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D'Mello, Sidney K.; Lehman, Blair; Person, Natalie – International Journal of Artificial Intelligence in Education, 2010
We explored the affective states that students experienced during effortful problem solving activities. We conducted a study where 41 students solved difficult analytical reasoning problems from the Law School Admission Test. Students viewed videos of their faces and screen captures and judged their emotions from a set of 14 states (basic…
Descriptors: Video Technology, Electronic Learning, Handheld Devices, Student Attitudes
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McLaren, Bruce M.; Scheuer, Oliver; Miksatko, Jan – International Journal of Artificial Intelligence in Education, 2010
An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about topics presented by a teacher. These tools are aimed at teaching students how to discuss and argue, important skills not often taught in traditional classrooms. But how…
Descriptors: Artificial Intelligence, Cooperative Learning, Computer Mediated Communication, Discussion (Teaching Technique)
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Perez-Marin, Diana; Pascual-Nieto, Ismael – International Journal of Artificial Intelligence in Education, 2010
A student conceptual model can be defined as a set of interconnected concepts associated with an estimation value that indicates how well these concepts are used by the students. It can model just one student or a group of students, and can be represented as a concept map, conceptual diagram or one of several other knowledge representation…
Descriptors: Concept Mapping, Knowledge Representation, Models, Universities
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Heilman, Michael; Collins-Thompson, Kevyn; Callan, Jamie; Eskenazi, Maxine; Juffs, Alan; Wilson, Lois – International Journal of Artificial Intelligence in Education, 2010
The REAP tutoring system provides individualized and adaptive English as a Second Language vocabulary practice. REAP can automatically personalize instruction by providing practice readings about topics that match interests as well as domain-based, cognitive objectives. While most previous research on motivation in intelligent tutoring…
Descriptors: Incentives, Cognitive Objectives, Intelligent Tutoring Systems, Motivation
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Muldner, Kasia; Conati, Cristina – International Journal of Artificial Intelligence in Education, 2010
Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples effectively. The Example Analogy (EA)-Coach is an Intelligent Tutoring System that provides adaptive support to foster meta-cognitive behaviors relevant to a specific…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Cognitive Psychology, Thinking Skills
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Suraweera, Pramuditha; Mitrovic, Antonija; Martin, Brent – International Journal of Artificial Intelligence in Education, 2010
Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing them is a labour-intensive and time-consuming process. A major share of the effort is devoted to acquiring the domain knowledge that underlies the system's intelligence. The goal of this research is to reduce this knowledge acquisition bottleneck and better…
Descriptors: Intelligent Tutoring Systems, Programming, Engineering, Tutoring
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Johnson, W. Lewis – International Journal of Artificial Intelligence in Education, 2010
The Tactical Language and Culture Training System (TLCTS) helps learners acquire basic communicative skills in foreign languages and cultures. Learners acquire communication skills through a combination of interactive lessons and serious games. Artificial intelligence plays multiple roles in this learning environment: to process the learner's…
Descriptors: Second Language Learning, Educational Games, Intelligent Tutoring Systems, Artificial Intelligence
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du Boulay, Benedict; Avramides, Katerina; Luckin, Rosemary; Martinez-Miron, Erika; Rebolledo-Mendez, Genaro; Carr, Amanda – International Journal of Artificial Intelligence in Education, 2010
This paper describes a Conceptual Framework underpinning "Systems that Care" in terms of educational systems that take account of motivation, metacognition and affect, in addition to cognition. The main focus is on "motivation," as learning requires the student to put in effort and be engaged, in other words to be motivated to learn. But…
Descriptors: Learning Motivation, Metacognition, Affective Behavior, Schemata (Cognition)
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Khandaker, Nobel; Soh, Leen-Kiat – International Journal of Artificial Intelligence in Education, 2010
Two critical issues of the typical computer-supported collaborative learning (CSCL) systems are inappropriate selection of student groups and inaccurate assessment of individual contributions of the group members. Inappropriate selection of student groups often leads to ineffective and inefficient collaboration, while inaccurate assessment of…
Descriptors: Computer Uses in Education, Cooperative Learning, Selection, Grouping (Instructional Purposes)
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Chieu, Vu Minh; Luengo, Vanda; Vadcard, Lucile; Tonetti, Jerome – International Journal of Artificial Intelligence in Education, 2010
Cognitive approaches have been used for student modeling in intelligent tutoring systems (ITSs). Many of those systems have tackled fundamental subjects such as mathematics, physics, and computer programming. The change of the student's cognitive behavior over time, however, has not been considered and modeled systematically. Furthermore, the…
Descriptors: Foreign Countries, Medical Students, Surgery, Human Body
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