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Ubiquitous computing could help the organisation and the mediation of social interactions wherever and whenever these situations might occur. Using those technologies enables the learning environment to be embedded in the real daily life. One of the most important ubiquitous technologies is radio frequency identification (RFID) tag, which is very useful and efficient to realise the ubiquitous computing, by mapping the real objects and the information into a virtual world. In the near future, RFID tags will be embedded in a lot of physical objects in order to trace the shipping of the products, and so forth. This paper proposes a computer assisted language learning (CALL) environment called tag added learning objects (TANGO). TANGO detects the objects around the learner using RFID tags, and assigns some questions to the learner related to the detected objects that he usually uses during the daily life to improve his vocabulary knowledge. Also, this environment allows the learners to share their knowledge through RFID tags and to learn a language with authentic and tangible objects. This environment is implemented and evaluated.

A student's learning style is the approach for learning that best allows the student to gather and to understand knowledge in a specific manner. Providing students with learning materials and activities that fit to their learning styles seems to have high potential to make learning easier for them. This research aims at providing teachers with recommendations on how to best extend their existing e-courses in learning management systems to accommodate more students with different learning styles. A smart e-course recommender tool has been developed for this purpose, which analyzes the e-courses with respect to their support levels for different students' learning styles, recommends learning objects to be added to the courses, and visualizes the recommendations and the improvement in the course support level for students' with different learning styles. The experimental results indicate that the tool has the ability to recommend suitable learning objects that,

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Student modeling and context modeling play an important role in adaptive and smart learning systems, enabling such systems to provide courses and recommendations that fit students' characteristics and consider their current context. In this chapter, three approaches are presented to automatically analyze learners' characteristics and courses in learning systems based on learners' cognitive abilities, learning styles, and context. First, a framework and a system are presented to automatically identify students' working memory capacity (WMC) based on their behavior in a learning management system. Second, a mechanism and an interactive tool are described for analyzing course contents in learning management systems (LMSs) with respect to students' learning styles. Third, a framework and an application are presented that build a comprehensive context profile through detecting available features of a device and tracking the usage of these features. All three approaches contribute toward building a foundation for providing learners with intelligent, adaptive, and personalized support based on their cognitive abilities, learning styles, and context.

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