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We examined how self-regulated learning (SRL) and externally-facilitated self-regulated learning (ERL) differentially affected adolescents' learning about the circulatory system while using hypermedia. A total of 128 middle-school and high school students with little prior knowledge of the topic were randomly assigned to either the SRL or ERL condition.(More)
Self-report data and think-aloud data from 37 undergraduates were used to examine the impact of conceptual scaffolds on self-efficacy, monitoring, and planning during learning with a commercial hypermedia environment. Participants, randomly assigned to either the No Scaffolding (NS) or Conceptual Scaffolding (CS) condition, used a hypermedia environment for(More)
Learning about complex and challenging science topics with advanced learning technologies requires students to regulate their learning. The deployment of key cognitive and metacognitive regulatory processes is key to enhancing learning in open-ended learning environments such as hypermedia. In this paper, we propose a metaphor—Computers as MetaCognitive(More)
In this paper we investigate the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity. The data was collected during a study with MetaTutor, an intelligent tutoring system (ITS) designed to promote the use of self-regulated learning strategies. We used a variety of machine learning and feature(More)
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In this paper, we explore the potential of gaze data as a source of information to predict learning as students interact with MetaTutor, an ITS that scaffolds self-regulated learning. Using data from 47 college students, we show that a classifier using a variety of gaze features achieves considerable accuracy in predicting student learning after seeing gaze(More)