Exploring characteristics of primary school students’ self-regulated learning (SRL) behaviors in human-GenAI collaborative programming learning environments: Insights from a proposed framework

Programming education in primary school is vital for nurturing future-ready talents, yet primary school students often struggle with self-regulated learning (SRL), particularly in resource utilization and strategy regulation. Although human-generative AI (GenAI) collaborative programming learning mi...

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Vydáno v:Computers and education Ročník 240; s. 105453
Hlavní autoři: Ma, Xuechun, Li, Cuixin, Xu, Jie, Zhu, Shiyun, Li, Yan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.01.2026
Témata:
Elementary education
Human-GenAI collaboration
Learning analysis
Programming education
Self-regulated learning
Self-regulated learning
Human-GenAI collaboration
Programming education
Learning analysis
Elementary education
ISSN:0360-1315
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Shrnutí:Programming education in primary school is vital for nurturing future-ready talents, yet primary school students often struggle with self-regulated learning (SRL), particularly in resource utilization and strategy regulation. Although human-generative AI (GenAI) collaborative programming learning might have the potential to enhance personalized programming education, GenAI's interplay with SRL processes remains underexplored. To address this gap, this study first proposed a SRL Behavior Analysis Framework for human-GenAI collaborative programming learning environments and then examined SRL behaviors of a group of sixth-grade students (n = 36) in such an environment using this framework, along with various learning analytics methods including cluster analysis, descriptive statistics and lag sequential analysis. The analysis yielded the following results: (1) Based on their learning performance, primary school students were identified as three distinct clusters: programming specialized unit (PSU), high performance unit (HPU), and low performance unit (LPU). (2) Regarding SRL behaviors, students prioritized self-control (65.8 %), followed by self-observation (19 %), task analysis (12.1 %), and behavior stagnation (3.2 %). (3) Students in PSU and HPU consistently adopted goal-oriented SRL strategies, whereas students in LPU exhibited passive dependence and fragmented strategy use. GenAI's facilitative effect in supporting learning correlated with users' SRL capabilities. (4) Students in PSU and HPU exhibited frequent transitions between SRL behaviors, whereas students in LPU had insufficient ability to switch strategies when facing programming difficulties. Based on these findings, this study proposed four forward-looking design recommendations: effectively integrating GenAI with the programming environment, utilizing multimodal data and AI for learning assessment and feedback, building a cluster-driven early warning mechanism, and conducting dynamic SRL analysis and guidance based on fine-grained time-series data. •Proposed a SRL Behavior Analysis Framework for human-GenAI collaborative programming learning.•SRL strategies strongly correlate with programming performance.•High performers use goal-driven SRL; low performers exhibit passive GenAI reliance.•GenAI's facilitative effect in supporting learning correlated with user SRL capabilities.•High performers exhibit frequent transitions between SRL behaviors.
ISSN:0360-1315
DOI:10.1016/j.compedu.2025.105453