Using Electroencephalography (EEG) to Understand and Compare Students' Mental Effort as they Learn to Program Using Block-Based and Hybrid Programming Environments

In recent years, the US has begun scaling up efforts to increase access to CS in K-12 classrooms and many teachers are turning to block-based programming environments to minimize the syntax and conceptual challenges students encounter in text-based languages. Block-based programming environments, such as Scratch and App Inventor, are currently being used by millions of students in and outside of classroom. We know that when novice programmers are learning to program in block-based programming environments, they need to understand the components of these environments, how to apply programming concepts, and how to create artifacts. However, we still do not know how are students' learning these components or what learning challenges they face that hinder their future participation in CS. In addition, the mental effort/cognitive workload students bear while learning programming constructs is still an open question. The goal of my dissertation research is to leverage advances in Electroencephalography (EEG) research to explore how students learn CS concepts, write programs, and complete programming tasks in block-based and hybrid programming environments and understand the relationship between cognitive load and their learning.