473 citations · 1.1k across the 16 of their papers we have counts for
5 papers · 1 filter
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,…
Evaluating Language Models for Generating and Judging Programming Feedback
Charles Koutcheme, Nicola Dainese, Arto Hellas +4
The emergence of large language models (LLMs) has transformed research and practice across a wide range of domains. Within the computing education research (CER) domain, LLMs have…
Evaluating Contextually Personalized Programming Exercises Created with Generative AI
Evanfiya Logacheva, Arto Hellas, James Prather +2
Programming skills are typically developed through completing various hands-on exercises. Such programming problems can be contextualized to students' interests and cultural backgr…
Benchmarking Educational Program Repair
Charles Koutcheme, Nicola Dainese, Sami Sarsa +3
The emergence of large language models (LLMs) has sparked enormous interest due to their potential application across a range of educational tasks. For example, recent work in prog…
Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge
Charles Koutcheme, Nicola Dainese, Sami Sarsa +3
Large language models (LLMs) have shown great potential for the automatic generation of feedback in a wide range of computing contexts. However, concerns have been voiced around th…