The Impact of Large Language Models on Learning Outcomes in Coding Classes

Khái niệm cốt lõi

Having access to large language models like ChatGPT can have both positive and negative effects on student learning outcomes in programming courses, depending on how students use the technology.

Tóm tắt

The study examines the impact of large language models (LLMs) like ChatGPT on student learning in coding classes. The key findings are:

1. In observational field data, the use of ChatGPT has a positive effect on students' performance on individual practice questions, but a negative effect on their overall learning outcomes. Students who excessively rely on ChatGPT to solve practice exercises without investing sufficient mental effort show impaired learning.

2. In a controlled laboratory experiment, providing students access to ChatGPT during a learning phase has a positive effect on their learning outcomes, but only when copy-and-paste functionality is enabled. Without copy-and-paste, the usage of ChatGPT is limited, and no significant effect on learning is observed.

3. The availability of copy-and-paste enables students to more easily copy problem descriptions into ChatGPT and copy generated solutions back into their code editor. This facilitates solution-seeking behavior, which has a detrimental effect on learning. In contrast, using ChatGPT to ask for explanations has a positive effect on learning.

4. Students without prior coding experience benefit more from having access to ChatGPT compared to more experienced students. However, inexperienced students are also more prone to over-relying on ChatGPT to solve problems for them.

Overall, the findings suggest that large language models like ChatGPT have the potential to support student learning, but students need to be cautious of over-relying on the technology to solve problems for them rather than engaging in the learning process.

Thống kê

"Every previous question for which a fully ChatGPT-generated solution was used decreases the grade for the current question by 0.018."
"Subjects in the treatment condition solve four questions more on average in the learning phase where they have access to ChatGPT."
"Subjects in the treatment condition solve two questions more on average in the post-test."

Trích dẫn

"If students, either consciously or unconsciously, rely too much on LLMs to solve their exercises for them, the students' learning progress could be greatly impaired."
"Without copy-and-paste, usage and consequentially also the effects thereof are likely to be greatly reduced."
"The ability to copy-and-paste has a statistically significant negative effect on Post-test – pre-test."

Thông tin chi tiết chính được chắt lọc từ

AI Meets the Classroom: When Does ChatGPT Harm Learning?

by Matthias Leh... lúc arxiv.org 09-17-2024

https://arxiv.org/pdf/2409.09047.pdf

Yêu cầu sâu hơn

How can educational institutions and instructors best guide students to use large language models like ChatGPT in a way that supports rather than hinders their learning?

Educational institutions and instructors can implement several strategies to ensure that students utilize large language models (LLMs) like ChatGPT effectively, enhancing their learning outcomes rather than hindering them.

Structured Guidance and Training: Institutions should provide structured training sessions that educate students on how to interact with LLMs. This includes teaching them how to ask specific questions, seek explanations, and engage in meaningful dialogue with the AI. By framing LLMs as personal tutors, students can learn to use them for clarification and deeper understanding rather than merely seeking solutions.

Promoting Active Engagement: Instructors can encourage students to use LLMs as a supplement to their learning rather than a crutch. This can be achieved by designing assignments that require students to first attempt problems independently before consulting the LLM for hints or explanations. This approach fosters a learning-by-doing environment, which is crucial for mastering complex subjects like coding.

Incorporating Reflection: After using LLMs, students should be encouraged to reflect on their learning process. Instructors can facilitate discussions or assignments that prompt students to analyze how LLM interactions contributed to their understanding of the material. This reflection can help students recognize the value of their own problem-solving efforts and the role of LLMs in supporting their learning.

Setting Clear Boundaries: Institutions should establish clear guidelines on acceptable LLM usage. For instance, they can specify when it is appropriate to use LLMs (e.g., for explanations) and when it is not (e.g., for direct solutions). By setting these boundaries, educators can help students develop a balanced approach to using technology in their studies.

Monitoring and Feedback: Regular monitoring of student interactions with LLMs can provide valuable insights into their usage patterns. Instructors can offer feedback based on this data, helping students adjust their approach to using LLMs in a way that maximizes learning benefits while minimizing over-reliance.

What are the potential long-term consequences of students over-relying on large language models for their education, and how can these be mitigated?

The long-term consequences of students over-relying on large language models (LLMs) for their education can be significant and multifaceted:

Skill Degradation: One of the primary risks is the degradation of critical thinking and problem-solving skills. If students consistently turn to LLMs for solutions, they may not develop the necessary cognitive skills to tackle complex problems independently. This reliance can lead to a lack of confidence in their abilities and a diminished capacity for self-directed learning.

Overestimation of Abilities: As noted in the study, students may perceive their learning progress to be greater than it actually is when using LLMs. This overestimation can result in complacency, where students believe they have mastered concepts without having engaged deeply with the material.

Reduced Engagement: Over-reliance on LLMs can lead to decreased engagement with course content. Students may become passive learners, relying on AI to do the heavy lifting rather than actively participating in their education.

To mitigate these potential consequences, several strategies can be employed:

Encouraging Independent Problem Solving: Educators should emphasize the importance of attempting to solve problems independently before consulting LLMs. This can be reinforced through assessments that reward effort and process over mere correctness.

Integrating LLMs into a Broader Learning Framework: LLMs should be positioned as one tool among many in a comprehensive learning framework. By integrating various resources and methods, students can develop a more rounded skill set.

Fostering a Growth Mindset: Educators can promote a growth mindset by encouraging students to view challenges as opportunities for learning rather than obstacles. This mindset can help students appreciate the value of struggle in the learning process.

Regular Assessments and Feedback: Implementing regular assessments that focus on understanding and application rather than rote memorization can help students stay engaged and aware of their actual learning progress.

How might the findings from this study on the use of large language models in coding classes apply to other academic domains and learning contexts?

The findings from the study on the use of large language models (LLMs) in coding classes have broader implications that can be applied to various academic domains and learning contexts:

Generalizable Learning Mechanisms: The contrasting effects of LLM usage—where asking for explanations enhances learning while seeking direct solutions impairs it—can be observed across different subjects. In fields such as mathematics, science, and humanities, students can benefit from using LLMs to clarify concepts and explore ideas, while excessive reliance on LLMs for answers can hinder their critical thinking and analytical skills.

Adaptation of Teaching Strategies: Educators in other domains can adopt similar strategies to those suggested for coding classes. For instance, they can encourage students to engage with LLMs for conceptual understanding while designing assessments that require independent thought and application of knowledge.

Technology Integration in Diverse Fields: The study highlights the importance of integrating technology into the learning process in a balanced manner. In disciplines like language learning, social sciences, or even arts, LLMs can serve as valuable resources for generating ideas, providing feedback, or facilitating discussions, provided that students are guided on how to use them effectively.

Addressing Diverse Learning Needs: The findings suggest that students with varying levels of prior knowledge may benefit differently from LLMs. This insight can inform differentiated instruction strategies in various academic contexts, allowing educators to tailor their approaches based on students' backgrounds and experiences.

Promoting Lifelong Learning Skills: The emphasis on using LLMs as learning aids rather than crutches can foster skills that are essential for lifelong learning. In an increasingly digital world, the ability to critically engage with technology and utilize it as a supportive tool will be crucial across all fields of study and professional practice.

In summary, the insights gained from the study on LLMs in coding education can inform practices across various academic domains, promoting effective technology integration while safeguarding against the risks of over-reliance.