Designing dashboards to support learners' self-regulated learning

Abstract

This contribution reports on the development of two learner-faced dashboards that support learners' self-regulated learning during practice activities in an adaptive learning technology (ALT). While learners learn using adaptive learning technologies on tablets, they leave rich traces of data that capture many details of their learning processes. The data can be used to create dashboards that support learners to make valid inference about how they regulate control and monitor their learning. Such personalized visualizations are a new tool to support learners regulation. In this paper we describe two designs of personalized dashboards supporting SRL. The first dashboard is drawn by learners themselves based on ALT achievement data. Learners are asked to set goals at the start of each lesson and add their achievements after each lesson. This is used as input to monitor progress and determine whether adaptation is needed to reach their goals. Learners draw elements of the dashboard themselves and hence make their own personalized visualizations. The second dashboard follows the same logic, but the visualization process is automated in an app. Again learners set their goal at the start of each lesson and view their achievement and progress in the dashboard after each lesson. Additional, learners also are presented with their learning path based on Moment-by-Moment Learning Curves and cues to translate data into actionable feedback to efficiently reach learning goals. The contribution of this paper is to discussion the design and rational for the two dashboards that support young learners SRL based on ALTs trace data.