Using Expertise Predictions to Facilitate Self-Regulated Learning

The self-awareness of the breadth and depth of one´s exper- tise is crucial for self-regulated learning. We propose an interface using predicted expertise scores that assists learners in reflecting their exper- tise. The preliminary study results show that learner models from partic- ipants using our interface are significantly larger in size than those of the control group. The results also indicate that the interface may encourage learners to equally think about their strengths and weaknesses. In addi- tion, participants´ feedback suggests an increased motivation regarding the task of self-assessment.