This paper describes a method of enhancing a user's motivation to improve his/her skill level in a given field. Currently, the number of “expert” users, who record their knowledge and release it in the form of a life log in an attempt to achieve certain aims, is increasing. Users, who are affected by such experts and who also make efforts in achieving their own aims, are also increasing. We report on the development of a method of enhancing user's motivation to begin making self-active efforts. The method should automatically select experts who can inspire users and be a good reference for them and present the experts' life logs to the users. To achieve these functions, we propose a quantitative evaluation index between the life logs of experts and users. Through experiments we verify the possibility that the higher the proposed index of experts, the higher the rate of motivating users.

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We have developed a lunar surface browsing system with a tangible user interface by using augmented reality technology. It creates an interactive tabletop environment where learners can intuitively learn about lunar surface exploration from a printed map and multimedia projections. We performed two experiments to investigate the properties of the lunar surface browsing system. The first compared it with the moon as presented on Google Earth as a window, icon, mouse, and pointer (WIMP) environment. The results revealed that the lunar surface browsing system would be suitable as an interactive environment to assist children's learning. The second experiment was done in a practical situation at an exhibit in the Chiba Museum of Science and Industry. The results suggested that the browsing system would provide a learning environment where children and their parents could initiatively learn together.

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Despite many Augmented Reality (AR) applications and development environments in existence today, almost all require 3D modeling skills in content creation. The purpose of this work is the development of a Web application that enables users to create real-size AR content without 3D modeling skills or software. The developed experimental system allows for the creation of real-size AR content using captured images via a Web application. Twenty-two out of twenty-four individuals who tested the experimental system were able to successfully create AR content. The experimental system was concluded to enable users to create AR content without 3D modeling skills.

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A search system for VOD (video on demand) lectures is useful if it can be applied beyond searches of only text. To facilitate better searching for movie segments to be used in VOD lectures with Japanese subtitles, we propose a method using subtitles and a solving maximum likelihood estimation from a mixture of Gaussian distributions. The detection was performed by a statistical method by using the expectation-maximization algorithm. This allows for the detecting of movie segments and determining their number. In addition to improving evaluation of movie segments, we provide movie segment rankings. Movie segment rankings are computed for each movie segment using a method that removes one Gaussian distribution from a mixture of Gaussian distributions.

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It is difficult to generalize experiences of system development as methodologies for building meta-learning support systems. Thus, the importance of a model-based system development approach has been recognized. Such an approach contributes to systematic refinement of learning systems according to the model and moreover, knowledge can be accumulated on meta-learning system development based on it. In this paper, we adopt a model-oriented approach and undertake the following: (i) we adopt Kayashima's computational model as a basis to build a meta-learning process model and we extend it for meta-learning activities; (ii) we conceptualize five concepts that clarify the means to remove or eliminate the factors of difficulty; then (iii) we clarify design rationales of support functions embedded into our system to facilitate meta-learning processes based on the meta-learning process model.

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