2018
DOI: 10.3991/ijet.v13i12.7918
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Personalized Ubiquitous Learning via an Adaptive Engine

Abstract: Nowadays, the world's population is increasingly waiting for permanent and constant access to information. Accessing the right information at any time and any place is becoming a necessity. A learning system is called ubiquitous if it is able to adapt itself to its context (user, platform, environment, device, etc.). In this sense, theories and methods of adaptations keep rolling in order to make learning processes more efficient and relevant. In this paper, we propose an approach for providing personalized co… Show more

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Cited by 18 publications
(9 citation statements)
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“…This highlights the potential of adaptive e-learning environments to support teachers' professional development and enhance their pedagogical practices Dhakshinamoorthy & Dhakshinamoorthy (2018) present an adaptive dynamic learning environment based on knowledge level and learning style, emphasizing the predominant use of learners' knowledge level and learning style characteristics in designing adaptive learning environments (Dhakshinamoorthy & Dhakshinamoorthy, 2018). This underscores the importance of leveraging learners' individual characteristics to create adaptive and personalized learning experiences Guabassi et al (2018) explore personalized ubiquitous learning via an adaptive engine, highlighting the adaptive ubiquitous learning system's ability to adapt to learner context information (Guabassi et al, 2018). This emphasizes the potential of adaptive ubiquitous learning systems to provide personalized and context-aware learning experiences.…”
Section: Adaptive Learning Environmentsmentioning
confidence: 96%
“…This highlights the potential of adaptive e-learning environments to support teachers' professional development and enhance their pedagogical practices Dhakshinamoorthy & Dhakshinamoorthy (2018) present an adaptive dynamic learning environment based on knowledge level and learning style, emphasizing the predominant use of learners' knowledge level and learning style characteristics in designing adaptive learning environments (Dhakshinamoorthy & Dhakshinamoorthy, 2018). This underscores the importance of leveraging learners' individual characteristics to create adaptive and personalized learning experiences Guabassi et al (2018) explore personalized ubiquitous learning via an adaptive engine, highlighting the adaptive ubiquitous learning system's ability to adapt to learner context information (Guabassi et al, 2018). This emphasizes the potential of adaptive ubiquitous learning systems to provide personalized and context-aware learning experiences.…”
Section: Adaptive Learning Environmentsmentioning
confidence: 96%
“…Due to the rapidly growing interest in the field of education [4][5] [6], there are several research studies have been conducted on predicting university admission based on several factors using supervised or unsupervised Machine Learning algorithms. Xiaojun Wu & Jing Wu [7] conducted a study on predicting students' selection criteria in non-native language MBA admission based on Ridge regression, SVM, Random forest, GBDT.…”
Section: Related Workmentioning
confidence: 99%
“…• Accessibility: Illustrates the Learner's Player disabilities and preferences such as (Language; Learning Styles; Disability). • Language attribute: Refers to learner's player's spoken language as it's one of the potential factors that can prevent the learning process [31] and many researchers shows that Learners are more motivated to learn when the story in the LG environment is in their native language [32]; Learning Style Attribute: Learning Style in this research refers to the favourite manner of learning. Designer can choose the best learning path according to the learner learning style, using the index learning styles of Felder-Solomon (ILS)(Felder and Silverman 1988) [33], assessment tool to identify it, in order to generate an adaptable UGL and to augment the learners player motivation [34] [18] and we have an attribute to define the mobile device platform profile (Mobile device characteristic's).…”
Section: Meadventure Extending Editor: Learner Player Model Layermentioning
confidence: 99%