A major goal of this study is to provide a practical Web application for English jumbled-word questions which records and reproduces learners'mouse trajectories. The authors believe that trajectories include useful clues to the solving process or hesitation when the problems are being solved. In this paper, we will discuss ways in which to analyze the recorded trajectories such as numbers of u-turns and drag&drops, in relation to the scores and newly incorporated ‘degree of confidence’ parameter. The results of our empirical findings are also shown. Our hope is to retrieve what learners will study next in accordance with their needs. It will also be applied as a tool to help on-site teachers work on their teaching strategies.

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This paper shows a method to identify a learner's action simply by a few data from a stereo vision camera system. It is assumed that the learner's head and both hands move characteristically while (s)he is learning in a seated position. In this method, first, the motions of the three body parts of the learner are measured. Then the behavior is identified by comparing the frequency characteristic of the motion data with that of pre-defined behaviors. We examined this method on nine typical learning actions such as “horizontal writing”, “document creation with word-processing software” and so on. As a result, it has been proved that the method is effective to identify learner's action on instrumental grounds.

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Although extensive reading is becoming more commonplace in the EFL/ESL classroom, preparing a large number of books, time measuring devices, log sheets, and calculators in a regular extensive reading class requires a large investment of time and money. In order to resolve these problems, PREMAβ (beta) was developed as software that allows users to utilize text from the Internet for extensive and speed reading practice, and automatically records user data so that learners and instructors can easily track progress. This software was further developed into PREMA, a browser-type application that displays and records the difficulty level of vocabulary, word count, and reading speed in words per minute by simply highlighting and clicking on target text on a homepage. In this paper, the philosophy of development is explained, features of both PREMAs are overviewed, the result of the “100,000-word extensive reading and speed reading challenge” is shown, and fufure targets are discussed.

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The goal of our investigation is to construct an educational system, supporting lessons' evaluation by student initiatives. In this type, the question items created by many students also have similarities, therefore it is necessary to standardize these items by a certain method. However, if teachers depend only on manual operation, adjustment of an item will take huge cost. Then, we have prototyped cloud application based on LAMP (Linux, Apache, MySQL, and PHP) and R language. The application is characterized in calling the R Commands through PHP scripts. As a result of performance test, we have a few issues around the retrieval accuracy mainly based on the query expansion and/or latent semantic analysis, with no problem in an actual use however. Some techniques can also be applied to the development of the modules in LMS such as Moodle in the future.

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This paper shows typical functions required on electronic textbooks to be adopted in various countries, trial implementation of these functions, and their verification of representation and behavior in six e-book platforms. First, the authors extracted the functions by checking preceding researches and investigations. The result contains 52 items, including 7 categories of “authentication”, “copyright”, “contents representation”, “related information”, “additional information by learners”, “learning support”, and “restriction of contents and platforms”. Next, these functions were implemented with ePub3 format e-books. ePub3 is going to be one of de jure standard format of e-books. Also, contents representation and behavior of the result ePub e-books were verified on 6 types of e-book platforms on operating systems of iOS, Android, Windows, and Kindle Linux. The verification result shows that iBooks3 on iOS worked best among the 6 e-book readers. Also it shows that many e-book readers support only representation of ePub2 files, while HTML5, JavaScript, and Web API are not supported yet.

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