Context-oriented data acquisition and integration platform for social learning

Yu Ren Chen, Yeong Sheng Chen, Timothy K. Shih

研究成果: 雜誌貢獻期刊論文同行評審


As the popularity of social media and mobile devices increases quickly, lots of teachers and students use social media and mobile devices to share learning information in the social network. This information may contain some ambient information (e.g., location, time and social relationship) that could be used to construct social knowledge for social learning. In the pursuit of such environments for social knowledge construction, it is a very important issue to transform the ambient information into context data so that the data can be exchanged in the social network. In this paper, a data acquisition and integration platform for social learning is proposed. The platform is developed under a cloud computing environment using context-oriented approaches. It collects sensor data from different types of sensor devices, including such as RPID, ZigBee sensors, GPS devices, temperature sensors, humidity sensors, luminance sensors, etc. First, we are devoted to the study of deployment, management, and control of different types of sensors for automatic acquisition of sensor data and its related ambient information, both of which will be stored in the IoT repository in a cloud environment. Then, with the devised context broker, the data retrieved from the IoT repository can be used to produce the contextual portfolio, which is annotated with semantic descriptions. The contextual portfolio is stored into a cloud database as the User Portfolio. Finally, services for accessing the User Portfolio in the cloud are developed on a middleware platform, compliant with the OSGi standard, such as Knopflerfish. With the proposed platform, the acquired data is integrated into semantic contexts, which can be easily shared and reused among different mobile learning applications. Also, the context information can enhance mobile learning applications' usability by adapting to the conditions that directly affect their operations for constructing social knowledge in the social learning evironment.

頁(從 - 到)73-83
期刊International Journal of Electrical Engineering
出版狀態已出版 - 4月 2013


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