Roger Azevedo

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Framed by the existing theoretical and empirical research on cognitive and intelligent tutoring systems (ITSs), this commentary explores two areas not directly or extensively addressed by Akhras and Self (this issue). The first area focuses on the lack of conceptual clarity of the proposed constructivist stance and its related constructs (e.g., affordances,(More)
We examined how self-regulated learning (SRL) and externally-facilitated selfregulated 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)
Think-aloud and pre-test data were collected from 49 undergraduates with varying levels of prior domain knowledge to examine the relationship between prior domain knowledge and self-regulated learning with hypermedia. During the experimental session, each participant individually completed a pretest on the circulatory system, and then one 40-min hypermedia(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)
Learning about conceptually rich domains with advanced learning technologies requires students to regulate their learning (Jacobson, 2008). Current research from cognitive and learning sciences provides ample evidence that learners have difficulty learning about these domains (Chi, 2005). This research indicates that the complex nature of the learning(More)
This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on studentgenerated paragraphs during prior knowledge activation, a(More)
In this study we aligned and compared self-report and on-line emotions data on 67 college students’ emotions at five different points in time over the course of their interactions with MetaTutor. Self-reported emotion data as well as facial expression data were converged and analyzed. Results across channels revealed that neutral and positively-valenced(More)
This article describes the problem of detecting the student mental models, i.e. students’ knowledge states, during the self-regulatory activity of prior knowledge activation in MetaTutor, an intelligent tutoring system that teaches students self-regulation skills while learning complex science topics. The article presents several approaches to automatically(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)