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Molenaar, Inge; Roda, Claudia; van Boxtel, Carla; Sleegers, Peter – Computers & Education, 2012
The aim of this study is to test the effects of dynamically scaffolding social regulation of middle school students working in a computer-based learning environment. Dyads in the scaffolding condition (N=56) are supported with computer-generated scaffolds and students in the control condition (N=54) do not receive scaffolds. The scaffolds are…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Metacognition, Learning Processes
Fulgham, Susan M.; Shaughnessy, Michael F. – Educational Technology, 2013
Stavros Demetriadis is currently an Assistant Professor with the Department of Informatics, Aristotle University of Thessaloniki (AUTh) in Greece. He also earned his Bachelor's degree in Physics and a Master Diploma in Electronic Physics from AUTh. He became interested in information and communications technologies when he was a high school…
Descriptors: Foreign Countries, Profiles, Educational Technology, Technology Integration
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Belghith, K.; Nkambou, R.; Kabanza, F.; Hartman, L. – IEEE Transactions on Learning Technologies, 2012
Roman Tutor is a tutoring system that uses sophisticated domain knowledge to monitor the progress of students and advise them while they are learning how to operate a space telerobotic system. It is intended to help train operators of the Space Station Remote Manipulator System (SSRMS) including astronauts, operators involved in ground-based…
Descriptors: Foreign Countries, Computer Uses in Education, Instructional Materials, Space Exploration
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El Saadawi, Gilan M.; Azevedo, Roger; Castine, Melissa; Payne, Velma; Medvedeva, Olga; Tseytlin, Eugene; Legowski, Elizabeth; Jukic, Drazen; Crowley, Rebecca S. – Advances in Health Sciences Education, 2010
Previous studies in our laboratory have shown the benefits of immediate feedback on cognitive performance for pathology residents using an intelligent tutoring system (ITS) in pathology. In this study, we examined the effect of immediate feedback on metacognitive performance, and investigated whether other metacognitive scaffolds will support…
Descriptors: Feedback (Response), Control Groups, Intervention, Intelligent Tutoring Systems
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Ward, W.; Cole, R.; Bolanos, D.; Buchenroth-Martin, C.; Svirsky, E.; Van Vuuren, S.; Weston, T.; Zheng, J.; Becker, L. – Grantee Submission, 2011
This paper describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute…
Descriptors: Elementary School Science, Elementary School Students, Science Education, Intelligent Tutoring Systems
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Lee, Young-Jin – Journal of Educational Technology Systems, 2011
In the last decades, many education researchers have been trying to use computerized learning environments to enhance student learning. Without proper instructional supports and guidance, however, students often failed to acquire knowledge from computer-based learning activities. The objective of this study was to demonstrate how research-based…
Descriptors: Learning Activities, Formative Evaluation, Physics, Misconceptions
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Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia – Journal of Educational Technology Systems, 2011
LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms…
Descriptors: Foreign Countries, Web Based Instruction, Distance Education, Intelligent Tutoring Systems
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Ting, Choo-Yee; Phon-Amnuaisuk, Somnuk – Computers & Education, 2009
There has been an increasing interest in employing decision-theoretic framework for learner modeling and provision of pedagogical support in Intelligent Tutoring Systems (ITSs). Much of the existing learner modeling research work focuses on identifying appropriate learner properties. Little attention, however, has been given to leverage Dynamic…
Descriptors: Decision Support Systems, Computer Simulation, Intelligent Tutoring Systems, Computer Interfaces
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Lane, H. Chad; Hays, Matthew Jensen; Core, Mark G.; Auerbach, Daniel – Journal of Educational Psychology, 2013
In the context of practicing intercultural communication skills, we investigated the role of fidelity in a game-based, virtual learning environment as well as the role of feedback delivered by an intelligent tutoring system. In 2 experiments, we compared variations on the game interface, use of the tutoring system, and the form of the feedback.…
Descriptors: Feedback (Response), Fidelity, Intercultural Communication, Communication Skills
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Arroyo, Ivon; Burleson, Winslow; Tai, Minghui; Muldner, Kasia; Woolf, Beverly Park – Journal of Educational Psychology, 2013
We provide evidence of persistent gender effects for students using advanced adaptive technology while learning mathematics. This technology improves each gender's learning and affective predispositions toward mathematics, but specific features in the software help either female or male students. Gender differences were seen in the students' style…
Descriptors: Gender Differences, Educational Technology, Technology Uses in Education, Mathematics Instruction
Pavlik, Philip, Jr.; Toth, Joe – Online Submission, 2010
The plethora of different subfields in intelligent tutoring systems (ITS) are often difficult to integrate theoretically when analyzing how to design an intelligent tutor. Important principles of design are claimed by many subfields, including but not limited to: design, human-computer interaction, perceptual psychology, cognitive psychology,…
Descriptors: Intelligent Tutoring Systems, Interdisciplinary Approach, Design, Computer Interfaces
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Chi, Min; VanLehn, Kurt – Educational Technology & Society, 2010
Certain learners are less sensitive to learning environments and can always learn, while others are more sensitive to variations in learning environments and may fail to learn (Cronbach & Snow, 1977). We refer to the former as high learners and the latter as low learners. One important goal of any learning environment is to bring students up…
Descriptors: Intelligent Tutoring Systems, Physics, Probability, Tutoring
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Wang, Ya-huei; Liao, Hung-Chang – British Journal of Educational Technology, 2011
In the conventional English as a Second Language (ESL) class-based learning environment, teachers use a fixed learning sequence and content for all students without considering the diverse needs of each individual. There is a great deal of diversity within and between classes. Hence, if students' learning outcomes are to be maximised, it is…
Descriptors: Cognitive Style, Learning Motivation, Learning Processes, Individualized Instruction
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Klinkenberg, S.; Straatemeier, M.; van der Maas, H. L. J. – Computers & Education, 2011
In this paper we present a model for computerized adaptive practice and monitoring. This model is used in the Maths Garden, a web-based monitoring system, which includes a challenging web environment for children to practice arithmetic. Using a new item response model based on the Elo (1978) rating system and an explicit scoring rule, estimates of…
Descriptors: Test Items, Reaction Time, Scoring, Probability
Nugent, Rebecca; Ayers, Elizabeth; Dean, Nema – International Working Group on Educational Data Mining, 2009
In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. We adopt a faster, simpler approach:…
Descriptors: Data Analysis, Students, Skills, Cluster Grouping
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