Collaborative Research: CUE-T: Hidden Curricula-Addressing Unseen Challenges within Computer Science Education

This project explores the gaps in knowledge, skills, and experiences that students may need to gain outside formal learning environments in computer science education and seeks to understand how these gaps impact students' success. By evaluating students' success as they navigate both formal (classroom) and informal curricula (e.g., makerspaces, internships, extracurricular clubs), we will develop learner-centered solutions to support their understanding of computing concepts and their gain of skills. The significance of this project lies in its potential to make CS education more comprehensive. In addition, this project addresses growing impact of artificial intelligence (AI) in education by examining the relationship of exposure to AI (e.g., large language models) outside of the class and student success in programming environments in the classroom. Our findings will benefit society by understanding and improving the educational experiences of all students and enhancing their success in computing programs. The three-year research program will investigate gaps in CS education through three primary strands: (1) identify factors from the formal and informal curricula, which when not available to students, could pose risks to students' mental health, such as anxiety, depression, and the impostor phenomenon; (2) study students' interactions with programming environments and large language models (LLMs) outside of class to characterize effective scaffolding strategies and address technical challenges in the classroom; and (3) evaluate the impact of makerspaces on students' creativity, exploratory skills, and sense of belonging. The project's methodology combines qualitative ethnographic methods, participatory design, and quantitative experimental and quasi-experimental approaches. This project will emphasize the experiences of all students in computing, aiming to create robust learning environments. The research will provide valuable insights and guidelines to improve CS education, ultimately reducing dropout rates and enabling students' success. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.