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A lack of engagement in general can lead to negligible learning, lower self-efficacy, diminished interest in educational activities, and, most importantly, increased attrition and dropout. Given this sketch of the role of engagement in learning, the paucity of research in this area is somewhat surprising and quite problematic. 
    The current research aspires to advance this goal by: (a) systematically investigating the mechanisms that facilitate or hinder engagement, and (b) leveraging these insights towards the development of interventions that promote persistent and productive engagement trajectories during deep learning. We focus on the domain of critical thinking and scientific reasoning, because it is widely acknowledged that there could be dramatic improvements in students’ scientific inquiry skills. 
    We adopt a theoretical model that depicts engagement as occurring at the intersection of what a learner brings to an instructional text, what the text offers the learner, and what the activity (in which the learning is situated) both demands of and affords the learner. The core research question is how a tripartite approach consisting of interactions between the learners themselves (i.e., individual differences), the instructional materials (i.e., the text), and the learning activities (i.e., the task) modulate engagement during learning of critical thinking skills. 
    Of critical importance is the challenge of predicting when a learner has given up, zoned out, and disengaged to an extent that any further instruction is essentially futile. Finally, we will consider integrating these predictive engagement models into a computer program that aims to increase engagement and learning by selecting activities and materials in a manner that is dynamically sensitive to individual learners.