ORA (Round 5) - Facilitating Self-Regulated Learning with Personalized Scaffolds on Student's own Regulation Activities

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The focus of education is increasingly set on students' ability to regulate their own learning within technology-enhanced learning environments (TELs). Prior research has shown that self-regulated learning (SRL) leads to better learning performance but students often experience difficulties to adequately self-regulate their learning. Instructional scaffolds are a successful method to help learners and consequently improve learning outcomes. However, scaffolds are often standardized and do not adapt to the individual learning process. Learning analytics and machine learning offer an approach to better understand SRL-processes during learning. Yet, current approaches lack validity or require extensive analysis after the learning process. This research collaboration will research how to advance support given to students by i) improving unobtrusive data collection and machine learning techniques to gain better measurement and understanding of SRL-processes and ii) using these new insights to facilitate student's SRL by providing personalized scaffolds. We will reach this goal by investigating and improving trace data in exploratory studies (exploratory study1 and study 2) and using the insight gained from these studies to develop and test personalized scaffolds based on individual learning processes in laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). Our joint expertise in the fields of self-regulated learning and learning analytics provide superior opportunities to develop and test more powerful adaptive educational technologies.