There has been a growing trend in employing generative artificial intelligence (GenAI) techniques to support learning. Moreover, scholars have reached a consensus on the critical role of self-regulated learning (SRL) in ensuring learning effectiveness within GenAI-assisted learning environments, making it essential to capture students’ dynamic SRL patterns. In this study, we extracted students’ interaction patterns with GenAI from trace data as they completed a problem-solving task within a GenAI-assisted intelligent tutoring system. Students’ purpose of using GenAI was also analyzed from the perspective of information processing, i.e., information acquisition and information transformation. Using the sequential and clustering analysis, this study classified participants into two groups based on their SRL sequences. These two groups differed in the frequency and temporal characteristics of GenAI use. As well, most of the students used GenAI for information acquisition rather than information transformation, while the correlation between the purpose of using GenAI and learning performance was not statistically significant. Our findings informed both the pedagogical design and the development of GenAI-assisted learning environments.

This study investigates the relationship between students' achievement goals and metacognitive processes within computer-simulated engineering design environments. A sample of 99 participants from diverse academic backgrounds were recruited to complete a net-zero energy house design challenge using a computer-simulated learning environment named Aladdin. Through latent profile analysis, we identified two distinct achievement goal profiles: Multiple Goal-Oriented (characterized by high scores across all goal dimensions) and Selective Mastery-Oriented (showing moderate mastery-approach goals but lower scores on other dimensions). Results revealed that Multiple Goal-Oriented students demonstrated significantly higher levels of Judgment of Learning (JOL) and Confidence compared to their Selective Mastery-Oriented peers while showing marginally higher levels of Feeling of Knowing (FOK) judgments. These findings suggest that engaging with engineering design tasks from multiple goal perspectives may enhance students' ability to evaluate their competency and performance in computer-simulated environments. These findings also provide valuable insight for the instruction of engineering design tasks and the design of educational simulations, highlighting the need for diverse instructional strategies that support both mastery and performance goals while fostering metacognitive development.

GPT has demonstrated impressive capabilities in executing various natural language processing (NLP) and reasoning tasks, showcasing its potential for deductive coding in social annotations. This research explored the effectiveness of prompt engineering and fine-tuning approaches of GPT for deductive coding of context-dependent and context-independent dimensions. Coding context-dependent dimensions (i.e., Theorizing, Integration, Reflection) requires a contextualized understanding that connects the target comment with reading materials and previous comments, whereas coding context-independent dimensions (i.e., Appraisal, Questioning, Social, Curiosity, Surprise) relies more on the comment itself. Utilizing strategies such as prompt decomposition, multi-prompt learning, and a codebook-centered approach, we found that prompt engineering can achieve fair to substantial agreement with expert-labeled data across various coding dimensions. These results affirm GPT's potential for effective application in real-world coding tasks. Compared to context-independent coding, context-dependent dimensions had lower agreement with expert-labeled data. To enhance accuracy, GPT models were fine-tuned using 102 pieces of expert-labeled data, with an additional 102 cases used for validation. The fine-tuned models demonstrated substantial agreement with ground truth in context-independent dimensions and elevated the inter-rater reliability of context-dependent categories to moderate levels. This approach represents a promising path for significantly reducing human labor and time, especially with large unstructured datasets, without sacrificing the accuracy and reliability of deductive coding tasks in social annotation. The study marks a step toward optimizing and streamlining coding processes in social annotation. Our findings suggest the promise of using GPT to analyze qualitative data and provide detailed, immediate feedback for students to elicit deepening inquiries.

While virtual learning environments (VLEs) are widely used in K-12 education for classroom instruction and self-study, young students’ success in VLEs highly depends on their self-regulated learning (SRL) skills. Therefore, it is important to provide personalized support for SRL. One important precursor of designing personalized SRL support is to understand students’ SRL behavioral patterns. Extensive studies have clustered SRL behaviors and prescribed personalized support for each cluster. However, limited attention has been paid to the algorithm bias and fairness of clustering results. In this study, we “fairly” clustered the behavioral patterns of SRL using fair-capacitated clustering (FCC), an algorithm that incorporates constraints to ensure fairness in the assignment of data points. We used data from 14,251 secondary school learners in a virtual math learning environment. The results of FCC showed that it could capture six clusters of SRL behaviors in a fair way; three clusters belonging to high-performing (i.e., H-1. Help-provider, H-2) Active SRL learner, H-3) Active onlooker), and three clusters in low-performing groups (i.e., L-1) Quiz-taker, L-2) Dormant learner, and L-3) Inactive onlooker). The findings provide a better understanding of SRL patterns in online learning and can potentially guide the design of personalized support for SRL.