“NARIKIRI English! Project”
This is an English educational tool for training business people. It enables them to learn English by using their cell phones. The key feature is that appropriate usages of English phrases are compiled in story telling skits for the user to listen to. Without a doubt, “You will find whatever English you will confront in the future.”
In this project the advocated method is the use of a series of skits for listening comprehension. CESP: (Contextual English for Specific Purpose) is in fact “English matching your specifications: You will find whatever English you will confront in the future.” This means that this educational tool will let you master precisely the English that is necessary for your own business.
In developing CESP, by interviewing CEOs and personnel directors, we investigated business activities, future prospects, management strategies and contextual English that had a high probability of usage by businessmen. Based on this research, scenarios were created and younger employers screened the substance of these scenarios in order to enhance the credibility of it actually taking place in their companies.
Content is installed in cell phones (smart phones) for use by businessmen who are busy but want to learn whenever they can.
In fiscal 2006, the process of development, practical use and format evaluation was conducted with Benesse Corporation. We now plan to verify the effectiveness of CESP.
“Learning Strategy Navigator Project”
This is a feedback system for learning that uses data mining. It analyzes massive data by data mining and based on the results, it diagnosis the learning plan for the individual student and provides information deemed to be favorable to his/her learning scheme.
Because our project aims to construct a total educational system, rather than adopting mobile ubiquitous technology that provides an interface much closer to the users, we take the opposite end of the spectrum, that is, we do research on the use of user server-side technology.
In fiscal 2006, we developed a system to prepare a commentary on more appropriate learning schemes. Based on the results of analyzing voluminous data with just data mining, we evaluated learning schemes for the users. With the data acquired through tests, we examined the system from the point of validity of the system. The result of this project is open to the public for actual commercial use and continues to be used.
Our present service relies on feedback from a single-point static data. In the future we will realize feedback based on analytical results of data compiled at multiple-points and create and offer dynamic contents that employ the specific data of the users.