Using Practical Programming Tasks to Enhance Combinatorial Understanding
Sigal Levy, Yelena Stukalin, Nili Guttmann‐Beck · 2024
Probability theory has extensive applications across various domains, such as statistics, computer science, and finance. In probability education, students are introduced to fundamental principles such as combinatorics and symmetric sample spaces. Students pursuing degrees in computer science possess a robust foundation in programming, software engineering, and algorithmic thinking. Hence, they enter probability courses with a unique perspective and learning potential. Despite that, these students encounter challenges in grasping combinatorial concepts. In this experiment, we challenged first-year computer science students to program a simulation of a practical combinatorics problem. Students commented on if and how this task helped them internalise the basic concepts of combinatorics. We aim to show how utilising programming tasks may empower students with a deeper grasp of combinatorics. The results presented in this paper are based on a recent paper by the authors: "Using Practical Programming Tasks to Enhance Combinatorial Understanding