Background and Context: In many countries, K-12 curricula include programming within existing STEM subjects, in particular mathematics, based on explicit arguments that everyone should learn some programming and that programming may improve students’ mathematical understanding, and implicitly acknowledging the lack of specialized computing teachers and timetable slots. While much research in this area has focused on programming in the context of geometry, an ongoing shift in probability education from classical calculations to simulation-based approaches makes combined programming and probability learning an important area to explore.

Objectives: We aim to identify behavioral patterns exhibited by students working on integrated programming and probability tasks and to assess how these patterns support/hinder students in learning programming and probability simultaneously.

Method: This paper reports on an in-depth case study of a single ninth-grade student taking part in a two-month intervention where introductory Python programming and basic probabilistic reasoning were taught simultaneously and guided by a revised PRIMM framework. The relevant data material consists of video/screen recordings and observations of the student during lessons and was analysed using Schulte’s Structure-Function based Block Model in the context of fine-grained microgenetic analysis.

Findings: The analysis revealed three recurring behaviors: (1) Informed Cycle of Adjustments, a cycle of iterative code modifications, which could lead to programming constructs being reinforced though immediate feedback at the expense of superficial engagement with probability concepts, but could also lead to strong conceptual connections between programming and probability; (2) Running Program Fallacy, where a functioning program is taken as a sign that the code is correct, potentially masking underlying errors in the probabilistic reasoning, and hence obstructing probabilistic learning; and (3) Domain-Guided Program Comprehension, where probabilistic understanding is used to understand the program logic and behavior, resulting in an increased capacity to interpret and debug programs effectively.

Implications: Our findings suggest that it is difficult for students to learn programming from scratch and probability at the same time and that it might be more efficient if students first learn basic programming before moving on to combining (possibly more advanced) programming when learning probability. Combining programming and probability could be advantageous for the latter in that it encourages active engagement and immediate feedback through simulations, although overall additional scaffolding may be required to help students achieve the intended learning outcomes in both domains.