Improving Measurements of Self-Regulated Learning

Abstract

Articles in this special issue present recent advances in using state-of-the-art software systems that gather data with which to examine and measure features of learning and particularly self-regulated learning (SRL). Despite important advances, there remain challenges. I examine key features of SRL and how they are measured using common tools. I advance the case that traces of cognition and metacognition offer critical information about SRL that other state-of-the-art measurements cannot.