Self-regulated Learning as a Predictor for Learning Analytics Dashboard Reference Frame Preferences

Abstract

In Immersive Learning Environments (ILEs) the feedback tools are often passive displays which do not necessarily help learners to progress in their learning process. Learners could really benefit from receiving the appropriate feedback through Learning Analytics Dashboards (LADs) that supports them and enables them to take immediate action. This can, however, only happen if the feedback that is given is suitable for the learner based on their needs and skill level. Therefore this study aims to investigate how the Self-Regulated Learning (SRL) skills predict the preference for a certain type of LAD reference frame. Three different types of reference frames were used in this study: the Progress RF, the Social RF, and the Achievement RF. This research found that learners with higher SRL skills have a preference for the Progress RF. The findings can contribute to the educational sciences and the designing of LADs in ILEs in order to foster learning for learners of all levels.