards for both mathematics and science include many connections to these data and computing competencies. In fact, the competencies for data and computing are not meant to replace existing content areas. Rather, they connect to, extend, and enhance the work that students do across the school curriculum, and represent areas where lessons can help to prepare students for engaging with data and computing

TABLE S-1 Overview of the Competencies for Data and Computing

in their future education and throughout their lives. Engaging with these competencies beginning in elementary school and continuing through high school can help all K–12 students to achieve understanding related to data and computing that can better prepare them to navigate the increasingly complex and technological world.

New advances in data and computing such as artificial intelligence (AI), machine learning, big data, quantum information science, and related emergent technologies and topics are currently receiving significant attention in many spheres. This raises important questions about what K–12 students need to learn about these contemporary and emerging topics and technologies. Many K–12 students and teachers are now using generative AI, and

this presents an opportunity for lessons and discussions in schools on the ways that AI models work, practical and ethical issues related to use of AI, and ways to evaluate and assess the validity of AI outputs.

RECOMMENDATIONS

Currently, students rarely have an opportunity to develop competencies in data and computing in a systematic way because many experiences are short, targeted exposures, particularly in grades K–5. Research evidence shows that the best way to support learning is through lessons that occur over time across subjects and grade levels and that connect to previous student experiences. This approach can provide coherence across grades and allow students to engage with progressions of relevant material of increasing depth and complexity over time. We present here the committee’s recommendations on the implications for K–12 curricula based on the committee’s vision for what learning experiences should look like in practice. The 14 recommendations are organized into three categories: Adding Data and Computing to K–12 Education (Recommendations 1–4 are primarily for state and local education agencies, and Recommendations 5 and 6 are primarily for curriculum developers), Supporting Teachers of Data and Computing (Recommendations 7–9), and Transforming the System (Recommendations 10–14).

Adding Data and Computing to K–12 Education

The current organization of curricula, courses, and graduation requirements in K–12 provides limited opportunities to introduce new stand-alone courses with content focused on data and computing. While data and computing courses exist, only a small percentage of students currently take these courses. A more coherent curriculum for data and computing can be designed by integrating the foundational competencies into existing school subject areas (such as mathematics, science, and social science) in multiple ways, beginning in elementary school. Integration of data and computing content into existing courses, while offering optional stand-alone courses where possible, provides a mechanism for adding new kinds of content to an already busy school schedule.

Pedagogical approaches, informed by education research, are crucial to meaningful data and computing learning experiences for students. For example, the pedagogical approach of making connections to data or computing explicit during lessons ensures that students are supported to see the ways that data and computing relate to and are used in multiple fields. Highlighting interconnections between the foundational competencies by addressing more than one competency in a single lesson or lesson sequence

can help students make connections between and among disciplinary domains. Embedding work on competencies in problems or projects that students find interesting, meaningful, and relevant and providing time for students to reflect on their own learning are pedagogical approaches that support students’ transfer of data and computing concepts and practices to other settings.

Learning experiences can be designed in ways that recognize and leverage the prior knowledge and experiences of students with data and computing and that engage students with issues of ethics and society. For example, to create experiences that are relevant to students’ daily lives, schools, and communities, lessons might build on units taught in previous grades, include examples relevant to students and the local community, incorporate ethical framing of technology uses, or be co-designed with local partners.

Recommendation 1: Data and computing are essential to work in STEM and other fields; therefore, state and local education agencies should develop plans to build data and computing into school curricula and courses in a consistent and coherent manner such that these topics are given higher priority across K–12 education. These changes should be made in a way that enhances foundational mathematics and science learning. The following principles should be considered to ensure that students have quality learning experiences:

• In all grades (K–12), learning experiences should focus primarily on integrating the foundational competencies for data and computing into existing school subjects and providing learning activities that help all students develop the competencies across all content areas.
• The opportunity to take stand-alone courses that focus on data and computing should be available to all students. Such courses, while they might be offered in middle school, may be most appropriate in high school.

Recommendation 2: When selecting curricula to adopt, state and local education agencies should prioritize curricula that help students see connections between data, computing, and other school subjects, and that provide students with explicit opportunities across multiple lessons, units, and grades to develop the foundational competencies for data and computing.

Recommendation 3: State and local education agencies, schools, and teachers should build opportunities for students to engage with data and computing across content areas starting in kindergarten in order to provide a progression of experiences that gradually build more

sophisticated knowledge to help all students use data and computing to interrogate, evaluate, and make sense of the world around them. These experiences should be during regular coursework, and topics should be accessible to all students and designed to foster success for all students.

Recommendation 4: States and local education agencies should evaluate how any new courses or requirements related to data and computing impact existing graduation requirements. Specifically, they should consider whether the new additions strengthen student learning of science or mathematics and the impact of any new additions on previous expectations for science or mathematics.

Recommendation 5: Curriculum developers and teachers who develop their own instructional materials should use the foundational competencies as a guide for articulating student learning objectives and designing learning experiences related to data and computing. This approach should be used for both integrated and stand-alone courses. Learning experiences should incorporate the following features:

• Students engage in problem-based or project-based experiences that they find meaningful.
• Students have opportunities to engage in both unplugged (hands-on, screen-free) and plugged-in (coding and other digital experiences) lessons and activities.
• Students use technological tools in ways that help them learn about data and computing (i.e., the foundational competencies), about the tools themselves, and about how to choose among tools for a specific purpose.
• Students engage in learning experiences about artificial intelligence (AI) and the opportunities, potential uses, tensions, limitations, and risks of AI.
• Students evaluate how ethics play a role in decision making related to data and computing, and reflect on the societal impacts of technology used for data and computing, including the practical implications and potential short- and long-term consequences of new and emerging technology.

Recommendation 6: Curriculum developers should design resources that support teachers in carrying out integrated learning experiences that intentionally bring mathematics, science, and engineering into courses focused on data and computing, as well as experiences that bring data and computing into mathematics, science, and engineering courses. When data and computing are integrated with other school

subjects, they should be explicitly discussed to help students improve their understanding of data and computing and to understand how they are connected to ideas in other disciplines. Developers should partner with experts in pedagogy, data and computing, and other relevant content areas in order to determine appropriate learning outcomes and to design resources to support meaningful and effective learning experiences for all students.

Supporting Teachers of Data and Computing

The principles that should guide the selection of instructional approaches for bringing high-quality data and computing content into teaching have not previously been well articulated. Guidance to support coherent integration is needed for all K–12 teachers and is particularly lacking for K–5 teachers. Many teachers will need new knowledge and pedagogical skills as well as more familiarity with data and computing in order to help students learn these foundational competencies. In-service professional development can help to improve teachers’ ability to teach the foundational competencies in data and computing, yet professional learning is not always supported by schools and districts, and high-quality teacher professional learning on data and computing is not widely available. Current preservice teacher preparation programs often lack opportunities to learn about the foundational competencies in data and computing. Potential teacher candidates often graduate from universities without the skills to facilitate data and computing instruction in K–12. Future teachers for all grades and disciplines would benefit from guidance on how they can support students in working with the foundational competencies. STEM methods courses that include experiences designed to integrate computing and data into STEM teaching and learning could build teacher skills and confidence.

Recommendation 7: Professional development providers should design opportunities for teachers to experience data and computing competencies within the disciplines they teach and in the context of the curricula they are expected to implement.

• For the generalist teachers (often teachers of grades K–6), professional learning should be cross-disciplinary and model the integrated approach inherent in the competencies.
• For content-certified teachers (often middle and high school teachers), professional learning should be context specific and focus on how prioritizing engagement with data and computing in courses can enhance student learning.

Recommendation 8: Leaders of teacher preparation programs should expand opportunities for preservice teachers to specialize in computing, including pathways that leverage partnerships with computer science departments. Comparable partnerships with departments or programs that focus on data (e.g., mathematics, statistics, data science, engineering) may also be appropriate for expanding preservice teacher opportunities.

Recommendation 9: All preservice teachers need opportunities to become familiar with computing and data as part of their preparation. Schools of education and leaders of other pathways for teacher preparation need to provide preservice candidates with the following:

• Opportunities to develop familiarity with the foundational competencies and computing and data broadly.
• Experiences that prepare them to integrate computing and data, specifically the foundational competencies, into the disciplines they teach.

Transforming the System

Fully integrating the teaching and learning of data and computing into an educational system is a complex task given the current structure and constraints of schools and districts. Both short- and long-term changes are required to ensure literacy in the foundational competencies for all students so that they are prepared to navigate new digital technologies in schools and society. It is critical to begin now to prioritize a more coherent and coordinated approach among curriculum developers, advocates for change, families, and educators. Bringing about changes in policies and additional resources, particularly those related to classroom curriculum and professional learning, will likely require more time.

Technology of various kinds is key to data and computing and should be part of learning experiences for students. K–12 students, using a range of powerful digital tools, can conduct sophisticated statistical analyses, tackle complex computing problems, and utilize advanced AI systems to address real-world questions. Such experiences can help prepare them to effectively utilize data and computing. There are a variety of computational tools available for students to handle multivariate datasets, and there are curated datasets available that can make data more accessible to students when appropriate.

The committee also recognizes that while there are a variety of programming environments and several data analysis tools that can be used to allow students to engage deeply with the competencies of data and

computing, there are also valuable unplugged learning experiences that do not require the use of digital technologies. These unplugged learning opportunities support development of students’ competencies in data and computing and are especially powerful for developing conceptual understanding in grades K–8.

Recommendation 10: State and local education agencies should design measurements for student, school, and district participation in data and computing learning experiences to document offering of both integrated experiences and stand-alone courses and track outcomes for students who have those experiences.

Recommendation 11: Advocates for increasing K–12 student learning about data and computing, professional societies, and other nonprofit leadership groups should prioritize efforts to advance teaching of the foundational competencies. They should work to support state and district efforts to integrate data and computing topics into existing courses.

Recommendation 12: Efforts to elevate and integrate computing and data across K–12 will require additional investment and coordination in several areas. Funders in philanthropy, business, industry, and government, informed by schools, districts, administrators, and teachers, should consider providing support for the development of curricula, professional development, and the purchase and ongoing maintenance of technology. They should also consider providing support for teachers to take courses or learn from data and computing professionals in ways that allow teachers to develop and bring expertise in data and computing to the classroom. Investments in education should not be contingent on the procurement of proprietary technologies and/or the collection of student or teacher data.

Recommendation 13: In selecting technological tools to be used in instruction related to data and computing, administrators and educators should do the following:

• Develop policies and allocate funding to promote digital and physical accessibility, including compliance with the Americans with Disabilities Act, to allow use of the tools by all students and teachers.
• Invest in professional development so that teachers are able to use existing tools effectively and have the ongoing support to integrate new tools.

• Allocate funding for the costs of initial purchase as well as the costs of ongoing maintenance, upgrades, and technical support.
• Develop policies related to data access and student privacy.

Recommendation 14: Professional societies related to mathematics, science, data science, statistics, and computing and those focused on education in these disciplines should consider revisiting their K–12 frameworks to elevate and highlight the foundational competencies in order to facilitate the ability of educators to see the connections between frameworks from different disciplines. These organizations should also coordinate with each other and in so doing signal to their communities the urgent need for increased coherence in efforts to improve student learning.