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Data retrieval can also be done after a specific request mobile recharge api by the user, which allows the user to provide different parameters such as scheduled disconnection time, predicted time when the system will remain offline, etc. Also, can be considered other parameters such as server control if a link has a high cost and base it behaves in different ways. In other words, it can synchronize the cache to get data when the connection is low cost local area network or cellular phone and to use the stored content when it is the high cost connection. You should still not return error messages when some web pages are not cached, but some informational messages such as The site is not available at this time. It will appear at the next interface. Another function of the server could be to record the user s activities and feedback with this mobile LMS information. For this reason, mobile user models must be responsible for meeting the needs of the user and adapting accordingly. The mobile LMS should be responsible for calculating and upgrading the user model, which will be different billpaymart from the basic model of the LMS.

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The role of the Hoarding algorithm in the system is to decide which texts the user mobile recharge api will prefer to study in an offline task and to retrieve them together with the necessary words. Each text consists of a small set of words in Edit. Still depending on the word, the user may ask to see only the translation, an example or a detailed analysis of it. Observing the user s behavior and plotting some common traffic methods in the system will help predict the Learning To object that will be needed in the future. Comparative analysis of the behavior of all users using the system and a particular user is very important. A possible criterion for modeling the user is the learning level of the user s language in if he is a beginner, middle or advanced. In this way, an analysis of what words people are never looking for in the same category will lead to their deviation from the hoarding algorithm and thus reduce the required mobile recharge api provider memory. And vice versa, if a word is used and searched very often by users of the same class, the algorithm should give it a high priority.

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One of the factors that should be taken into account in the analysis of the mobile recharge api provider learning style is the interest shown by a user in the particular material under consideration. ELD IT s main body of text contains texts with different subjects for both Italian and German such as food, beverages, economy, industry, culture, city, family, art. A useful criterion would be the time-consuming users to read these specific pages. If some learners are interested in some of these pages, they will devote enough time to studying the texts and the words they make. This will allow users with common interests to join the same group.

Different user models should store information see here about learner behavior. These different ELD IT user models are a separate XML file for each user that contains login information for each user login and password, how many times it has been entered and how many times it has left, the number of words it has seen, how many times he clicked on the time he was logged in, how many times he went to the various positions that exist for idioms, word derivatives, linguistic features that are broken down into notes. The recording and use of information about the mother tongue of users, their linguistic interests, their progress, their learning styles and other information are in the planning.