Learning statistical concepts present a significant challenge for students in various fields due to the difficulty of connecting theoretical knowledge to practical applications. This problem is exacerbated by demotivation caused by a lack of interactivity in classical teaching methods. In addition, it should be noted that the new methodologies developed in recent years are based on explanatory videos and learning guides. The need for developing innovative learning techniques based on interactive applications that facilitate the understanding of deeper concepts becomes apparent upon entering the classroom. To address these difficulties, tools such as the Shiny R package can offer a more engaging and effective learning experience for students. The Shiny package allows the development of interactive web applications that integrate statistical analysis, simulations, and graphical displays of results in real time. Our work introduces a Shiny application designed to aid students across various degree programs in studying key concepts within the field of reliability engineering. It interactively develops the study of the reliability of a system based on the state of its components, considering configurations such as series, parallel, series-parallel, parallel-series, and k-out-of-n, and other types of redundancy. Our application allows students to engage with both simulated and real-world scenarios, enabling them to analyze system performance across varying levels of complexity. In concrete, the students can more easily understand dynamic indicators of the system performance, and visualize the results numerically and graphically. This interactive and in-real-time methodology helps students understand complex theoretical concepts and visualize in practice how statistical models work, overcoming situations of demotivation and learning difficulties. We believe that our application encourages curiosity and understanding of more complex areas, such as reliability theory.

Keywords: Reliability engineering, Shiny, interactive learning, R-cran.