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Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Education and Information Technologies, 2020
With the emergence of technology, the personalization of e-learning systems is enhanced. These systems use a set of parameters for personalizing courses. However, in literature, these parameters are not based on classification and optimization algorithms to implement them in the cloud. Cloud computing is a new model of computing where standard and…
Descriptors: Electronic Learning, Internet, Information Storage, Models
Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Smart Learning Environments, 2022
Personalized learning systems use several components in order to create courses adapted to the learners'characteristics. Current emphasis on the reduction of costs of development of new resources has motivated the reuse of the e-learning personalization components in the creation of new components. Several systems have been proposed in the…
Descriptors: Individualized Instruction, Technology Uses in Education, Electronic Learning, Mathematics
Tortorella, Richard A. W.; Kinshuk; Chen, Nian-Shing – Education and Information Technologies, 2018
Today people learn in many diverse locations and contexts, beyond the confines of classical brick and mortar classrooms. This trend is ever increasing, progressing hand-in-hand with the progress of technology. Context-aware learning systems are systems which adapt to the learner's context, providing tailored learning for a particular learning…
Descriptors: Electronic Learning, Educational Technology, Context Effect, Models
Kinshuk; Jesse, Ryan – International Review of Research in Open and Distance Learning, 2013
E-learning technologies have allowed authoring and playback of standardized reusable learning objects (RLO) for several years. Effective mobile learning requires similar functionality at both design time and runtime. Mobile devices can play RLO using applications like SMILE, mobile access to a learning management system (LMS), or other systems…
Descriptors: Foreign Countries, Electronic Learning, Educational Technology, Resource Units
Mac Callum, Kathryn; Jeffrey, Lynn; Kinshuk – Journal of Information Technology Education: Research, 2014
As mobile technology has advanced, awareness is growing that these technologies may benefit teaching and learning. However, despite this interest, the factors that will determine the acceptance of mobile technology by lecturers have been limited. This study proposed and tested a new model that extends the technology acceptance model (TAM) with…
Descriptors: Adoption (Ideas), Electronic Learning, Performance Factors, Technological Literacy
Hastie, Megan; Hung, I-Chun; Chen, Nian-Shing; Kinshuk – Innovations in Education and Teaching International, 2010
Educators and students living in the digital age are faced with complex problems that are forcing them to seek collaborative solutions. These problems can be addressed through the successful application of digital technologies and pedagogies that enhance the educational, social and economic prospects of students. The main aim of this study was to…
Descriptors: International Cooperation, Educational Technology, Distance Education, Blended Learning
Graf, Sabine; Viola, Silvia Rita; Leo, Tommaso; Kinshuk – Journal of Research on Technology in Education, 2007
Learning styles are increasingly being incorporated into technology-enhanced learning. Appropriately, a great deal of recent research work is occurring in this area. As more information and details about learning styles becomes available, learning styles can be better accommodated and integrated into all aspects of educational technology. The aim…
Descriptors: Cognitive Style, Legislators, Educational Technology, Learning Processes
Viola, Silvia Rita; Graf, Sabine; Kinshuk; Leo, Tommaso – Interactive Technology and Smart Education, 2007
Learning styles are incorporated more and more in e-education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important…
Descriptors: Cognitive Style, Validity, Questionnaires, Correlation
Karampiperis, Pythagoras; Lin, Taiyu; Sampson, Demetrios G.; Kinshuk – Innovations in Education and Teaching International, 2006
Adaptive cognitive-based selection is recognized as among the most significant open issues in adaptive web-based learning systems. In order to adaptively select learning resources, the definition of adaptation rules according to the cognitive style or learning preferences of the learners is required. Although some efforts have been reported in…
Descriptors: Cognitive Style, Models, Web Based Instruction, Learning Strategies