2026 ASEE Annual Conference & Exposition

Over the past decade, computer science education has undergone a significant digital transformation, with interactive activities becoming a core feature of many introductory programming textbooks, designed to help students actively engage with new concepts. These interactive activities can accommodate various learning styles through differentiated instruction such as visualizations of complex algorithms, formative assessments, and advanced coding practice problems, all of which reinforce learning through reasoning. However, increased cognitive load can impact learning negatively. The optimal amount and types of interactive activities that best support learning without any negative impacts are not well established. Finding the right balance is especially important for introductory students, who are often learning to program for the first time.

This paper examines patterns of student engagement and behavior to investigate the optimal level of interactivity while maintaining a positive impact on student learning. We identify which design choices encourage active participation with a positive effect on student performance, and which may create barriers for newer students.

We focus on a widely used introductory Python textbook and select several high-enrollment sections that feature varying degrees of interactive elements, such as animations, along with formative assessments and autograded coding activities that provide immediate and meaningful feedback. Using the activity data, we analyze metrics including students' time spent, number of attempts, and completion rates to understand how students interact with sections that differ in structure and intensity. We also study the interaction patterns of students who use accessibility features in the same introductory Python textbook to understand how accessibility features impact student performance.

Interactive elements can improve engagement, but excessive interactivity may increase cognitive load and reduce effectiveness, thereby negatively influencing student behavior. By identifying engagement patterns across thousands of students, this work aims to provide practical insights into how much interactivity is most effective, guiding future digital textbook and course material design. This work offers insights on student behavior with accessible content and guidelines to create more inclusive digital material.