Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation (2407.05437v1)

Abstract: LLMs and prompt engineering hold significant potential for advancing computer programming education through personalized instruction. This paper explores this potential by investigating three critical research questions: the systematic categorization of prompt engineering strategies tailored to diverse educational needs, the empowerment of LLMs to solve complex problems beyond their inherent capabilities, and the establishment of a robust framework for evaluating and implementing these strategies. Our methodology involves categorizing programming questions based on educational requirements, applying various prompt engineering strategies, and assessing the effectiveness of LLM-generated responses. Experiments with GPT-4, GPT-4o, Llama3-8b, and Mixtral-8x7b models on datasets such as LeetCode and USACO reveal that GPT-4o consistently outperforms others, particularly with the "multi-step" prompt strategy. The results show that tailored prompt strategies significantly enhance LLM performance, with specific strategies recommended for foundational learning, competition preparation, and advanced problem-solving. This study underscores the crucial role of prompt engineering in maximizing the educational benefits of LLMs. By systematically categorizing and testing these strategies, we provide a comprehensive framework for both educators and students to optimize LLM-based learning experiences. Future research should focus on refining these strategies and addressing current LLM limitations to further enhance educational outcomes in computer programming instruction.

• The paper introduces a framework to categorize prompt engineering strategies for various coding education contexts.
• It demonstrates that multi-step prompts, especially with GPT-4 and GPT-4o, boost accuracy, speed, and adherence to coding standards.
• The study offers actionable guidelines for educators and highlights ongoing challenges in addressing complex, multi-stage programming problems.

Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation

Introduction

The paper "Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation" investigates the transformative potential of LLMs in the field of computer programming education. The research focuses on how tailored prompt engineering strategies can enhance the educational utility of LLMs, particularly in generating Python code. The paper leverages models like GPT-4, GPT-4o, Llama3-8b, and Mixtral-8x7b, employing datasets from LeetCode and USACO to evaluate model efficacy.

Research Contributions

The paper addresses three primary research questions:

1. Categorization of Prompt Strategies: The paper proposes a framework to systematically categorize prompt engineering strategies based on educational needs. This categorization helps optimize learning experiences across foundational, competitive, and advanced problem-solving contexts.
2. Empowerment of LLMs: It examines how specific prompts can enhance the problem-solving capacity of LLMs beyond their native capabilities, especially when engaged with complex coding challenges.
3. Evaluation Framework: The research establishes a robust framework for testing different prompt strategies, providing educators with actionable guidelines to implement LLMs effectively in programming education.

Methodology

The methodology involves a structured approach where programming questions are categorized, various prompt engineering strategies are applied, and the generated responses are evaluated. Key methodological steps include:

• Question Categorization: Aligning questions into categories based on educational goals—basic skills, competitive programming, and complex problem solving—enables targeted pedagogical strategies.
• Prompt Engineering Strategies: Techniques ranged from no-engineering to dynamic and specific prompts. The paper emphasizes the benefits of multi-step conversational prompting to enhance model interaction and response accuracy.
• Evaluation Metrics: The paper employs pass rate, execution time, and Pylint scores for LeetCode datasets, while USACO problems were evaluated through user submissions, assessing correctness and efficiency.

  Figure 1: Flowchart illustrating the users in code generation and evaluation process.

Experimental Results

LeetCode Dataset

The analysis on LeetCode datasets demonstrated that GPT-4 and GPT-4o substantially outperformed other models. Performance metrics indicated:

• Accuracy: Both models achieved near-perfect accuracy, with GPT-4o showing a slight edge in multi-step prompts.
• Time Efficiency: GPT-4 was the most time-efficient across all prompt types, supporting its use in time-sensitive educational scenarios.
• Code Quality: High Pylint scores underscored sound adherence to coding standards, vital for educational integrity and maintainability.

USACO Dataset

The USACO analysis provided deeper insights into the use of advanced and specific prompts:

• Complex Problem-Solving: Enhanced prompts significantly increased the solvability of complex problems, with effectiveness magnified by problem-specific tailoring.
• Remaining Challenges: Despite improvements, certain problems, notably those requiring deep logical reasoning and multi-stage context retention, remained unsolvable, highlighting areas for further LLM enhancement.

  Figure 2: Conceptual Diagram Highlighting the Interaction Between LLMs and Prompt Engineering.

Discussion

The research suggests that prompt engineering can dramatically enhance LLM efficacy in educational contexts, with tailored strategies recommended for various learning objectives. For instance:

• Basic Instruction: Direct problem statements suffice for basic coding skills due to pre-existing exposure in LLM training data.
• Competitive Programming: Multi-step conversational prompts excel in competitions, offering iterative refinement and contextual understanding.
• Advanced Problems: Highly specific, detailed prompts are essential for tackling sophisticated algorithms and mathematical challenges.

The paper's findings support the use of GPT-4o, emphasizing its adaptability and superior performance across scenarios requiring iterative and complex problem-solving approaches.

Conclusion

This paper underscores the potential of LLMs in redefining computer programming education through strategically engineered prompts. By systematically categorizing and testing prompts, educators can deploy LLMs to effectively enhance learning outcomes, from fundamental skill acquisition to addressing formidable cognitive challenges. Future research could extend these findings by developing more sophisticated prompt strategies to further improve LLM robustness and functionality in educational settings, addressing intricacies in logic, numerical complexity, and multi-stage contextual reasoning.

Figure 3: A screenshot of the USACO evaluation system displaying user submission results (all pass).