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Latham, Annabel; Crockett, Keeley; McLean, David; Edmonds, Bruce – Computers & Education, 2012
This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and…
Descriptors: Cognitive Style, Teaching Methods, Cognitive Measurement, Prediction
Wong, Lung-Hsiang; Looi, Chee-Kit – Interactive Learning Environments, 2012
The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…
Descriptors: Electronic Learning, Entomology, Educational Technology, Individualized Instruction
Deliyska, Boryana; Manoilov, Peter – International Journal of Distance Education Technologies, 2010
The intelligent learning systems provide direct customized instruction to the learners without the intervention of human tutors on the basis of Semantic Web resources. Principal roles use ontologies as instruments for modeling learning processes, learners, learning disciplines and resources. This paper examines the variety, relationships, and…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Curriculum Development, Lesson Plans
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
Magnisalis, I.; Demetriadis, S.; Karakostas, A. – IEEE Transactions on Learning Technologies, 2011
This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence…
Descriptors: Evidence, Instructional Design, Bibliographic Databases, Classification
Rey-Lopez, Marta; Brusilovsky, Peter; Meccawy, Maram; Diaz-Redondo, Rebeca; Fernandez-Vilas, Ana; Ashman, Helen – International Journal on E-Learning, 2008
Current e-learning standardization initiatives have put much effort into easing interoperability between systems and the reusability of contents. For this to be possible, one of the most relevant areas is the definition of a run-time environment, which allows Learning Management Systems to launch, track and communicate with learning objects.…
Descriptors: Instructional Design, Integrated Learning Systems, Educational Technology, Artificial Intelligence
Li, Frederick W. B.; Lau, Rynson W. H.; Dharmendran, Parthiban – International Journal of Distance Education Technologies, 2010
Existing adaptive e-learning methods are supported by student (user) profiling for capturing student characteristics, and course structuring for organizing learning materials according to topics and levels of difficulties. Adaptive courses are then generated by extracting materials from the course structure to match the criteria specified in the…
Descriptors: Electronic Learning, Programming, Profiles, Student Characteristics
Lynch, Collin; Ashley, Kevin D.; Pinkwart, Niels; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2009
In this paper we consider prior definitions of the terms "ill-defined domain" and "ill-defined problem". We then present alternate definitions that better support research at the intersection of Artificial Intelligence and Education. In our view both problems and domains are ill-defined when essential concepts, relations, or criteria are un- or…
Descriptors: Definitions, Artificial Intelligence, Problem Solving, Educational Research
Zhao, Guopeng; Ailiya; Shen, Zhiqi – Educational Technology & Society, 2012
Teachable agent is a type of pedagogical agent which instantiates Learning-by-Teaching theory through simulating a "naive" learner in order to motivate students to teach it. This paper discusses the limitation of existing teachable agents and incorporates intrinsic motivation to the agent model to enable teachable agents with initiative…
Descriptors: Foreign Countries, Instructional Design, Artificial Intelligence, Electronic Learning
Ozpolat, Ebru; Akar, Gozde B. – Computers & Education, 2009
A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user's browsing…
Descriptors: Cognitive Style, Classification, Measures (Individuals), Measurement Techniques
Su, Jun-Ming; Lin, Huan-Yu; Tseng, Shian-Shyong; Lu, Chia-Jung – Turkish Online Journal of Educational Technology - TOJET, 2011
Promoting the development of students' scientific inquiry capabilities is a major learning objective in science education. As a result, teachers require effective assessment approaches to evaluate students' scientific inquiry-related performance. Teachers must also be able to offer appropriate supplementary instructions, as needed, to students.…
Descriptors: Foreign Countries, Web Based Instruction, Portfolios (Background Materials), Learning Problems
The Social Semantic Web in Intelligent Learning Environments: State of the Art and Future Challenges
Jovanovic, Jelena; Gasevic, Dragan; Torniai, Carlo; Bateman, Scott; Hatala, Marek – Interactive Learning Environments, 2009
Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments…
Descriptors: Models, Interaction, Educational Technology, Design Requirements
Stankov, Slavomir; Rosic, Marko; Zitko, Branko; Grubisic, Ani – Computers & Education, 2008
Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student's characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the…
Descriptors: Elementary Secondary Education, Intelligent Tutoring Systems, Artificial Intelligence, Tutoring
Hayashi, Yusuke; Bourdeau, Jacqueline; Mizoguchi, Riichiro – International Journal of Artificial Intelligence in Education, 2009
This paper describes the achievements of an innovative eight-year research program first introduced in Mizoguchi and Bourdeau (2000), which was aimed at building a theory-aware authoring system by using ontological engineering. To date, we have proposed OMNIBUS, an ontology that comprehensively covers different learning/instructional theories and…
Descriptors: Foreign Countries, Theory Practice Relationship, Engineering, Teaching Methods
Boticario, Jesus G.; Santos, Olga C. – Journal of Interactive Media in Education, 2007
Adaptive LMS have not yet reached the eLearning marketplace due to methodological, technological and management open issues. At aDeNu group, we have been working on two key challenges for the last five years in related research projects. Firstly, develop the general framework and a running architecture to support the adaptive life cycle (i.e.,…
Descriptors: Foreign Countries, Learner Controlled Instruction, Computer Software, Educational Technology
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