Empirical Analysis of Context based Content Delivery for M-Learning Scenarios using ANFIS

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Today's world is unusually popular with the internet and mobile devices in everyday life. It offers unprecedented possibilities learning with mobility. This kind of learning can be called (Mobile Learning) at any point in the world. Meeting learners ' needs in the current scenario in which collaborative electronic and mobile education systems have become more evolving. Every learners ' needs differ in terms of learning; context, content, learning styles, speed of learning, even including preferences, places. Mobile learning enables the learner to learn while moving, enabling the learner to learn in any time and any place. Learning styles have evolved along with advances in technology; specifically advances in mobile technology and mobile networks. Portable devices such as mobile phones, tabs, iPods, etc. are commonly used today by all. The way we learn has been changed with the use of these devices in education. In M-learning environment the learner has access to the contents everywhere and every time through mobile devices. Customization and learner context awareness are the important factors in providing the learner with relevant content. Appropriate content delivery based on a learner's context is a complex process. So a content delivery system is needed that takes into account learners ' needs such as context, style and devices features. To model such a system neural network with fuzzy reasoning can be used, to accommodate the dynamically changing learning styles, contexts and characteristics of smart device. If-then conditions can be formed to build the suggestion rules required for such a content delivery system. ANFIS i.e. Artificial Nero Fuzzy Inference System is an integral asset to create fuzzy systems with IF-THEN guidelines. To model and analyze this type of context aware and adaptive content delivery system for an M-learning environment, ANFIS can be used. In this article, use of ANFIS tool is demonstrated for various m-learning scenarios with different contexts. Four different contexts are constructed based on the inputs given by the student learners. Using ANFIS these four scenarios have been analyzed empirically for their performance based on the RMSE of various membership functions.