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Taub, Michelle; Azevedo, Roger – Journal of Educational Data Mining, 2018
Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry…
Descriptors: Metacognition, Undergraduate Students, Student Behavior, Scientific Research
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Rebolledo-Mendez, Genaro; Du Boulay, Benedict; Luckin, Rosemary; Benitez-Guerrero, Edgard Ivan – Journal of Educational Data Mining, 2013
Tutoring systems are a common tool for delivering educational content and recent advances in this field include the detection of and reaction to learners' motivation. A data set derived from interactions in a tutoring system and its motivationally-aware variant provided opportunities to discover patterns of behavior in connection with motivational…
Descriptors: Data Analysis, Intelligent Tutoring Systems, Student Motivation, Interaction
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Forsyth, Carol M.; Graesser, Arthur C.; Pavlik, Philip, Jr.; Cai, Zhiqiang; Butler, Heather; Halpern, Diane; Millis, Keith – Journal of Educational Data Mining, 2013
Operation ARIES! is an Intelligent Tutoring System that is designed to teach scientific methodology in a game-like atmosphere. A fundamental goal of this serious game is to engage students during learning through natural language tutorial conversations. A tight integration of cognition, discourse, motivation, and affect is desired to meet this…
Descriptors: Intelligent Tutoring Systems, Scientific Methodology, Science Instruction, Educational Games
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Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C. – Journal of Educational Data Mining, 2013
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…
Descriptors: Student Behavior, Classification, Learner Engagement, Data Analysis