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TopPersonalized Mobile Learning And Recommender System
Rapid proliferation of mobile and internet technology has potentially promoted the diverse learning approaches. Personalization in learning, boosts the interest in learning process which helps the learner to achieve their learning objective/outcomes (Muna, 2019). In this section, presents the overview of existing personalized mobile learning and recommender system.
Benlamri and Zhang (Benlamri & Zhang, 2014) presented context aware recommender system for mobile learning which has proactive context awareness mechanism that can sense both system centric and learner centric context and adopts the accessed services accordingly at run time. Personalization is achieved based on the learner’s background knowledge, preferences, previous learning activity, covered concepts, adapted learning path and consumed learning resources. Then the system will construct new learning sequence for learner that consist of optimized system-centric learning resources to fulfill the current learner’s activity and goal.
Inssaf El Guabassi et.al (El Guabassi et al., 2018) presented the personalized course content adaptation system which considers learner profile as the base for content adaption learner. The learner profile consists of four attributes such as Learning Style(Visual, Verbal, Global, Sequential, Reflexive, Intuitive, Active, Sensing), Cognitive Style (Text, Audio, Video), Cognitive State (Beginner, Intermediate, Expert) and Device context(Device, Activity, Environment) . Based on the learner profile, course content will be adapted.
Brita Curum et.al (Curum, Chellapermal, & Khedo, 2017) proposed mobile learning system in which personalization is achieved by adopting the course based on learner age (11-17 -Junior, 18-45 Adult and 46-65 Senior) Junior and Senior learners are presented with courses which carries basic explanations while adults are presented with explicitly detailed contents. Difficulty of the assessment questions depends on the quiz level (beginner, intermediate, advanced) and the user age group.