16 citations · 21 across the 5 of their papers we have counts for
5 papers
Enhancing Debugging Skills with AI-Powered Assistance: A Real-Time Tool for Debugging Support
Elizaveta Artser, Daniil Karol, Anna Potriasaeva +6
Debugging is a crucial skill in programming education and software development, yet it is often overlooked in CS curricula. To address this, we introduce an AI-powered debugging as…
KOALA: a Configurable Tool for Collecting IDE Data When Solving Programming Tasks
Daniil Karol, Elizaveta Artser, Ilya Vlasov +3
Collecting data of students solving programming tasks is incredibly valuable for researchers and educators. It allows verifying that the students correctly apply the features and c…
One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks
Anastasiia Birillo, Elizaveta Artser, Anna Potriasaeva +6
Students often struggle with solving programming problems when learning to code, especially when they have to do it online, with one of the most common disadvantages of working onl…
Clustering MOOC Programming Solutions to Diversify Their Presentation to Students
Elizaveta Artser, Anastasiia Birillo, Yaroslav Golubev +4
In many MOOCs, whenever a student completes a programming task, they can see previous solutions of other students to find potentially different ways of solving the problem and to l…
Detecting Code Quality Issues in Pre-written Templates of Programming Tasks in Online Courses
Anastasiia Birillo, Elizaveta Artser, Yaroslav Golubev +4
In this work, we developed an algorithm for detecting code quality issues in the templates of online programming tasks, validated it, and conducted an empirical study on the datase…