Expanding and Enriching K-12 Computational Thinking Pathways with Data Science Competencies

• In Event: Data Science Education: Re-Envisioning for 21st Century Teaching, Learning, and Research

Abstract

As the need for AI education has emerged as a key goal for K-12, so has the recognition that understandings of computational thinking (CT) and data science (DS)- are foundational for developing future-oriented STEM competencies. Preparing for this AI-driven future cannot be achieved with an understanding of CT and DS in isolation.Through “CSForAll: Research-Practice Partnership” initiatives, districts have built capacity in designing CT pathways that integrate CT into subjects (e.g. Mills et al., 2024). Our ongoing research addresses the need to expand these CT pathways to integrate data science with the goal of better preparing students with the skills needed to navigate a world powered by both data and computing.

Though the distal goal of our ongoing work is to develop curricular experiences grounded in real-world examples to help students develop understandings of data, CT, and AI, an early project goal is to develop mappings between CT and DS competencies that will guide new CT+DS pathways and related curricular design efforts. Two axes drive this work:

The first axis adapts Kafai and Proctor’s (2022) conceptualization of CT as encompassing cognitive, situated, and human-centered dimensions, and builds on Kafai & Grover (2025)’s re-conceptualization of CT for the age of AI, addressing the technical, contextual, and ethical imperatives of DS and ML alongside CT to foster expansive and comprehensive learning experiences for all students. The Cognitive Data Science dimension emphasizes foundational competencies, ensuring students build mastery in essential skills through competency-based instruction. This means addressing core CT concepts such as decomposition, pattern recognition, and abstraction (Grover & Pea, 2018) as well as data-driven, probabilistic CT paradigms (Tedre et al.,2021) as well as DS practices, with a specific focus on ML applications (Ng et al., 2021a; Tedre & Vartiainen, 2023). The Situated Data Science dimension embeds learning in authentic, contextualized activities that reflect students' lives and communities, drawing on sociocultural theories of learning. By integrating DS into core subjects including math, science, history, and ELA, students engage meaningfully with new technologies while connecting technical skills to personal and cross-domain experiences (Burke & Kafai, 2012; Kong et al., 2024). The Human-Centered Data Science dimension highlights the need for students to examine the societal implications of AI and data-driven technologies. Beyond technical proficiency, students must develop human-centered computational literacy to interrogate skewed preferences in automated systems, analyze fairness in data sources, and address issues of safety and privacy in the data that drive AI applications (Lee et al., 2021; Grover, 2024).

The second axis involves mapping productive integration points between existing CT pathways (Millis et al., 2024; Figure 1) & DS. For this we draw on newly released “Data Science 4 Everyone” progressions along 5 strands (data literacy & responsibility, creation & curation, analysis & modeling techniques, interpreting problems & results, and visualization & communication) across grade bands (Miller & Wilkerson, 2025) and a soon-to-be released National Academies consensus study on “Foundational Competencies for Data and Computing” in K-12 (NASEM, n.d.) that articulates competencies at the intersection and union of DS and computing.

Our presentation will present early results from ongoing CT+DS mapping and pathways efforts.

Figure 1. (https://digitalpromise.org/initiative/computational-thinking/ct-pathways-toolkit/)

Authors

• Shuchi Grover, Looking Glass Ventures

• Quinn Burke, Digital Promise

• Sharin R. Jacob, Digital Promise Global