Investigating elementary students’ experiences and perspectives with data science education
Bibliographic Data
| ID | 21348523 |
|---|---|
| Authors | Ibrahim Oluwajoba Adisa (0000-0003-1657-2030, Stanford University, corresponding author), Danielle Herro (0000-0002-1268-816X, Learning Sciences, Clemson University), Jeremiah Nosakhare Akhigbe (0000-0001-7410-6279, Learning Sciences, Clemson University) |
| Year | 2026 |
| Pages | 1-13 |
| Publication date | 2026-06-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Educational Research (JOURNAL) |
| Journal identifiers | ISSN: 0022-0671 • E-ISSN: 1940-0675 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00220671.2026.2686788 |
| OpenAlex | W7164924976 |
| Language | EN |
| References cited | 36 |
This study investigates how elementary students experience and perceive data science instruction in interest-driven and contextually relevant learning environments. We focused on fourth- and fifth-grade students (ages 8–11) who participated in data science units co-developed by educators and researchers over two years. Drawing on qualitative data from 214 students in a rural STEM-focused elementary school, we examined how students engaged with data science concepts, tools, and practices. Findings revealed that students conceptualized data science as both an analytical and narrative process of collecting, organizing, and visualizing data to tell evidence-based stories. They described data science as a space for discovery and creativity, expressing enjoyment in using digital tools to design and communicate their findings. Challenges included learning to use visualization tools and balancing teacher support with creative autonomy. Our findings underscore the value of incorporating children’s perspectives into the design of data science curricula and tools
Curriculum · Data collection · Data visualization · Narrative · Process (computing) · Qualitative property · Science education · Science learning · Data Visualization and Analytics · Statistics Education and Methodologies · Teaching and Learning Programming
Defining Computational Thinking for Mathematics and Science Classrooms
Integrating Data Science and the Internet of Things Into Science, Technology, Engineering, Arts, and Mathematics Education Through the Use of New and Emerging Technologies
The importance and emergence of K-12 data science
Data to decision-making
The methods of reflexivity
Identification of Problem-Solving Techniques in Computational Thinking Studies
Analyzing a teacher and researcher co-design partnership through the lens of communities of practice
A Call for a Humanistic Stance Toward K–12 Data Science Education
Coding In-depth Semistructured Interviews
| Citation velocity | historical |
|---|---|
| Highly cited | No |