Running head : DETECTING LEARNER ENGAGEMENT 1 Operationalizing and Detecting Disengagement

In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics (STEM) domains, in which many students struggle and where the U.S. needs skilled workers. This article lays out some issues important for framing research on this topic, and provides a review of some existing work with similar goals on engagement in science learning. Specifically, here we seek to help better concretize engagement, a fuzzy construct, by operationalizing and detecting (i.e., identifying using a computational method) disengaged behaviors that are antithetical to engagement. We, in turn, describe our realtime detector (i.e., machine learned model) of disengaged behavior and how it was developed. Lastly, we address our on-going research on how our detector of disengaged behavior will be used to intervene in real time in order to better support students’ science inquiry learning in Inq-ITS (Inquiry-Intelligent Tutoring System; Gobert, Sao Pedro, Baker, Toto, & Montalvo, 2012; Gobert, Sao Pedro, Raziuddin, & Baker, 2013).