Using LLMs to Generate Code Explanations in Programming Classes
Worked examples in programming classes are highly valued for their ability to provide practical demonstrations of solving coding problems. However, instructors often face the challenge of lack of time to provide detailed explanations for numerous examples used in a programming course. In this paper, the feasibility of using Language Models (LLMs) to generate code explanations for both passive and active example exploration systems is assessed.
The traditional approach to presenting code explanations involves line-by-line explanations of the example code. This approach relies heavily on instructors manually providing explanations, but due to time constraints, this is often not feasible for all examples. This limitation impacts students’ ability to fully understand and grasp the concepts presented in these examples.
To overcome this limitation, the paper proposes leveraging the power of LLMs, specifically chatGPT, to automatically generate code explanations. LLMs are language models trained on extensive datasets and have the ability to analyze and generate human-like text based on the input.
The research compares the code explanations generated by chatGPT with those provided by experts and students. This comparison serves to assess the effectiveness and accuracy of the LLM-generated explanations. By evaluating multiple perspectives, the researchers aim to gain a comprehensive understanding of how well the LLM performs in generating useful code explanations.
The results of this study will provide valuable insights into the potential of LLMs in helping instructors streamline the process of providing code explanations in programming classes. If successful, LLMs could significantly enhance the learning experience for students, particularly when it comes to understanding worked examples.
In addition, the use of LLMs for code explanation generation can also benefit students in active example exploration systems. These systems allow students to interactively explore and experiment with example code. By providing LLM-generated explanations during this process, students can gain a deeper understanding of the underlying concepts and improve their problem-solving skills.
This research opens up new possibilities for automating and enhancing code explanation processes in programming education. As LLMs continue to improve and evolve, they have the potential to become a valuable tool for instructors, alleviating the time constraints and ensuring that students have access to comprehensive code explanations.
In the future, further research can explore the integration of LLMs with existing programming education platforms and tools. This would enable real-time generation of code explanations tailored to specific programming problems and individual students’ needs. Additionally, refining the accuracy and clarity of LLM-generated explanations would be an important area of focus.
In conclusion, the use of LLMs for generating code explanations in programming classes holds great promise. By leveraging the power of language models, instructors can overcome the challenge of providing comprehensive explanations for numerous examples, ultimately enhancing the learning experience for students.