Three-phase SRL scaffolding framework (planning, task execution, post-task reflection) operationalized across iterative design cycles¶

Theory · All theories
Evidence · 1 claim (1 mixed) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The study organizes chatbot design around a three-phase conceptualization of self-regulated learning—planning, task execution, and post-task reflection—drawn from the common structure of prominent SRL models. Each DBR cycle targeted one phase: cycle one supported planning via reflective prompts, cycle two supported before and during task execution via a Telegram chatbot, and cycle three guided post-task reflection via a web-based Socratic tutor. The authors state each cycle "was inspired by a three-phase conceptualization of SRL (planning, task execution, and post -task reflection)". This framing let them test how students used the tool to plan, execute, and reflect as development progressed.

Design Implications¶

Context¶

Requirements¶

• Chatbot activities must be designed and configured by instructors to align with the targeted SRL phase and course content

Constraints¶

• The study does not aim to demonstrate causal effects on students' SRL; most chatbot research supports only one or two SRL phases

Target Learners¶

• higher education students

Target Learning Objectives¶

• self-regulated learning
• self-reflection
• metacognition

Claims¶

• Chatbot Used As Study Aid Not Socratic Reflection [~M]

Related Theories¶

• GBSRI framework: four stages with seven steps integrating SFL genre-based teaching-learning cycles with social-cognitive self-regulated learning phases
• Self-Regulated Learning
• Three-phase framework of teacher roles in collaborative learning: before, during, and after
• Four-category operationalization of Winne and Hadwin's four-stage SRL model for tutored problem-solving verbalizations

Examples¶

• Four design insights for GAI chatbots supporting SRL: real-time personalization, warm language, platform integration, and course-content embedding
• Self Regulated Learning Instruction
• Four-phase weekly cycle for AI-based noticing practice: video, chatbot interaction, reflection, mentor feedback
• Use the SRL conceptual framework to guide future research on college student motivation and learning

Key Sources¶

• Hilliger, I., Pérez-Sanagustín, M., González, C., Villalobos, E. and Celis, S. (2026). A GAI-Based Chatbot for Scaffolding Self-Regulated Learning: Insights from a Design-Based Research Approach. Journal of Learning Analytics, 13(1), 74–88. https://doi.org/10.18608/jla.2026.9143