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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. 

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. 

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. 

There is increasing interest in broadening participation in computational thinking (CT) by integrating CT into pre-college STEM curricula and instruction. Science, in particular, is emerging as an important discipline to support integrated learning. This highlights the need for carefully designed assessments targeting the integration of science and CT to help teachers and researchers gauge students’ proficiency with integrating the disciplines. We describe a principled design process to develop assessment tasks and rubrics that integrate concepts and practices across science, CT, and computational modeling. We conducted a pilot study with 10 high school students who responded to integrative assessment tasks as part of a physics-based computational modeling unit. Our findings indicate that the tasks and rubrics successfully elicit both Physics and CT constructs while distinguishing important aspects of proficiency related to the two disciplines. This work illustrates the promise of using such assessments formatively in integrated STEM and computing learning contexts. 

There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the field of STEM and CT integration.