In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics domains, in which many students struggle and where the United States 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 real-time detector (i.e., machine learned model) of disengaged behavior and how it was developed. Last, we address our ongoing research on how our detector of disengaged behavior will be used to intervene in real time to better support students' science inquiry learning in Inq-ITS (Inquiry-Intelligent Tutoring

No abstract

No abstract

No abstract

In recent years, an increasing number of analyses in learning analytics and educational data mining (EDM) have adopted a "discovery with models" approach, where an existing model is used as a key component in a new EDM or analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior (i.e., carelessness) in the context of middle school computer-based science inquiry. This behavior was acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computerbased learning environments, careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part because of difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data to study the correlations between the prevalence of carelessness and student goal orientation.