2026 ASEE Annual Conference & Exposition

In many engineering classrooms with programming components, students are required to master equations and syntaxes, but the bridge between theory and computation often remains missing. Interactive visualization tools provide a natural way to connect these two dimensions of learning, allowing students to see how statistical reasoning translates into computational outcomes. To address this gap, a multi-semester instructional process was designed and evaluated in “From Data to Knowledge”, an introductory statistical programming course that combines project-based learning, peer evaluation, and interactive visualization through R Shiny Apps.

Across four semesters, more than 200 students participated in a series of assignments centered on student-developed Shiny Apps that visualize and apply core statistical concepts such as hypothesis testing, confidence intervals, regression, clustering, etc. Early cohorts designed original Shiny Apps as extra-credit projects, each focused on a selected statistical method and/or dataset. These Apps were evaluated for clarity, accuracy, and functionality by the instructors, and the most well-designed were selected into a shared course repository used as instructional material for later cohorts. In the final semester of the study, enrolled students completed a mid-semester project designed to close the instructional loop. Each group selected one app from the repository, studied its logic, identified limitations, and developed an improved version. The project required an analytical report, peer-review responses, source code submission, and two short videos with one documenting the build process and another demonstrating app use, assessed on both technical accuracy and communication clarity.

A mixed methods design was used to assess the educational impact. Quantitative data included topic-aligned exam questions, project evaluations, and post-surveys measuring programming confidence and conceptual understanding. Statistical analyses using two-sample t-tests and one-way ANOVA were applied to compare outcomes across semesters and stages of app development. Qualitative data such as student reflections, peer feedback, and project documentation were analyzed to identify patterns of engagement, creativity, and knowledge transfer. Early evidence in prior analysis shows that iterative engagement with peer-created Shiny Apps strengthens students’ confidence and deepens their comprehension of statistics learning. The results also suggest that embedding student-developed Shiny Apps within a cycle of critique and revision creates a sustainable and promising model for active learning in data-focused engineering education.