Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning

• DOI: 10.1007/s40593-021-00249-z
• Publication Date: 2021-05-21T16:02:41Z
• Authors: Yizhou Fan; Wannisa Matcha; Nora’ayu Ahmad Uzir; Qiong Wang; Dragan Gašević
• Keywords: Learning Analytics; Self-Regulated Learning; Learning design; Learning Sciences

Abstract The importance of learning design in education is widely acknowledged the literature. Should learners make effective use opportunities provided a design, especially online environments, previous studies have shown that they need to strong skills for self-regulated (SRL). literature, which reports analytics (LA), shows SRL are best exhibited choices tactics reflective metacognitive control and monitoring. However, spite high significance evaluation experience, link between has been under-explored. In order fill this gap, paper proposes novel analytic method combines three data techniques, including cluster analysis, process mining technique, an epistemic network analysis. proposed was applied dataset collected massive open course (MOOC) on teaching flipped classrooms offered Chinese MOOC platform pre- in-service teachers. results showed application approach detected four ( Search oriented , Content assessment Assessment ) were used by learners. analysis tactics’ usage across sessions revealed from different performance groups had priorities. study also shaped instructional cues embedded units MOOC. high-performance group level regulation through alignment with tasks design. provides discussion about implications research practice.