Using Large Language Models for Automated Coding of Self-Regulated Learning Think-Aloud Protocol Data
DOI:
https://doi.org/10.18608/jla.2026.9025Keywords:
large language models, self-regulated learning, think-aloud protocols, automated coding, STEM education, prompt engineering, research paperAbstract
Documenting and understanding self-regulated learning (SRL) processes can inform the design of learning activities and scaffolds to enhance student success in STEM. Think-aloud protocols (TAPs)—prompting students to verbalize thoughts during task performance—reveal real-time, ecologically sensitive verbalizations of students’ SRL processes such as planning, monitoring, and strategy use. However, coding TAP data requires substantial resources. We investigated the capabilities of Large Language Models (LLMs) in automating the coding of SRL processes in TAPs across undergraduate STEM courses. To examine how task features, prompt engineering strategies, and SRL codes are linked to LLMs’ coding accuracy, we used a factorial design comparing different LLMs (GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro) across six SRL TAP codes (representing distinct cognitive and metacognitive processes), six prompt conditions (varying few-shots and context levels), and two STEM tasks (mathematics and biology). Analysis of 600 student verbalizations revealed that mathematics tasks yielded significantly higher classification accuracy compared to biology tasks, with GPT-4o and Claude 3.5 Sonnet outperforming Gemini 1.5 Pro. Few-shot prompting showed code-specific effects, with descriptively higher accuracy for monitoring negative judgments of learning but significantly decreased accuracy for subgoal setting. The addition of contextual information showed minimal impact across tasks. Cognitive process TAP codes (e.g., mathematical problem-solving) demonstrated the most consistent cross-task classification accuracy. In contrast, metacognitive monitoring (e.g., judgment of learning) showed substantial task-dependent variations. These findings highlighted both the promise and limitations of LLMs for scaling SRL research. They suggest that theoretical alignment in prompt engineering is essential for effective automated coding of dynamic regulatory processes.
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