Programming computational models is a key practice that supports both scientific and computational learning. Block-based, domain-specific programming environments have made this practice more accessible but may constrain what students can design compared to general- purpose or text-based languages. This poster reports on the classroom use of block unpacking-a feature that allows high-level primitives to be opened, inspected, and modified-within a domain-specific modeling environment. We implemented unpacking in a four-day high school biology unit on eutrophication, where students programmed models to represent interactions among algae, bacteria, fish, nutrients, and oxygen. From the 16 students who participated, we analyzed 36 unpacking modifications made by five focal students, classifying each as a parameter change or a code structure change, and examining whether these modifications were informed by scientific reasoning. Our findings show that unpacking let students move beyond fixed domain-specific primitives, producing diverse and more expressive models. Some introduced new mechanisms, such as photosynthesis, while others focused on optimizing or debugging simulations. Importantly, more complex edits did not always correspond to deeper scientific reasoning. This study highlights unpacking as a mid-level design strategy that can expand student agency and flexibility in block-based modeling. We discuss implications for designing programming environments and instructional supports that balance ease of entry with opportunities to engage meaningfully in computing practices.
This poster presents a practical model for transatlantic collaboration in computer science education through a blended mobility program between Karlsruhe University of Education (Germany) and Northeastern Illinois University (USA). The program funded by the European Union (Erasmus+) combines virtual and physical mobility phases, enabling students to participate in collaborative courses such as Physical Computing and Cybersecurity without interrupting their home semester. We share insights from the program's first implementation cycle, including student feedback and preliminary data on learning outcomes. This model demonstrates how EU-US cooperation can effectively support practical, handson computer science courses in a blended learning environment. Our work highlights challenges and best practices for designing sustainable, international blended mobility programs in computer science teacher education, offering valuable implications for institutions seeking to foster global collaboration in computer science education.
College instructors often allow students some form of cheatsheet during an exam, such as one page of notes and formulas. Many believe these reduce test anxiety and help students to do better on exams. Focusing on the latter, we found, in a study of two different types of computer science courses, that features we might have believed would help students, do not actually have any correlation with performance. Coupled with mixed results from the literature, we are still left with the agonizing question: what about cheatsheets are useful?
The rapid expansion of K-12 computer science (CS) education has outpaced the supply of teachers with formal CS training, leaving many educators to teach outside their expertise. This creates challenges in assessing student understanding and interpreting conceptual growth through code. To address this, we introduce ARCHIE (Assessment and Recognition of Coding Habits for Instructional Enhancement), a system that extracts conceptual patterns from student programs and visualizes them at both individual and class levels. We solicited feedback from 20 K-12 CS teachers regarding their confidence in analyzing code and their perceptions of ARCHIE. Teachers reported that ARCHIE helped make student thinking more visible, supported formative assessment, and showed strong potential to enhance instructional decision-making. These findings suggest ARCHIE could provide valuable support for K-12 CS teachers, particularly those without a computing background.
Computing programs increasingly need to prepare graduates who can apply AI in context, not just write software. We present a transferable model for an undergraduate B.S. in Applied AI that treats application domains (e.g., Business, Life Sciences) as co-equal with a rigorous AI technical core. To widen participation while maintaining depth, we replace the traditional calculus sequence with a tailored "Mathematics for AI" track. The 120-credit curriculum is anchored by a technical AI core, required co-operative education, and a culminating one-semester senior design project, with ethics explicitly threaded throughout. For the SIGCSE community, our contribution is a pragmatic blueprint for institutions that aim to broaden participation and produce integrated AI practitioners. We share our domain-based curriculum, course maps, learning outcomes, and launch domains co-developed with partner schools, with institutional adoption planned for Fall 2026.
