الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Many studies have addressed e-learning, aiming to create a platform for the learning process that completes the traditional classroom work and maximizes the effectiveness of learning outcomes. Striving to achieve such platform, studies have considered gamification and personalizing the educational resources for the adaptation of educational systems as per the intended learners through intensive learning analytics. In the events of the recent incident that is the Coronavirus pandemic, e-learning systems have proven to be an essential pillar for education. This raises to surface what many studies have addressed earlier; creating a platform that completes the traditional classroom work and maximizes the effectiveness of learning outcomes. Gamification of the personalized adaptable educational systems have been recently considered to keep the learners motivated and positively progressing in a flow state. However, the current models remain inadequate, providing limited resources for comprehensive learning analytics. Some considered the educational games without fully covering the different learning styles of learners, others did consider learning styles but were domain-specific educational games, while others targeted the adaptation in order to personalize the educational process without mentioning the gamification concept.
In this thesis, the Personalized Adaptive Gamified E-learning (PAGE) model is proposed to provide an infrastructure for a new generation of learning style-based adaptive educational game systems and to extend the Massive Open Online Courses (MOOCs) by providing new satisfactory levels of learning analytics and visualization in the rich e-learning process that supports the learner’s intervention in the resultant learning analytics. It is a generalized, domain-independent model, in such a way that keeps all interested entities engaged through a gamified and personalized learning process. The PAGE model supports the blended learning by encouraging the involvement of the traditional learning process’s parties, where effective learning analytics can be sustained to continuously improve the learning experience. The proposed model has been evaluated for its value and usability through a survey from different perspectives as a preliminary evaluation to proceed with the expected vast effort of inter- disciplinary development. In addition, the proposed Learning analytics have been developed to make the necessary adaptation to the course and learner’s learning flow, as well as visualizing the process and adaptation decisions to the learners. Results show a positive potential towards learning adaptation and visualization, and a necessity to provide an additional focus for the gamification concept.