Teaching Algorithms Design Approaches via Interactive Jupyter Notebooks

Abstract

Interactive learning environments play a crucial role in facilitating effective education. Traditional approaches to teaching algorithm design techniques often lack interactivity, resulting in a limited learning experience. In this article, we propose the utilization of Jupyter Notebooks, renowned for their versatility in combining code, visualizations, and explanations, as a powerful tool for enhancing understanding and promoting an engaging learning experience in teaching algorithm design techniques. We provide a comprehensive structure for a Jupyter Notebook page, encompassing problem descriptions, brute force solutions, algorithm design techniques, application areas, and references, to present a thorough solution for computer science problems using these design techniques. Our study includes experiments conducted with computer science students, demonstrating the practical application of Jupyter Notebooks in algorithm design education. Furthermore, we share sample Jupyter Notebooks for popular algorithm design techniques, including Divide & Conquer, Greedy, Dynamic Programming, and Backtracking. Importantly, we emphasize the significance of interactive comparisons between brute force solutions and algorithm design techniques, which foster valuable learning opportunities by providing insights into performance improvements, complexity analysis, validation, optimization strategies, trade-offs, and a deeper understanding of algorithmic principles. In conclusion, we propose the integration of Jupyter Notebooks as a potent tool for teaching algorithm design approaches, empowering learners to actively engage with the material, visualize complex concepts, and collaborate effectively. By incorporating Jupyter Notebooks into algorithm design education, instructors can enhance students' comprehension, cultivate critical thinking skills, and facilitate a profound understanding of algorithmic principles and optimization strategies.