473 citations · 1.2k across the 39 of their papers we have counts for
12 papers · 1 filter
Overview of Web Application Performance Optimization Techniques
Juho Vepsäläinen, Arto Hellas, Petri Vuorimaa
During its thirty years of existence, the World Wide Web has helped to transform the world and create digital economies. Although it started as a global information exchange, it ha…
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,…
On the Opportunities of Large Language Models for Programming Process Data
John Edwards, Arto Hellas, Juho Leinonen
Computing educators and researchers have used programming process data to understand how programs are constructed and what sorts of problems students struggle with. Although such d…
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks
Leonardo Pasquarelli, Charles Koutcheme, Arto Hellas
Providing sufficient support for students requires substantial resources, especially considering the growing enrollment numbers. Students need help in a variety of tasks, ranging f…
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…
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…
Experiences from Integrating Large Language Model Chatbots into the Classroom
Arto Hellas, Juho Leinonen, Leo Leppänen
In the present study, we provided students an unfiltered access to a state-of-the-art large language model (LLM) chatbot. The chatbot was intentionally designed to mimic proprietar…
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…
Let's Ask AI About Their Programs: Exploring ChatGPT's Answers To Program Comprehension Questions
Teemu Lehtinen, Charles Koutcheme, Arto Hellas
Recent research has explored the creation of questions from code submitted by students. These Questions about Learners' Code (QLCs) are created through program analysis, exploring…
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Seth Bernstein, Paul Denny, Juho Leinonen +5
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a goo…