9 papers · 1 filter
Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks
Victor-Alexandru PÄdurean, Kaitlin Riegel, Gweneth Barbre +7
Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computation…
The Evolving Usage of GenAI by Computing Students
Irene Hou, Hannah Vy Nguyen, Owen Man +1
Help-seeking is a critical aspect of learning and problem-solving for computing students. Recent research has shown that many students are aware of generative AI (GenAI) tools; how…
Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
James Prather, Juho Leinonen, Natalie Kiesler +12
Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and…
Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
James Prather, Brent N. Reeves, Paul Denny +11
Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction…
LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education
Juho Leinonen, Paul Denny, Olli Kiljunen +3
There is a great need for data in computing education research. Data is needed to understand how students behave, to train models of student behavior to optimally support students,…
Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing Students
Stephen MacNeil, Magdalena Rogalska, Juho Leinonen +3
Large language models (LLMs) present an exciting opportunity for generating synthetic classroom data. Such data could include code containing a typical distribution of errors, simu…