Several efforts around the country are re-examining the skills students need to be prepared for the 21st century. Frontier digital technologies such as artificial intelligence, quantum computing, and blockchain carry the potential—and in some cases have already begun—to radically transform the economy and the workplace. Global engagement and national competitiveness will likely rely upon the skills, deep understanding, and leadership in these areas.
These technologies run on a new type of fuel: data, and very large amounts of it. The “big data” revolution has already changed the way modern businesses, government, and research is conducted, generating new information and shaping critical decisions at all levels. The volume and complexity of modern data has evolved to such a degree that an entire field—data science—has emerged to meet the needs of these new technologies and the stakeholders employing them, drawing upon an inter-disciplinary intersection of statistics, computer science, and domain knowledge. Data science professionals work in a variety of industries, and data now run many of the systems we interact with in our daily life—whether smart voice assistants on our phone, social media platforms in our personal and civic lives, or Internet of Things infrastructure in our built environment.
Students in grades K-12 also interact with these systems. Despite the vast amount of data that students are informally exposed to, there are currently limited formal learning opportunities for students to learn how to understand, assess, and work with the data that they encounter in a variety of contexts. Data science education in K-12 is not widespread, suggesting that our education system has not invested in building capacity around these new and important skill sets. A review of the NCES 2019 NAEP High School Transcript Study (HSTS) data revealed that only 0.07% of high school graduates took a data science course, and 0.04% of high school graduates took an applied or interdisciplinary data science course in health informatics, business, energy, or other field. Critically, education research informing the design, implementation, and teaching of these programs is similarly limited.
To develop a better understanding of the state of data science education research, on October 26, 2021, NCER convened a Technical Working Group (TWG) panel to provide recommendations to NCER on 1) the goals for K-12 data science education research, 2) how to improve K-12 data science education practice, 3) how to ensure access to and equity in data science education, and 4) what is needed to build an evidence base and research capacity for the new field. The five key recommendations from the panel are summarized in a new report.
- Recommendation 1. Articulate the Developmental Pathway—Panelists recommended more research to better articulate K-12 learning pathways for students.
- Recommendation 2: Assess and Improve Data Science Software—Panelists suggested additional research to assess which data analysis software tools (tinker-based tools, spreadsheets, professional software, or other tools) should be incorporated into instruction and when, in order to be developmentally appropriate and accessible to all learners.
- Recommendation 3: Build Tools for Measurement and Assessment—Panelists advocated for additional research to develop classroom assessment tools to support teachers and to track student success and progress, and to ensure students may earn transferable credit for their work from K-12 to postsecondary education.
- Recommendation 4: Integrate Equity into Schooling and Systems—Panelists emphasized the importance of equity in opportunities and access to high quality data science education for all learners. Data science education research should be conducted with an equity lens that critically examines what is researched and for whom the research benefits.
- Recommendation 5: Improve Implementation—Panelists highlighted several systematic barriers to successfully implementing and scaling data science education policies and practices, including insufficient resources, lack of teacher training, and misalignment in required coursework and credentials between K-12, postsecondary education, and industry. The panel called for research to evaluate different implementation approaches to reduce these barriers and increase the scalability of data science education policies and practices.
Given the limited evidence base informing data science education at the K-12 level, panelists expressed a sense of urgency for additional research, and for expanded research efforts to quickly build an evidence base to evaluate the promise of, practices for, and best ways to impart data science education. These transformations may carry significant implications for career and technical skills, online social and civic engagement, and global citizenship in the digital sphere.
Importantly, this report highlights more research is still needed—and soon. IES looks forward to the field’s ideas for research projects that address what works, for whom, and under which conditions within data science education and will continue to engage the education research community to draw attention to critical research gaps in this area.
Written by Zarek Drozda, 2021-2022 FAS Data Science Education Impact Fellow.