Analysing contextualized attention metadata for self-regulated learning: A supporting framework for self-monitoring and self-reflection

In order to successfully learn in a self-regulated way, self-monitoring of the learner and reflection of learning behaviour is required. We therefore introduce a framework that collects usage metadata from application programs and stores them as Contextualized Attention Metadata (CAM). We also present three approaches on how we exploit the collected CAM for further analysis such as object recommendation or learning activity classification in order to help the learner become aware of her learning behaviour, to self-reflect and to support her during her learning processes