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

Designing an Daptive Mobile Learning Using Multiple Intelligence (MI) Theory - Dissertation Example Ubiquitous learning is highly contextual and involves multiple technologies like mobile, wireless, sensing, etc. Context-aware ubiquitous learning platform (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 Gardner’s multiple intelligence theory is â€Å"a classical model 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 collaboration; 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, Artificial Intelligence, Fuzzy Logic and Neural Networks are also used in developing ubiquitous 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 present 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 collaboration for gaining the relevant knowledge by focusing on the integration of mobile technology, human learning capacity and social interactions. Figure 1: FRAME model for mobile learning. Source: (Koole, 2011). Context-sensitive learning schedule framework, mCALS, uses context-aware location and time information to schedule learning. Verification for the strict adherence of schedule for learning is also incorporated in this framework (Yau et al., 2010). Sung’s (2009) Ubiquitous Learning Environment (ULE) model uses MI theory and effectively combines the advantages that an adaptive learning environment and ubiquitous computing have to offer along with the flexibility of mobile devices. This combination enables learning collaborators, content and services to be available in a context-aware framework. While the models discussed have been able to allow interaction based on context-awaren ess, 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 synchronised learning capability 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