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This paper describes an experience report centered on high school mathematics teachers’ use of ALICE, a Generative AI (GenAI) module of the Edfinity homework system. Given natural language prompts (from teachers), ALICE generates the programming code (in WeBWorK format) for the corresponding interactive, isomorphic, auto-gradable problem along with hints and a solution. Writing such code would normally require programming skills. Working with teachers in high schools across a mid-western US state, this paper presents teachers’ experiences using ALICE, on prompt engineering, and the factors that influence these experiences. The implementation study also examines the impact of this experience on teachers’ classroom practice and their views about AI. Findings suggest that teachers’ experiences were largely very positive, however these experiences are shaped by several factors including their context, their attitudes toward technology and AI use, and the perceived usefulness of the tool. These factors hold different levels of importance for individual teachers. The promising results contribute to the burgeoning field of GenAI in education and understanding teacher-AI teaming. 

Many block-based programming environments have proven to be effective at engaging novices in learning programming. However, most offer only restricted access to the outside world, limiting learners to commands and computing resources built in to the environment. Some allow learners to drag and drop files, connect to sensors and robots locally or issue HTTP requests. But in a world where most of the applications in our daily lives are distributed (i.e., their functionality depends on communicating with other computers or accessing resources and data on the internet), the limited support for beginners to envision and create such distributed programs is a lost opportunity. We argue that it is feasible to create environments with simple yet powerful abstractions that open up distributed computing and other widely-used but advanced computing concepts including networking, the Internet of Things, and cybersecurity to novices. The paper presents the architecture of and design decisions behind NetsBlox, a programming environment that supports these ideas. We show how NetsBlox expands opportunities for learning considerably: NetsBlox projects can access a wealth of online data and web services, and they can communicate with other projects. Moreover, the tool infrastructure enables young learners to collaborate with each other during program construction, whether they share their physical location or study remotely. Importantly, providing access to the wider world will also help counter widespread student perceptions that block-based environments are mere toys, and show that they are capable of creating compelling applications. In this way, NetsBlox offers an illuminating example of how tools can be designed to democratize access to powerful ideas in computing. 

This paper develops a systematic approach to identifying and analyzing high school students’ debugging strategies when they work together to construct computational models of scientific processes in a block-based programming environment. We combine Markov models derived from students’ activity logs with epistemic network analysis of their collaborative discourse to interpret and analyze their model building and debugging processes. We present a contrasting case study that illustrates the differences in debugging strategies between two groups of students and its impact on their model-building effectiveness. 

Creating pathways that stimulate high school learners’ interest in advanced topics with the goal of building a diverse, gender-balanced, future-ready workforce is crucial. To this end, we present the curriculum of a new, high school computer science course under development called Computer Science Frontiers (CSF). Building on the foundations set by the AP Computer Science Principles course, we seek to dramatically expand access, especially for high school girls, to the most exciting and emerging frontiers of computing, such as distributed computation, the internet of things (IoT), cybersecurity, and machine learning. The modular, open-access, hands-on curriculum provides an engaging introduction to these advanced topics in high school because currently they are accessible only to CS majors in college. It also focuses on other 21st century skills required to productively leverage computational methods and tools in virtually every profession. To address the dire gender disparity in computing, the curriculum was designed to engage female students by focusing on real world application domains, such as climate change and health, by including social applications and by emphasizing collaboration and teamwork. Our paper describes the design of curricular modules on Distributed Computing, IoT/Cybersecurity, and AI/Machine Learning. All project-based activities are designed to be collaborative, situated in contexts that are engaging to high school students, and often involve real-world world data. We piloted these modules in teacher PD workshops with 8 teachers from North Carolina, Tennessee, Massachusetts, Pennsylvania, and New York who then facilitated virtual summer camps with high school students in 2020 and 2021. Findings from teacher PD workshops as well as student camps indicate high levels of engagement in and enthusiasm for the curricular activities and topics. Post-intervention surveys suggest that these experiences generate student interest exploring these ideas further and connections to areas of interest to students. 

The benefits of computational model building in STEM domains are well documented yet the synergistic learning processes that lead to the effective learning gains are not fully understood. In this paper, we analyze the discussions between students working collaboratively to build computational models to solve physics problems. From this collaborative discourse, we identify strategies that impact their model building and learning processes. 

Introducing computational modeling into STEM classrooms can provide opportunities for the simultaneous learning of computational thinking (CT) and STEM. This paper describes the C2STEM modeling environment for learning physics, and the processes students can apply to their learning and modeling tasks. We use an unsupervised learning method to characterize student learning behaviors and how these behaviors relate to learning gains in STEM and CT. 

The introduction of computational modeling into science curricula has been shown to benefit students’ learning, however the synergistic learning processes that contribute to these benefits are not fully understood. We study students’ synergistic learning of physics and computational thinking (CT) through their actions and collaborative discourse as they develop computational models in a visual block-structured environment. We adopt a case study approach to analyze students synergistic learning processes related to stopping conditions, initialization, and debugging episodes. Our findings show a pattern of evolving sophistication in synergistic reasoning for model-building activities. 

Computational modeling has been shown to benefit integrated learning of science and computational thinking (CT), however the mechanics of this synergistic learning are not well understood. In this research, we examine discourse during collaborative computational model building through the lens of a collaborative problem solving framework to gain insights into collaboration and synergistic learning of high school physics and CT. We pilot our novel approach in the context of C2STEM, a designed modeling environment, and examine collaboration and synergistic learning episodes in a video capture of a dyad modeling 2D motion with constant velocities. Our findings exhibit the promise of our approach and lay the foundation for guiding future automated approaches to detecting the synergistic learning of science and CT. 

Synergistic learning of computational thinking (CT) and STEM has proven to effective in helping students develop better understanding of STEM topics, while simultaneously acquiring CT concepts and practices. With the ubiquity of computational devices and tools, advances in technology,and the globalization of product development, it is important for our students to not only develop multi-disciplinary skills acquired through such synergistic learning opportunities, but to also acquire key collaborative learning and problem-solving skills. In this paper, we describe the design and implementation of a collaborative learning-by-modeling environment developed for high school physics classrooms. We develop systematic rubrics and discuss the results of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices.