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Kumar, R.; Rose, C. P. – IEEE Transactions on Learning Technologies, 2011
Tutorial Dialog Systems that employ Conversational Agents (CAs) to deliver instructional content to learners in one-on-one tutoring settings have been shown to be effective in multiple learning domains by multiple research groups. Our work focuses on extending this successful learning technology to collaborative learning settings involving two or…
Descriptors: Educational Technology, Computer Software, Computer Software Evaluation, Programming
Khandaker, N.; Soh, Leen-Kiat; Miller, L. D.; Eck, A.; Jiang, Hong – IEEE Transactions on Learning Technologies, 2011
Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant…
Descriptors: Web 2.0 Technologies, Instructional Design, Program Implementation, Cooperative Learning
Graesser, Arthur C.; Jeon, Moongee; Dufty, David – Discourse Processes: A Multidisciplinary Journal, 2008
During the last decade, interdisciplinary researchers have developed technologies with animated pedagogical agents that interact with the student in language and other communication channels (such as facial expressions and gestures). These pedagogical agents model good learning strategies and coach the students in actively constructing knowledge…
Descriptors: Intelligent Tutoring Systems, Dialogs (Language), Interactive Video, Animation
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)
Lavesson, N. – IEEE Transactions on Education, 2010
This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…
Descriptors: Artificial Intelligence, Diagnostic Tests, Foreign Countries, Self Evaluation (Individuals)
Behrend, Tara S.; Thompson, Lori Foster – International Journal of Training and Development, 2012
Animated agents have the potential to increase engagement and learning during online training by acting as personalized tutors. However, little is known about the conditions that make these agents most effective. In this study, 183 e-learners completed a Microsoft Excel training course. Approximately half were assigned an agent with predetermined…
Descriptors: Computer Assisted Instruction, Self Efficacy, Learner Controlled Instruction, Feedback (Response)
Prakash, Edmond; Brindle, Geoff; Jones, Kevin; Zhou, Suiping; Chaudhari, Narendra S.; Wong, Kok-Wai – Simulation & Gaming, 2009
Games technology has undergone tremendous development. In this article, the authors report the rapid advancement that has been observed in the way games software is being developed, as well as in the development of games content using game engines. One area that has gained special attention is modeling the game environment such as terrain and…
Descriptors: Artificial Intelligence, Games, Video Technology, Technological Advancement
Knobel, Cory Philip – ProQuest LLC, 2010
Living inside built environments--infrastructure--it is easy to take for granted the things that we do not need to engage, but are at work behind the scenes nonetheless. Well-designed systems become invisible, but to engage them, how do we know which perspectives, objects, and relationships are useful? I examine the University of Michigan Digital…
Descriptors: Electronic Libraries, Organizational Change, Library Science, Library Services
Leung, Chun Ming; Tsang, Eva Y. M.; Lam, S. S.; Pang, Dominic C. W. – EDUCAUSE Quarterly, 2010
Universities are increasingly looking into self-service systems with intelligent digital agents to supplement or replace labor-intensive services, such as academic counseling. The Open University of Hong Kong has developed an intelligent online system that instantly responds to enquiries about career development, learning modes, program/course…
Descriptors: Counseling Services, Learning Modalities, Foreign Countries, Natural Language Processing
Elayeb, Bilel; Evrard, Fabrice; Zaghdoud, Montaceur; Ahmed, Mohamed Ben – Interactive Technology and Smart Education, 2009
Purpose: The purpose of this paper is to make a scientific contribution to web information retrieval (IR). Design/methodology/approach: A multiagent system for web IR is proposed based on new technologies: Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). This system is based on a possibilistic qualitative approach which extends the…
Descriptors: Information Retrieval, Models, College Students, Internet
Monteserin, Ariel; Schiaffino, Silvia; Amandi, Analia – Computers & Education, 2010
In CSCL systems, students who are solving problems in group have to negotiate with each other by exchanging proposals and arguments in order to resolve the conflicts and generate a shared solution. In this context, argument construction assistance is necessary to facilitate reaching to a consensus. This assistance is usually provided with isolated…
Descriptors: Persuasive Discourse, Problem Solving, Cooperative Learning, Artificial Intelligence
Essa, Alfred; Ayad, Hanan – Research in Learning Technology, 2012
The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50-60%. At the college level in the US only 30% of students graduate from…
Descriptors: Artificial Intelligence, Computer Graphics, Computer Interfaces, Statistical Analysis
Simonson, Michael, Ed.; Seepersaud, Deborah, Ed. – Association for Educational Communications and Technology, 2019
For the forty-second time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented at the annual AECT Convention in Las Vegas, Nevada. The Proceedings of AECT's Convention are published in two volumes. Volume 1 contains 37 papers dealing…
Descriptors: Educational Technology, Technology Uses in Education, Research and Development, Elementary Education
Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo; Jantke, Klaus P. – Journal of Educational Computing Research, 2010
University education often suffers from a lack of an explicit and adaptable didactic design. Students complain about the insufficient adaptability to the learners' needs. Learning content and services need to reach their audience according to their different prerequisites, needs, and different learning styles and conditions. A way to overcome such…
Descriptors: Prerequisites, College Instruction, Educational Experiments, Cognitive Style
Russell, Ingrid; Markov, Zdravko; Neller, Todd; Coleman, Susan – ACM Transactions on Computing Education, 2010
Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that…
Descriptors: Artificial Intelligence, Program Effectiveness, Computer Science, Teaching Methods