Preparing elementary-aged children to practice data science literacies is important and understudied. Our research investigates how data science curricula might be effectively designed and integrated into elementary classroom instruction. We use narrative case study methodology, focusing on a single case detailing a second-grade teacher's approach…

Theorists and educators increasingly highlight the importance of computational thinking in STEM education. While various scaffolding strategies describe how to best support this skillset (i.e., paired programming, worked examples), less research has focused on the design and development of these digital tools. One way to support computational…

Artificial intelligence (AI) has been widely adopted in higher education. However, the current research on AI in higher education is limited lacking both breadth and depth. The present study fills the research gap by exploring faculty members' perception on teaching AI and data science related courses facilitated by an open experiential AI…

Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research…

Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden…

Drawing on the scholarship of writing and learning, this article motivates the use of writing assignments in analytics courses and develops a framework for instructional design that advances both writing skills and discipline-specific learning. We translate a best practices set of foundational writing concepts into a matrix of design levers for…

Data-based decision-making is a well-established field of research in education. In particular, the potential of data use for addressing heterogeneous learning needs is emphasized. With data collected during the learning process of students, teachers gain insight into the performance, strengths, and weaknesses of their students and are potentially…

Tutorials for EXperimentalisT Interactive LEarning (TEXTILE) is an interactive semi-linear module-based curriculum for training students at various educational levels on data science methodologies currently utilized by research laboratories. We show how we developed our eleven module TEXTILE program to train 15 students from high school,…

Purpose: There is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how "sandbox" or open-inquiry data science with social media supports learning. Design/methodology/approach: This…

These proceedings are a written record of the research presented at the 44th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (PME-NA) held in Nashville, Tennessee, and virtually. This year's conference theme is "Critical Dissonance and Resonant Harmony." The aim of this…