Do Prompts Work? Experimental Insights Into Metacognitive Support in Digital Learning [abstract: poster]

Abstract

The rapid expansion of digital learning environments has increased students' responsibility for managing their learning, underscoring the central role of Self-Regulated Learning (SRL). Metacognitive prompts have been proposed as an instructional mechanism to trigger core SRL processes—planning, monitoring, and reflection. However, empirical evidence regarding their effectiveness remains mixed, highlighting the need for controlled designs that systematically test boundary conditions and moderating variables. The present project employs a quasi-experimental, within-subjects design to examine the causal impact of metacognitive prompts on information retrieval in a digital context. University students (≥18 YO) complete two multimedia learning sessions (approximately 10 minutes each) covering the same topic but containing non-overlapping content. In the experimental condition, metacognitive prompts are embedded at three theoretically defined phases aligned with Zimmerman's SRL model: (1) pre-task planning prompts, (2) mid-task monitoring prompts, and (3) post-task reflection prompts. In the control condition, the video is presented without prompts. Condition order and video sequence are counterbalanced to control for order and learning effects. Immediately following each session, participants complete parallel multiple-choice knowledge tests assessing memory performance. Individual differences in self-efficacy, metacognitive learning strategies, and metamemory are assessed using validated self-report measures and examined as moderators of the prompt–performance relationship. By experimentally manipulating prompt presence within participants while controlling task order, this project aims to determine whether metacognitive prompts enhance learning and to identify the individual conditions under which their effects are strengthened or attenuated, contributing to experimental research on SRL in digital learning environments.