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Found 8 papers, 0 papers with code
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle.
Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources
In this study, we investigated the potential for LLMs to produce learning resources in an introductory programming context, by comparing the quality of the resources generated by an LLM with those created by students as part of a learnersourcing activity.
Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems.
Computing Education in the Era of Generative AI
The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning.
Comparing Code Explanations Created by Students and Large Language Models
In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code.
Using Large Language Models to Enhance Programming Error Messages
Large language models can be used to create useful and novice-friendly enhancements to programming error messages that sometimes surpass the original programming error messages in interpretability and actionability.
Automatic Generation of Programming Exercises and Code Explanations using Large Language Models
Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students.
Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability
To evaluate how different aspects of DLKT models influence model performance, we test input and output layer variations found in the compared models that are independent of the main architectures.
