The rapid advancement of artificial intelligence and its increasing societal impacts have turned many computing educators' focus toward early education in machine learning (ML). Limited options for educational tools for teaching novice learners about the mechanisms of ML and data-driven systems presents a recognized challenge in K-12 computing…

In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…

In this study, the authors analyzed data from a sample of thirty-two middle school students from an urban school district in the southeastern United States who used MIT's App Inventor to design, create, and remix mobile apps during an afterschool program for one school year. This paper focuses on computer science learning outcomes as measured by…

This paper explores how minoritized Computer Science (CS) students articulate their sense of critical agency to positively impact the world around them, both for today and the future, when participating in a Girls Who Code program focused on ethics, equity, and underrepresentation in computing. Observations, interviews, and surveys were conducted…

The integration of artificial intelligence (AI) topics into K-12 school curricula is a relatively new but crucial challenge faced by education systems worldwide. Attempts to address this challenge are hindered by a serious lack of curriculum materials and tools to aid teachers in teaching AI. This article introduces the theoretical foundations and…