Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
Computers in Education Division (COED)
Diversity
13
10.18260/1-2--42867
https://peer.asee.org/42867
630
Safia Malallah is a postdoc in the computer science department at Kansas State University working with Vision and Data science projects. She has ten years of experience as a computer analyst and graphic designer. Besides, she's passionate about developing curriculums for teaching coding, data science, AI, and engineering to young children by modeling playground environments. She tries to expand her experience by facilitating and volunteering for many STEM workshops.
Associate professor of computer science at Kansas State University.
William H. Hsu is an associate professor of Computing and Information Sciences at Kansas State University. He received a B.S. in Mathematical Sciences and Computer Science and an M.S.Eng. in Computer Science from Johns Hopkins University in 1993, and a Ph
Dr. Josh Weese is a Teaching Assistant Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 students.
Salah Alfailakawi is a PhD student in Educational Technology (ET) Graduate Programs at Kansas State University's College of Education. His areas of interest include social/cultural issues in ET, the impact of ET on learners and teachers, as well as pract
Although evidence suggests that children as young as four years old can develop coding and engineering projects based on data science concepts, data science is often overlooked in early childhood research, and limited resources existed slow its inclusion into this field of study. This paper proposes the Dataying framework to teach data science concepts to young children ages 4–7 years old. The framework development included identifying K–12 data science elements and then validating element suitability for young students. Six cycled steps were identified: identifying a problem, questioning, imagining and planning, collecting, analyzing, and story sharing. This paper also presents examples of data decision problems and demonstrates use of a proposed Insight-Detective method with a plan worksheet for Dataying.
Malallah, S. A., & Shamir, L., & Hsu, W. H., & Weese, J. L., & Alfailakawi, S. (2023, June), Data Science (Dataying) for Early Childhood Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42867
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