Inside IES Research

Notes from NCER & NCSER

Catalyzing Data Science Education in K-12: Recommendations from a Panel of Experts

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.

 

Leveraging Diversity of Academic Disciplines and Cultural Experiences to Advance Education Research

This year, Inside IES Research is publishing a series of blogs showcasing a diverse group of IES-funded education researchers and fellows that are making significant contributions to education research, policy, and practice. For Asian American and Pacific Islander Heritage Month, we asked researcher Mingyu Feng, a senior research associate at WestED, to discuss her career journey. Dr. Feng serves as principal investigator of the IES-funded ASSISTments and MathSpring efficacy studies, which examine the effects of intelligent tutoring systems, data-driven instruction, and formative assessment on student learning outcomes.

How did you become interested in a career in education research? How have your background and experiences shaped your scholarship and career?

When I first came from China to the United States to pursue a PhD in computer science, I wasn’t thinking of having a career in education research. My goal was to become a computer scientist who plays with algorithms and codes every day. Then, I was surprised to learn how many U.S. students trailed their peers academically in a highly developed country like America. The 2002 NAEP data indicated that only 30% of 8th graders were at or above the proficient level in reading or math. My first thought when I saw that statistic was, “There must be a way to help these students.”

I pursued a PhD in intelligent tutoring systems at Worcester Polytechnic Institute out of a desire to leverage technology to boost student learning. An intelligent tutoring system (ITS) is a computer system that aims to automatically provide immediate and customized instruction, feedback, or intervention to learners. Building ITSs requires a highly interdisciplinary field, where computer technology intersects with artificial intelligence, data mining, learning sciences, cognitive sciences, and education. As a graduate student, I developed systems and analyzed student learning data. I built upon my prior math knowledge from studying engineering and taught myself statistical modeling and learned about experimental design methods. I was also fortunate to be able to visit classrooms and work directly with educators and students to understand how critical it is for a student to receive needed support and for a computer system to be effectively integrated into an educator’s classroom routine. This experience inspired me to pursue a career in applied education research. Since then, my research career has focused on the development and research of education technologies and conducting rigorous evaluations of their impact on learning or practices in authentic education settings.

What has been the biggest challenge you have encountered, and how did you overcome the challenge?

As a first-generation immigrant, I haven’t directly experienced the U.S. K-12 and college education system and was initially less familiar with U.S. policies and practices. I asked a lot of “naïve” questions—What’s the difference between a public school and a charter school? How does a district decide the adoption of a supplemental program or a core curriculum? Does 5th grade belong to elementary school or middle school? Does a teacher or a school have discretion regarding instructional practices? It was a long and steep learning curve, but I found connecting directly with students, educators, administrators, and policymakers to be beneficial for learning about the education system. Listening to their needs, observing classrooms, and discussing research and findings in a meaningful way with practitioners provided me with the context and inspiration I needed as a researcher. When I saw an exhausted teacher running around to put out fires in the classroom, or a frustrated student staring at the computer screen, I knew there was still a long way for us edtech developers and researchers to go.

I also recognized that my cultural background and resulting perspective on education could be both a challenge and an asset. In many East and Southeast Asian cultures, Confucian ideals such as respect for elders, deferred gratification, and discipline are strong influences. Traditionally, Asian parents teach their children to value educational achievement, respect authority, feel responsibility for relatives, and show self-control. These perspectives were infused in my upbringing and influenced my approach to understanding education in the United States where diverse cultures thrive. I worked to gain perspective-taking skills to understand situations from other positions, to consider other beliefs, experiences, and viewpoints.

How can the broader education research community better support the careers and scholarship of researchers from underrepresented groups?

Early exposure to research and opportunities can be quite helpful. My doctoral training expanded my view of career options beyond academia and helped me see the value of applied research and the impact research can have on practices.

Recently, IES has advocated specifically for inclusion of emerging scholars from underrepresented groups in RFAs and during the reviewing process. That’s a great way to support scholars from these groups. Just for fun, I skimmed the list of 1,500 PIs of IES funded grants and found about 50 first or last names resembling Asian names. With acknowledgement of this less-than-rigorous approach, this very rough estimate of 3-4% suggests there are not a whole lot of IES PIs with Asian heritage. Therefore, I really appreciate the increased attention and encouragement IES has given to addressing underrepresentation in the education research community and would love to meet more scholars like me at the PI meetings.

In addition, I’d encourage project directors to think more creatively when considering institutional partnerships, building a staff team, or forming an advisory board. By including collaborators or advisors from underrepresented groups, the team benefits from the breadth of talent, perspectives, and skills that arise from diversity.

What advice would you give to emerging scholars from underrepresented, minoritized groups that are pursuing a career in education research?

My first piece of advice is to be confident and brave. Believe in yourself and in the value you bring to the table. Sometimes, this means stretching outside your comfort zone or persevering towards a goal you are passionate about. In Chinese culture, modesty is viewed as a virtue. I’ve always been told “modesty helps one go forward” and “silence is golden.” Yet, I’d encourage emerging scholars from minoritized groups to put their best selves forward and display their pride. 

My second piece of advice is to find someone you trust, a mentor or an advisor, who will show you how to navigate the field and provide guidance for your academic and career advancement. A great mentor can show you your strengths and weaknesses, encourage and advocate for you, and support your growth by creating opportunities and connecting you with collaborators. For someone from underrepresented groups, I found it is best to have someone who can speak up for you when you are not present in the room.


Mingyu Feng is a senior research associate with WestEd’s Learning and Technology team. She leads large-scale grants focused on leveraging education technologies to transform science and mathematics instruction to improve student learning.

Produced by Wai Chow (Wai-Ying.Chow@ed.gov), program officer for the Effective Instruction grant program within the National Center for Education Research.