Adaptive Mobile Learning Using Multiple Intelligence Theory - Dissertation Example

?Designing an adaptive mobile learning using Multiple Intelligence (MI) theory Introduction Mobile and wireless technologies have made life easier for learning, and students are increasingly taking to this fad as mobile technology penetration can be found to be the greatest among this segment. Also, the convenience of learning anywhere anytime or ubiquitous learning is driving mobile learning. However, user preferences need highly customized content to be delivered along with the unique format for mobile viewing, and this requires use of multiple intelligences that aid in adapting the learning environment according to the user learning styles or preferences (Spaniol, et al., 2009; Bull and McCormick, 2011; Hwang and Tsai, 2011). 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 “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. Mobile learning technologies in the market Kim (2009) designed a mobile learning model based on a four stage cyclical action model called strategize, apply, evaluate and reflect; to design, test and enhance the learning model. Koole’s (2011) model, the Framework for the Rational Analysis of Mobile Education (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-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 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 learner’s model of interaction according to the contextual features of location, probability of interruption, amount of time for study and the level of concentration at the location that the learner is likely to have. This is not contrary to the observations of Bull et al. (2004), as the content or settings for a particular location can be changed by a user based on the learner’s ability to study at that location. Learner model (Bull et al., 2011) uses PC and mobile environments where the main learning is on the PC enabled with interaction with the mobile learning environment for customized consultation based on synchronization of the open learner model and mobile learning. MobiGlam is another mobile learning environment model that is interoperable with Virtual Learning Environments (VLEs) and uses Bayesian Networks (BNs) that detect learning styles using the web platform (Meawad and Stubbs, 2006). Barker et al. (2011) proposed a theoretical model for m-learning that uses mobile environments with wireless technologies based on a context model of learning where traditional learning through PCs is supported by policies and guidelines of m-learning to enable learner-to-learner and learner-to-teacher communication. MOBIlearn provides a learning environment with re-usable architecture in context-aware components that use ultrasound tracking system alongside the context-aware hierarchical model to customize the content based on the learner’s current situation, preferences and learning history. This model is capable of suggesting collaborative activities based on the proximity and context (Lonsdale, et al., 2011). The User model is the simplistic learning model that takes into account the user preferences, knowledge, background, interests, expertise, learning styles, cognitive traits, etc. which distinguish each user. This dimension to e-learning is relevant to m-learning with the added contextual information of the user and other dimensions of adaptivity for m-learning (Drira et al., 2006). The Task model uses the socio-cognitive engineering design method which is similar to contextual design by Beyer &Holtzblatt (1998), and is illustrated by the Waycott’s tool integration procedure. This procedure applies Engestrom’s expansive model based on Activity theory to demonstrate integrating wireless technology for learning and working with knowledge. This model uses the fact that there is a demarcation between required functionalities and their embodiment in wireless technology as is the case in any technology; and this helps the user integrate the two spaces of learning: functionalities (semiotic) and the embodiments of these functionalities using devices (technological), which are the aspects of context (Taylor et al., 2011). Personalized Intelligent Mobile-Learning System (PIMS) uses Fuzzy Item Response Theory (FIRT) that evaluates the learner’s reading abilities of English news (Chen and Hsu, 2008). Personalised On-the-job Reflective Mobile Learning (PORML) framework uses Activity theory applied to emergency services like fire and rescue services where crew members need to interact with the framework based on the past and present risk assessment experiences (Eamsinvattana, Dimitrova and Allen, 2011). Scenario, Message, Synchronization, and Evaluation (SMSE) model facilitates mobile learning through these four major processes. This model for instructional design (Figure 2) helps instructors to increase efficiency, integrate mobile learning and other learning activities and also increase online learning experience for distance education students (Shih, 2005). Figure 2: SMSE instructional design model. Source: (Shih, 2005). Technology Acceptance Model (TAM) is a value-based m-learning model that is used to evaluate the design of the m-learning environment on the basis of meeting the user’s needs and his willingness to pay for the services (Philipp et al., 2011). Adapted Content Model and Contextual Content Model, both are used in to customize content to the context of the user and the device being used. While adapted content model adapts the content related to the capabilities of the mobile device, contextual model adapts the content based on the learner’s preferences and capabilities along with the content adapted for the device (Zhao and Okamoto, 2008). The Context model by Economides (2009) takes into consideration the states of the learner, infrastructure, educational activity and the environment by sensing, and further adapts to define the context. UTAUT Research Model utilizes the UTAUT IT Acceptance Model which includes Theory of Reasoned Action (TRA) Model, Technology Acceptance Model 2 (TAM2), Theory of Planned Behaviour (TPB) Model, Combined TAM and TPB Model (C-TAM-TPB), The Performance Model, Moderator Model among others to assess effectiveness in m-learning as these models use theoretical aspects related to Information Systems, Psychology and Sociology (Williams, 2009). Personalised Ubiquitous Learning Platform (PULP) is an intelligent m-learning environment that enables adaptive personalization, collaborative learning, and human computer interaction to enhance learning experience (Ayoola and Pehlan, 2011). Conceptualised model considers the social presence awareness in an Instant Messaging (IM) environment that is independent of context and where the learners and tutors are both aware of the presence of the others on the social network, ready to IM (Kekwaletswe, 2007). Adaptive Mobile Learning System(AMLS) is similar to the Adapted and Contextual Content Model as it implements the Bayesian inference theory and adaptive content technology in a web-based m-learning environment to create a dynamic learning model that personalizes the content over heterogeneous mobile devices (Huang and Hsieh, 2011). CALA-ONT Model uses conceptual architecture along with Ontology Context Model to provide context aware learning environment that also enables ubiquitous computing (Hoong and Cho, 2008). Shih’s mobile learning model, The multi-agent model, AEFIRIP, Open learner model, Information System (IS) model, DeLone and McLean’s model and M-learning all are considered by Udanor and Nwodoh (2011) as m-learning models that form the basis for the development of future models that are intelligent and easy-to-use models for both learner-learner and learner-teacher learning environments. The Low-Level Context Model, the Medium-Level Context Model, and the World Model contribute to the higher-level contextual information for the Context model using service applications and Adaptive Dialogue Manager (ADM) components, accessed through the Context Reasoner (Pederson et al., 2011). m-LOCO is ontology-based framework capturing contextual information in a socially-enhanced self-regulated learning environment through a set of integrated ontology-based repositories (Siadaty et al., 2011). Each of the models or frameworks for ubiquitous learning caters to a set of requirements for mobile learning but fail to address scalability and complexity of such learning in real-time, where time is limited and learning preferences and styles vary as much as the individual learner and learning environments. Proposed mobile learning model “We have to discuss about MI theory in our proposed model ” The proposed m-learning model is based on the above discussed models to incorporate multiple intelligences based on a standard Learner model that is highly contextual and built around a Context model using a Context Reasoner implemented in the four stages of a cyclical action: strategize, apply, evaluate and reflect. This proposed model addresses the information overload, navigation of highly customized content and member collaboration for gaining the relevant knowledge that can be used primarily on the PCs with ubiquitous computing enabled for heterogeneous mobile devices based on a set of policies and guidelines for m-learning. Ultra-sound tracking systems can be used by utilizing Activity theory and ontology-based repositories for implementing Engestrom’s expansive model to sense the learner, infrastructure, educational activity and the environment states of learner in relation to the UTAUT Research model that takes into consideration, aspects of Information Systems, Psychology and Sociology to develop the ITS. This proposed model allows human computer interaction, uses FIRT, Bayesian inference theory and adaptive content technologies and implements SMSE to suggest collaborative activities based on the proximity and context while verifying for the strict adherence of schedule. This model also has the more standard capability of the Conceptualised model with the value-added features for which learners are willing to pay, once the relevance of such a model is established to the ubiquitous learning community. Figure 3 below represents the proposed m-learning model: Figure 3: m-learning model. Conclusion This paper discussed the multiple intelligences applied in the ubiquitous, mobile learning environment through a study of the various m-learning models that help ubiquitous learning through learner-learner and learner-tutor interactions. Highly customized content based on the context and learning device is the need always, and the proposed model is intended to cater to this need for contextual content that can be used on heterogeneous devices like the PC and mobile devices using an array of theories or frameworks discussed. References 1 Chapman, Alan. 2009. Howard Gardner's multiple intelligences. www.businessballs.com 2 Spaniol, M., Q. Li, et al. A Machine Learning Based Framework for Adaptive Mobile Learning. Advances in Web Based Learning – ICWL 2009, Springer Berlin / Heidelberg. 5686:34-43. 2009. 3 Bull, P. H. and C. McCormick "Mobile Learning: Enhancing a Pre-Algebra Course at a Community College with Text Messaging." 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