How Self-Regulated Learning Is Affected by Feedback Based on Large Language Models: Data-Driven Sustainable Development in Computer Programming Learning

Self-regulated learning (SRL) is a sustainable development skill that involves learners actively monitoring and adjusting their learning processes, which is essential for lifelong learning. Learning feedback plays a crucial role in SRL by aiding in self-observation and self-judgment. In this context...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 14; no. 1; p. 194
Main Authors: Sun, Di, Xu, Pengfei, Zhang, Jing, Liu, Ruqi, Zhang, Jun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2025
Subjects:
Academic achievement
Artificial intelligence
Chatbots
Cognitive ability
Computer programming
Continuing education
Design of experiments
Distance learning
Education
Educational objectives
Feedback
Human performance
Language
Large language models
Learning activities
Learning strategies
Lifelong learning
Metacognition
Motivation
Natural language processing
Real time
Self evaluation
Self-efficacy
Skills
Sustainable development
China
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:Self-regulated learning (SRL) is a sustainable development skill that involves learners actively monitoring and adjusting their learning processes, which is essential for lifelong learning. Learning feedback plays a crucial role in SRL by aiding in self-observation and self-judgment. In this context, large language models (LLMs), with their ability to use human language and continuously interact with learners, not only provide personalized feedback but also offer a data-driven approach to sustainable development in education. By leveraging real-time data, LLMs have the potential to deliver more effective and interactive feedback that enhances both individual learning experiences and scalable, long-term educational strategies. Therefore, this study utilized a quasi-experimental design to examine the effects of LLM-based feedback on learners’ SRL, aiming to explore how this data-driven application could support learners’ sustainable development in computer programming learning. The findings indicate that LLM-based feedback significantly improves learners’ SRL by providing tailored, interactive support that enhances motivation and metacognitive strategies. Additionally, learners receiving LLM-based feedback demonstrated better academic performance, suggesting that these models can effectively support learners’ sustainable development in computer programming learning. However, the study acknowledges limitations, including the short experimental period and the initial unfamiliarity with LLM tools, which may have influenced the results. Future research should focus on refining LLM integration, exploring the impact of different feedback types, and extending the application of these tools to other educational contexts.
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14010194