With the continuous advances in mobile technology, numerous applications are being developed to empower the mobile users. Mobile commerce (m-commerce) is starting to make a mark following the success of the internet and e-commerce. With this, application development aims to optimize the performance of the mobile environment such as PDA and cellular phones. Mobile advertisement is one example of mobile applications that provide a lot of potentials.In this paper, we introduce a new method of personalizing mobile advertisement by considering not only the user's location but also his/her interest.A prototype system called 'Concierge' has been developed and tested on a shopping environment. The result of the experiment shows good potential for this kind of service.

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In this paper, we propose a method to predict cognitive load and its factors affecting the learning efficiency in programming learning from the learning behavior of learners. Generally, since the concepts of programming are difficult for learners, some of them suffer inappropriate cognitive load to understand them. Although teachers must keep cognitive load of such learners appropriate, it is difficult for them to find learners who has inappropriate cognitive load from a large number of learners. To find learners with inappropriate cognitive load, we construct models with the random forest algorithm, using learning behavior collected from learners solving fill-in-the-blank tests. An experiment shows the models can detect cognitive load for IL and GL along with their factors. Teachers must address adjustment of cognitive load of learners. This result clarifies the learning factors affecting cognitive load of learners, which enables teachers to address the adjustment with small burdens.

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