How to design and evaluate personalized scaffolds for self-regulated learning

Abstract

Abstract Self-regulation is an essential skill for lifelong learning. Research has shown that self-regulated learning (SRL) leads to greater academic achievement and sustainable education, but students often struggle with SRL. Scaffolds are widely reported as an effective and efficient support method for SRL. To further improve digital scaffolds’ effectiveness, real-time detection of learning behavior can be used to personalize scaffolds. Therefore, the present study aimed to inform the field of scaffolding SRL by reporting on the design and evaluation of digital scaffolds. We present decisions made during the design process of personalized scaffolds to inform future scaffold designs. We evaluated how scaffolds were personalized based on real-time detection SRL, how university students respond to the scaffolds (i.e., compliance), and how this response is related to learning outcomes (i.e., quality of an essay). The research design was a pre-posttest with three conditions (no, generalized (same for all), or personalized scaffolds). A 45-minute reading and writing task was used, during which SRL processes were recorded in real-time. Findings revealed that different real-time SRL processes could be used to personalize scaffolds, meaning that we were able to personalize the content of scaffold based on students’ actual learning behavior. In addition, students in the personalized condition complied more with the scaffolds than students in the other conditions. This compliance with the scaffolds was generally associated with better learning outcomes. To conclude, our approach showed how design decisions could be evaluated and provided insight into the personalization of scaffolds.