Integrating Large Language Models as AI Tutors in Automated Programming Assessment Systems: Exploring Student Experiences and Lessons Learned
Integrating large language models like GPT-3.5-Turbo as AI tutors within automated programming assessment systems can offer timely feedback and scalability, but also faces challenges like generic responses and student concerns about learning progress inhibition.
Algorithmic Reasoning Tasks: A Novel Approach to Assess and Predict Programming Abilities of Novice Learners
Algorithmic Reasoning Tasks (ARTs) can effectively assess the reasoning skills required for code writing and be used to predict student performance in introductory programming courses.
Exploring How Large Language Model-Generated Programming Hints of Varying Levels Support or Disappoint Novice Learners
Providing multiple levels of programming hints, from high-level natural language guidance to concrete code examples, can better support novice learners' problem-solving compared to offering high-level hints alone.
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Programming Assignments
Peer-aided Repairer (PaR) is a novel framework that empowers large language models to effectively repair bugs in advanced student programming assignments by leveraging peer solutions and a multi-source prompt generation approach.
Clustering MOOC Programming Solutions to Diversify Their Presentation to Students
Developing tools to cluster and select diverse programming solutions from MOOC submissions to present to students, in order to improve their learning experience.
Personalized Programming Guidance with Deep Learning Style Capturing
Proposing a novel model, PERS, to provide personalized programming guidance by simulating learners' intricate programming behaviors.