Visual Apps in Data Science Education: Lowering the Threshold for Undergraduate Students

The growing complexity of data science (DS) education calls for interactive and innovative teaching methods to improve student engagement and conceptual understanding of mathematical and algorithmic concepts. Traditional lecture-based instruction often struggles to effectively communicate these topics, highlighting the need for dynamic, exploratory learning tools. This paper investigates whether the use of interactive tools in lectures can improve the comprehension of students learning the functioning of DS algorithms. To address this question, two interactive applications were developed using Shiny for Python. While Shiny for R is well-established in academia, Shiny for Python, a newly stabilized web framework, has received little attention in educational research yet. This study systematically evaluates the potential of Shiny for Python in teaching operations research (OR) and algorithm visualization (AV) through two interactive applications: OptiSense, which focuses on linear programming (LP) and sensitivity analysis, and PathSolver, a Dijkstra AV tool that enables step-by-step exploration of shortest-path problems. Both applications were developed using Design Science Research (DSR) and assessed through undergraduate coursework, comparing their effectiveness against traditional teaching methods. The results indicate that interactive tools enhance student engagement, comprehension, and problem-solving abilities by allowing learners to experiment, visualize, and dynamically analyze mathematical models. This research presents one of the first systematic investigations of Shiny for Python in higher education, demonstrating its potential to reduce cognitive barriers and foster an active learning experience in DS education.