In the field of Computer based Education, personal computers were first used to support education for individual learners in the 1980s, WWW technologies were first used to support e-learners in the 1990s, and mobile devices and wireless technologies were first used to support education for mobile learners in the 2000s. The changes in the way that learners carry out their studies (e.g. e-learning or mobile learning) were a result of the development of such computer technologies. As with every age, there is a corresponding emergence of the educational applications and theories, which accompany such computer technological advancements.

Our research is about using computer tecnologies to support both teaching and learning. Especially, our current professional interest focuses on e-learning, mobile learning, educational data mining, and learning analysis.

We are conducting researches as follows:

Mobile Learning(from 2003): JAPELAS: JAPELAS is a personal digital assistant (PDA)-based language-learning support system for Japanese polite expressions learning. This system helps foreigners to learn Japanese polite expressions by detecting users' social relationships etc.

SNS-based Learning Environment(from 2008): SONKULE: SONKULE is a using SNS to support knowledge awareness in a ubiquitous cooperative learning system, which its characteristics to let users help each other by using SNS.(Grant-in-Aid for Young Scientists B:21700816)

Research Trend Survey Learning Environment(from 2011): Research trend survey is an essential preliminary step for any academic researches, but many beginning researchers have difficulty because they are still foreign to appropriate keywords in his/her research field. We constructed a support system for research trend surveys not only to accelerate the preliminary step but also to make students have a better grips of trend progresses and keyword transitions.Grant-in-Aid for Young Scientists B:25750084)

Educational Big Data ( From 2014): This study is intended to identify meaningful measures from e-book materials used and to employ these measures for analyzing learning behavioral patterns. In an evaluation, students were grouped into four clusters using k-means clustering, and their learning behavioral patterns were analyzed. We examined whether the learning behavioral patterns have relations with the learning outcomes. The results show that the learning behavior of “backtrack” style reading has a significant positive influence on learning effectiveness, which can help students to more efficiently learn.(NICT Grant No.178A03, Grant-in-Aid for Scientific Research B: 16H03078)