Inclusive Data Science Education for Rural Elementary Students
This Research Practitioner Partnership project is aimed at making data science education accessible to rural, elementary students, including students with high-incidence disabilities (e.g., learning disabilities, emotional/behavioral disorders) to increase participation in computer science education and broaden ways to hone computational thinking skills.
National Science Foundation; CS for All: Research and RPPs program
Golnaz Arastoopour Irgens
Visit the Project Website
Scalable and agile approaches are needed to inspire young learners to develop STEM and computer science literacies and increase interest in STEM and computer science careers. However, advancing STEM and computer science skills are particularly challenging in elementary schools where teachers often teach subjects outside of their preparation, have limited technology support, and limited computer science curricular resources. In rural areas, geographical isolation and poverty further exacerbate existing barriers, and students with disabilities struggle significantly more than their peers in STEM disciplines. As a result, opportunities to develop computer science (CS) and computational thinking (CT) skills for these students are fundamentally inequitable. This Research Practitioner Partnership project is aimed at making data science education accessible to rural elementary students, including students with high-incidence disabilities (e.g., learning disabilities, emotional/behavioral disorders) to increase participation in CS education and broaden ways to hone CT skills. The project team will accomplish this through collaborative work between Clemson University researchers and teachers in grades 4-5 from a rural school district. The researchers and teachers will work together to develop, implement, and test a model for creating and sustaining a customizable learning module that focuses on developing CT skills in a STEM context.
The team will take a Design-Based Implementation Research approach to the project, where they will iteratively co-design curricular resources and conduct research to inform revisions to the curriculum. They will also use a pop-up approach to address the need for scalable and agile data science curriculum modules. In education, pop-ups are often understood as customizable courses or units that vary in length and are implemented at various times based on student needs; they are often best suited to teaching a new skill or technology. In this project, the pop-up modules will be designed to:
- Provide local contextualized problems and issues
- Align to South Carolina’s Computer Science and Digital Literacy Standards
- Map to a research-based taxonomy of CT practices for mathematics and science classrooms (Weintrop et al., 2016)
- Appeal to young rural learners, including those with disabilities
The team will use Connected Learning Theory and the Universal Design for Learning framework to guide the curriculum development work. Through a concurrent parallel mixed-methods approach, they will investigate the key features of the co-design curriculum process; teachers’ successes and challenges during iterative implementation cycles of the data science curriculum; the impact of the project and curriculum on teachers’ confidence, self-efficacy, and interest; the impact of the curriculum on elementary students’ data and computational problem-solving practices; whether the impact is different for a student with and without disabilities; and the impact on students’ confidence, self-efficacy, and interest in data science. The project team will share a model that they develop with researchers and practitioners across the United States to improve STEM learning for students who historically have been at a disadvantage in terms of access and resources. The data science modules that the teachers co-create will be available online for teachers across the country to download and customize.