This paper presents a set of in-class activities and chemistry-based lab exercises developed for a CS1 course in Python that teaches programming through scientific contexts. The course pairs context-free in-class practice with a series of chemistry lab problems that increase in complexity, prompting students to apply computational thinking and form connections across course components. Survey results indicate that students responded positively to this approach.

Introductory computer science (CS1) courses are foundational to students' computing education, yet they often rely on abstract, decontextualized problems and introduce core concepts only once. This can lead to fragile learning, where students struggle to retain knowledge or apply it in new contexts. To address these challenges, we designed a CS1 course that introduces computing twice—first through a biology lens, then through a chemistry lens. This dual-introduction, dual-context structure aims to reinforce foundational computing concepts while highlighting their relevance across scientific domains. Our results indicate that the course structure effectively supported student learning, and end-of-semester survey responses reflected strong student engagement and appreciation for the interdisciplinary approach.

In an undergraduate CS1 course, we designed two projects to help students apply their learning: one in biology and one in chemistry. In this paper, we focus on the biology project, in which students identify and visualize gene mutations using real BRCA1 gene data. The projects received positive feedback, helping students see how coding applies to real-world problems in different scientific fields, understand real-world limitations, and reinforce their programming and problem-solving skills. This multi-context approach ensures a comprehensive understanding of programming concepts and their applications.

In an undergraduate CS1 course, we designed two projects to help students apply their learning: one in biology and one in chemistry. In this paper, we focus on the chemistry project, which centers on cheminformatics, the application of computational methods to analyze and interpret chemical data, which are particularly useful in drug discovery. In this project, students filter drug candidates using the PubChem database. The projects received positive feedback, helping students see how coding applies to real-world problems in different scientific fields, understand real-world limitations, and reinforce their programming and problem-solving skills. This multi-context approach ensures a comprehensive understanding of programming concepts and their applications.