In this project, students will explore the fundamentals of machine learning applied to a space weather example through a hands-on activity. By using the space weather domain, this lesson can also be applied to general science standards. Using Google's Teachable Machine, students create an image classification model that can recognize and differentiate between key space weather phenomena such as auroras, solar flares, and sunspots. This accessible approach introduces real-world applications of AI in science while helping students build practical skills in data analysis and pattern recognition. Students then deploy their models using a simple web application built with HTML and JavaScript. This combined assignment allows students to gain firsthand experience with advanced machine learning concepts, including model inference, confidence scores, and integrating models into digital tools. Designed for high school and introductory computer science courses, the project requires minimal technical setup and is adaptable to various levels. Students not only learn the basics of machine learning, such as how training data works, why class balance matters, and how to test with new examples, but also strengthen their coding skills by customizing and debugging their own web apps. For more advanced students, optional extension activities introduce topics such as user interface design, artificial intelligence ethics, and the impact of space weather on the technology we rely on every day.

In this lightning talk, we discuss the creation of an initial G6-12 unit that traces how fundamentals of coding have evolved in response to socio-economic-political and technological motivators. The unit employed unplugged strategies to enhance students' grasp of core computer science principles and spark deeper engagement and conceptual understanding. Lessons were designed. We designed the unit so lessons can be used alone or part of a larger experience. While these lessons were created separately and used independently, the cohort collaboratively reflected and observed that technological advancements consistently build upon prior practices and habits, revealing interconnected developments that unfold over time. The impetus for this research has also been influenced by Meadow's work on communication throughout the ages, which explains coding in a similar manner. The complexity of communications progressively increases, yet the systems developed rely on simple, repeatable patterns regardless of whether they appear in the historical timeline. Examples of these communications include smoke signals, Morse code, and spectral imaging. Even binary code, a basic language composed of only two characters (0s and 1s), enables the creation of complex and sophisticated communication technologies. For example, the computational spectral imaging lesson immerses students in binary code and data representation through unplugged pixel art activities that simulate the transmission of satellite images, illustrating how simple patterns form the foundation of complex computing systems. In addition, the lesson supports the unifying theme by connecting historical communication methods to modern coding practices, revealing the continuity of technological evolution across time.

Guided by grounded theory and using a drawing task, this study aimed to analyze how education stakeholders, including K-8 teachers and school administrators, perceive and conceptualize the work of computer scientists. Twenty-eight participants completed the draw-a-computer-scientist task and underwent a six-session summer professional development workshop at a major university in New Jersey. The content analysis of the collected drawing data revealed various stereotypical images people hold of computer scientists, especially male computer scientists. The study findings also highlighted that a few participants contemplated the traditional view of a computer scientist and demonstrated their enhanced understanding of the computer science field. Considering the existence of deep-rooted stereotypes regarding the computer science field and computer scientists, this study identified important implications for how well-designed professional development workshops can address the lack of awareness about and broaden the perspectives of the computer science field.

States are moving to adopt Computer Science (CS) education standards to help K-12 teachers adapt and integrate computing /computational thinking (CT) concepts into the curriculum. These approaches also rely heavily on training current and pre-service teachers and creating opportunities to learn CS while also managing the rigors of their education career. This poster presents elements of the collaboration between the Department of CS and Department of Teaching and Learning at Montclair State University (MSU) to bring CS to pre- and in-service educators. Here we will highlight our curriculum work and professional development (PD) series. The New Jersey (NJ) Department of Education has adopted CS Education standards for K-12 and distinctly funded curriculum development and faculty formation programs. MSU has built programs that support teachers through PD experiences in CT and CS. In the 10-month period ending March 2023, we will offer 30 PD opportunities for a CS and CS education. To date, more than 200 educators have received PD to address their educational needs regarding CS curricula.