CRII: IIS: HCC: Interplay of Emotions and Self-Efficacy of Novice Students during Programming Tasks: A Multi-Modal Approach

Computer programming is an increasingly necessary skill for computing and engineering students that is often found early in an undergraduate program. However, learning programming is a difficult undertaking that can induce a wide array of emotions that may impact students' learning and belief in their abilities to learn programming. This, in turn, may affect their persistence and eventual retention in both individual programming courses and computing-related degrees and careers.

This project looks to better understand how individual students' backgrounds and beliefs about their abilities shape their emotional responses to programming challenges, and how those responses in turn affect their beliefs. The work will lead to better models of how people learn programming and inform the way programming is taught to undergraduate students, and may be especially beneficial to students from groups who often experience more negative emotions and lower self-efficacy than students overall, including women and minority students.

In the longer term, the project will contribute towards workforce development and broadening participation in computing and STEM in general.

This project aims to use novel ideas to advance the understanding of non-cognitive factors (emotions and self-efficacy) as they relate to learning introductory programming for undergraduate students. The overarching research question is: how do students' emotions and self-efficacy beliefs interact with each other during programming tasks? The work will also tease out how that interplay varies for students across differences in gender, ethnicity, and prior experience with programming, drawing on the diverse sample provided by doing the work at one of the largest public universities in the United States.

To do this, the project team will conduct two rounds of data collection and analysis. During the first round, the validity and reliability of two data collection instruments (the Achievement Emotions Questionnaire for programming and Computer Programming Self-Efficacy Scale) will be established. In the second round the team will collect data using the validated scales, along with real-time biometric data and interviews.

Triangulating these data will help build both theoretical and computational models of the interplay between emotions and self-efficacy in the context of learning programming at the undergraduate level.

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.