3 citations · 4 across the 5 of their papers we have counts for
5 papers
Can LLMs Identify Gaps and Misconceptions in Students' Code Explanations?
Priti Oli, Rabin Banjade, Andrew M. Olney +1
This paper investigates various approaches using Large Language Models (LLMs) to identify gaps and misconceptions in students' self-explanations of specific instructional material,…
Explaining Code Examples in Introductory Programming Courses: LLM vs Humans
Arun-Balajiee Lekshmi-Narayanan, Priti Oli, Jeevan Chapagain +4
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approach…
Mastery Guided Non-parametric Clustering to Scale-up Strategy Prediction
Anup Shakya, Vasile Rus, Deepak Venugopal
Predicting the strategy (sequence of concepts) that a student is likely to use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt themselves to different t…
Automated Assessment of Students' Code Comprehension using LLMs
Priti Oli, Rabin Banjade, Jeevan Chapagain +1
Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based m…
The Behavior of Large Language Models When Prompted to Generate Code Explanations
Priti Oli, Rabin Banjade, Jeevan Chapagain +1
This paper systematically investigates the generation of code explanations by Large Language Models (LLMs) for code examples commonly encountered in introductory programming course…