Diverse Perspectives on Including Data Science as a Computational Thinking Skill for Young Children

• In Event: Computational Thinking in Early Childhood: The State of Our Burgeoning Field

Abstract

A set of converging educational priorities has prompted the need to integrate computational thinking (CT) into STEM disciplines. As a distal goal, the need for highly skilled workers in STEM fields that include a 21st century data-capable workforce (Big Ideas for Future NSF Investment, NSF 2019), has led to the growing interest in computational thinking (Apone et al., 2005; Israel, Pearson, Tapia, Wherfel, & Reese, 2015), spurring recent efforts to prepare students from the early grades through high school by developing curricula to foster CT in preK–12 education (NSF, STEM+C solicitation).

Although definitions for CT vary, in essence it is a problem-solving process requiring the use of data. ISTE and CSTA (2011) describe CT as formulating problems in such a way that enables the use of technology to help solve them, collecting, organizing and analyzing data logically, and representing data through models and simulations. Specifically, we contend that one approach to supporting the development of CT in preK includes asking questions and investigating the answers through collecting, organizing, representing, and analyzing data with the goal of efficiently addressing real-world problems (ISTE & CSTA, 2011; Barr & Stephenson, 2011).

States and districts have begun to include standards for 4- and 5-year-olds that relate to data collection and analysis (DCA), CT, and general problem solving (e.g., NYS Prekindergarten), so there is an urgent need to ensure that teachers are able to facilitate learning in these domains in a developmentally appropriate and fun way for young children. Yet, very little research exists on explicitly integrating CT into data collection and analysis (DCA) in preK or in the early grades.

This paper will discuss what is known about data collection and analysis learning in the early years and describe how it overlaps and integrates with CT, mathematics, and science. Within this larger discussion, we will describe an exploratory project that develops a DCA intervention. The intervention provides opportunities for preschoolers to leverage their growing mathematical knowledge (i.e., counting, sorting, classifying, comparing and contrasting) to engage in investigations that are hands-on and play-based.

A key component is a tablet-based, teacher-facing digital app that supports the collaboration of preschool teachers and children in collecting data, creating simple graphs, and using the graphs to answer authentic questions. The app scaffolds this process by supporting teachers as they moved through specific DCA steps, such as identifying research questions, collecting data, creating simple representations (i.e. graphs and tally charts), and discussing and interpreting the graphs to answer questions. Investigations include curricular investigations, scaffolded teacher-generated investigations, and an emergent theme-based investigation that results in the creation of a short, narrative story about the students’ research question and DCA process. Data from pilot studies will describe what we have learned about the intervention’s developmental appropriateness, feasibility, and identify what supports teachers need to implement DCA activities, and how this might apply more broadly.

Overall, we will situate the inclusion of data sciences within the umbrella of CT and describe the diverse perspectives related to how and whether data science is included as a CT skill for young children.

Authors

• Ashley E. Lewis Presser, Education Development Center, Inc.

• Jessica Mercer Young, Education Development Center, Inc.