Fostering self-regulated learning through adaptive learning technology: A differentiated perspective on the role of feedback

Synthesis: Mejeh and Fromm investigate how differentiated feedback types — directive, informative, and transformative — delivered through an adaptive learning technology (ALT) and taken up by learners relate to self-regulated learning (SRL) across its pre-actional, actional, and post-actional phases. In a study of 194 students in a stochastics course using an ALT over eight weeks, analyzed with self-report + trace data via hierarchical linear modeling, they find that task value, Self Efficacy, goal orientation, and positive emotions in the pre-actional phase positively influence regulatory behavior in the actional phase, while negative emotions hinder it. However, adaptive feedback's moderating effects were limited and sometimes negative — transformative feedback may contribute to cognitive overload — underscoring that feedback effectiveness is context-dependent and must be tailored to learners' needs.

Key Findings

• Pre-actional factors drive actional Regulation. Task value, self-efficacy, goal orientation, and positive emotions in the pre-actional phase positively influenced regulatory behavior in the actional phase; negative emotions hindered it.
• Post-actional reflection feeds forward. Post-actional satisfaction was positively associated with motivation, metacognitive activity, and positive emotions in the subsequent pre-actional phase — evidence of the cyclical nature of SRL.
• Adaptive feedback's effects are limited and context-dependent. Feedback's moderating effects were limited and, in some cases, negatively affected regulatory outcomes; transformative feedback may contribute to cognitive overload.
• Differentiated feedback types matter. Directive, informative, and transformative feedback delivered through ALT are taken up differently by learners, with variable effects on SRL components across phases.
• Design implication. While ALT feedback can promote SRL, its effectiveness requires tailoring to learners' needs; future work should optimize feedback design and address individual differences in SRL development.

Implications

This study contributes directly to the knowledge base's Self Regulated Learning and Adaptive Learning threads by showing that how feedback is designed and taken up through adaptive technology shapes its effect on SRL — more is not always better. The finding that transformative feedback can impose cognitive load rather than support regulation echoes broader concerns about feedback density and Feedback Literacy. It reinforces the point that effective adaptive learning systems must tailor feedback to learners' regulatory needs and states, connecting to Feedback, Metacognition, Motivation, and Self Efficacy. The use of trace data alongside self-report also highlights the value of Learning Analytics in studying SRL in authentic technology-mediated settings.

Connected Concepts

• Self Regulated Learning — the construct the ALT aims to foster
• Adaptive Learning — the technology delivering personalized feedback
• Feedback — the differentiated feedback under study
• Feedback Literacy — learners' capacity to engage with feedback
• Metacognition — a key SRL component supported/studied
• Motivation — pre-actional driver of regulatory behavior
• Self Efficacy — a pre-actional factor influencing regulation
• Learning Analytics — trace-data methodology
• Cognitive Offloading — the cognitive-load risk of transformative feedback
• Higher Ed — the study context (stochastics course)

Connected Articles

• Banihashem AI SRL Systematic Mapping Review 2025 — Mapping review of AI and self-regulated learning

Citation

Mejeh, M., & Fromm, Y. M. (2026). Fostering self-regulated learning through adaptive learning technology: A differentiated perspective on the role of feedback. Learning and Instruction, 105, 102394.