Designing an Daptive Mobile Learning Using Multiple Intelligence (MI) Dissertation

Designing an Daptive busy Learning Using Multiple Intelligence (MI) Theory - Dissertation ExampleUbiquitous learning is super contextual and involves multiple technologies like energetic, wireless, sensing, etc. Context-aware ubiquitous learning plan (CULP) is one such platform that is found to enhance the efficiency of learning (Gu et al., n.d. Hwang et al., 2011 Hwang et al., 2010). Howard Gardners multiple intelligence theory is a unsullied example by which to understand and teach many aspects of human intelligence, learning style, personality and behaviour - in education and industry (Chapman, 2009). Earlier tools were limited in providing real-time support through mutual coaction however, most recent tools for adaptive learning through multiple intelligences have state-of-the-art technologies enabling a real-world adaptive learning environment for CULP. Multiple Intelligences, arranged Intelligence, Fuzzy Logic and Neural Networks are as well used in developing ubiquit ous learning tools (Cabada et al., 2008). The models for such tools will be studied in this paper to help design a new model that can address the short-comings in the preface models. The proposed model for ubiquitous learning will be a comprehensive and highly adaptive model based on the use of multiple intelligences. ... FRAME) (Figure 1) addressed the issue of information overload, navigation of content and quislingism for gaining the relevant knowledge by focusing on the integration of mobile technology, human learning electrical condenser and social interactions. Figure 1 FRAME model for mobile learning. Source (Koole, 2011). Context-sensitive learning schedule framework, mCALS, uses context-aware spot and time information to schedule learning. Verification for the strict adherence of schedule for learning is also integrate in this framework (Yau et al., 2010). Sungs (2009) Ubiquitous Learning Environment (ULE) model uses MI theory and effectively combines the advantages th at an adaptive learning environment and ubiquitous computing have to offer along with the flexibleness of mobile devices. This combination enables learning collaborators, content and services to be available in a context-aware framework. musical composition the models discussed have been able to allow interaction based on context-awareness, Intelligent Tutoring System (ITS), similar to Artificial Intelligence (AI), has a low-end model called TenseITS which offers the flexibility of learning English tenses and does not take into consideration the individual needs of the learner in terms of location. C-POLMILE is another standard model of ITS that offers synchronized learning capableness over PC and a mobile environment, and is used for C programming. MoreMATHS is another extension of ITS, used for mobile revision for Maths as it offers a Math learning environment that is mainly on the PC with revision enabled on a mobile device. Similarly, SQL ITS is another customized ITS for MS SQL database administration that can be synchronised with the mobile device (Bull et al., 2004). Cui and Bull (2005) note that TenseITS adapts to the