Computer Science

Computational thinking (CT) is central to computer science, yet there is a gap in the literature on how CT emerges and develops in early childhood especially for children from historically marginalized communities. Understanding how teachers provide asset-based, culturally responsive opportunities for CT in early childhood classrooms remains largely unknown. The purpose of this paper is to share a subset of findings from a qualitative, ethnographic study that explored the ways in which early childhood teachers (ECT) learned and implemented CT using asset-based pedagogies.

Computational thinking CT is central to computer science, yet there is a gap in the literature on the best ways to implement CT in early childhood classrooms. The purpose of this qualitative study was to explore how early childhood teachers enacted asset-based pedagogies while implementing CT in their classrooms.

This chapter features intersections of art, literacy, and creative computing. As a component of STEAM, creative computing augments story creation, or storymaking (Buganza et al., 2023; Compton & Thompson, 2018), prompting learners to explore expressive meaning making as collective interactions with texts. To signify a way of teaching that supports such learning activities, we propose expressive STEM as a design principle, illustrated here with examples from an elementary school and a preservice art education program in Texas, USA.

Teach mathematics and science using materials for the weather-focused Community Collaborative Rain, Hail, & Snow Network project.

This article portrays how citizen science (CS) projects can be integrated into elementary classrooms to enhance students’ sensemaking skills and connect to real-world science problems.

The recent surge of artificial intelligence (AI) in science education has heightened interest among the NARST community—a curiosity about how technology can transform education that has lasted for decades. This growing interest is evidenced by the launch of the Research in Artificial Intelligence-Involved Science Education (RAISE) Research Interest Group in 2022 and the increasing number of AI-related studies presented at NARST conferences. Despite the growth, limited studies have shed light on how the community members integrate AI into science education and the challenges. We systematically reviewed 36 AI-related papers presented at the 2024 NARST conference to address this gap.

Prior research has explored innovative ways to introduce data science to young learners, emphasizing not only the development of data skills but also the connection of data science to students’ authentic inquiries and critical actions. Building on this foundation, this study aims to achieve two complementary goals: integrating Knowledge Building, a well-established pedagogical approach, into K-12 data science education, and enhancing students’ epistemic agency through data practices in knowledge building.

Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool.

Teachers play a central role in designing structures which encourage the development of students’ individual creative capacity and the classroom’s sense of community. We offer considerations for designing engaging and collaborative experiences in elementary and intermediate computing education.