11 citations · 16 across the 7 of their papers we have counts for
7 papers
Oversight in Action: Experiences with Instructor-Moderated LLM Responses in an Online Discussion Forum
Shuying Qiao, Paul Denny, Nasser Giacaman
The integration of large language models (LLMs) into computing education offers many potential benefits to student learning, and several novel pedagogical approaches have been repo…
Howzat? Appealing to Expert Judgement for Evaluating Human and AI Next-Step Hints for Novice Programmers
Neil C. C. Brown, Pierre Weill-Tessier, Juho Leinonen +2
Motivation: Students learning to program often reach states where they are stuck and can make no forward progress. An automatically generated next-step hint can help them make forw…
Teaching Program Decomposition in CS1: A Conceptual Framework for Improved Code Quality
Georgiana Haldeman, Judah Robbins Bernal, Alec Wydra +1
Program decomposition is essential for developing maintainable and efficient software, yet it remains a challenging skill to teach and learn in introductory programming courses. Wh…
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…
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…
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Stephen MacNeil, Paul Denny, Andrew Tran +5
Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a…