Using Learning Analytics to Support Self-Regulated Learning in Primary Education – A Learning Design Perspective

Abstract In digitalized learning processes, learning analytics (LA) can help teachers make pedagogically sound decisions and support pupils’ self-regulated learning (SRL). However, research on the role of the pedagogical dimensions of learning design (LD) in influencing the possibilities of LA remains scarce. Primary school presents a unique LA context characterized by blended learning environments and pupils’ various abilities to regulate their learning, underscoring teachers’ vital importance. This study explores how pedagogically diverse LDs influence pupils’ SRL behaviors and learning management system (LMS) usage, as well as how this is reflected in LA visualizations. Two LDs were designed and implemented in two primary school classes of fifth ( n = 30) and sixth ( n = 22) graders within authentic pedagogical and technological contexts. We used sequence analysis to examine the pupils’ SRL actions during LDs, using LMS log data and observation data to contextualize these actions. The results show that LA offers less accurate feedback in more open, collaborative LDs as pupils tend to rely less on the LMS to regulate their learning. Furthermore, the teacher powerfully influences LMS usage in blended primary school classrooms. To maximize the potential of using LA to support SRL, its design needs to be grounded in the LD through an understanding of how the regulation of learning is promoted in diverse learning processes.