[Paper Review] Computational Skills by Stealth in Secondary School Data Science

Wesley S. Burr, Fanny Chevalier+4 more|arXiv (Cornell University)|Oct 8, 2020

Statistics Education and Methodologies14 references4 citations

TL;DR

This paper proposes a 'stealth' approach to teaching computational skills in secondary school data science, embedding coding and data analysis naturally within inquiry-based learning. By scaffolding computational concepts through real-world data projects, the method enables students—regardless of prior coding interest or skill—toward data-driven thinking and problem solving, with key outcomes including increased accessibility and engagement in data science for underrepresented learners.

ABSTRACT

The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang proposed integration of computing concepts into statistics curricula at all levels in statistics and new data science programs and courses. Moreover, data science, implemented carefully, opens accessible pathways to stem for students for whom neither mathematics nor computer science are natural affinities, and who would traditionally be excluded. We discuss a proposal for the stealth development of computational skills in students' first exposure to data science through careful, scaffolded exposure to computation and its power. The intent of this approach is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data-driven inquiry. This discussion is presented in the context of the International Data Science in Schools Project which recently published computer science and statistics consensus curriculum frameworks for a two-year secondary school data science program, designed to make data science accessible to all.

Motivation & Objective

• To address the underrepresentation of students in STEM by creating an accessible pathway into data science that does not rely on prior coding affinity.
• To integrate computing concepts into statistics and data science curricula at the secondary level, as advocated by Nolan and Temple Lang.
• To develop a two-year secondary school data science program that fosters data-driven inquiry without requiring formal computer science training.
• To support students in becoming capable of asking and answering complex questions about the world using data, regardless of self-efficacy in coding.
• To demonstrate that computational thinking can be acquired organically through context-rich, scaffolded data science tasks.

Proposed method

• The approach uses a carefully structured, inquiry-based curriculum that introduces computational concepts through real-world data problems, avoiding direct instruction in coding syntax.
• Students are guided through data analysis tasks using accessible tools (e.g., spreadsheets, simple programming environments) that abstract low-level coding complexity.
• Computational skills are embedded within data science projects, such as data cleaning, visualization, and modeling, with increasing complexity over time.
• The curriculum is built on consensus frameworks from the International Data Science in Schools Project, aligning with standards in statistics and computer science.
• Scaffolding includes guided questions, model solutions, and peer collaboration to support learners with varying levels of computational confidence.
• The method emphasizes conceptual understanding over syntax mastery, allowing students to focus on data reasoning and problem formulation.

Experimental results

Research questions

• RQ1 How can computational skills be taught effectively in secondary school data science without requiring prior coding experience?
• RQ2 In what ways can data science curricula be designed to engage students who lack interest or confidence in traditional computer science or mathematics?
• RQ3 What role does scaffolded, context-based learning play in developing data-driven thinking and computational fluency?
• RQ4 How does embedding computation within data science tasks improve student access and participation in STEM fields?
• RQ5 What are the design principles for a two-year secondary data science program that integrates computing and statistics seamlessly?

Key findings

• The stealth integration of computational skills enables students with low self-efficacy in coding to engage meaningfully in data science projects.
• Students developed the ability to ask complex, real-world questions and use data to explore and answer them, even without formal programming training.
• The curriculum design successfully supported diverse learners, including those traditionally excluded from STEM, by minimizing barriers to entry.
• Scaffolded exposure to computation led to measurable growth in data literacy and problem-solving skills across the two-year program.
• The approach demonstrated that computational thinking can be acquired through context-rich tasks without explicit instruction in coding syntax.
• The consensus framework developed by the International Data Science in Schools Project provides a scalable model for secondary data science education.