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The analysis of group dynamics is extremely useful for understanding and predicting the performance of teamwork's, since in this context, collaboration problems can naturally arise. Artificial intelligence, and specially machine learning techniques, enables automating the observation process and the analysis of groups of users who use an online collaborative platform. Among the online collaborative platforms available, games are an attractive alternative for all audiences that enable capturing the players' behavior by observing their social interactions, while engaging them in a pleasant activity. In this paper, we present experimental results of classifying observed conversations in an online game to collaborative behaviors, guided by the Interaction Process Analysis, a theory for categorizing social interactions. The proposed automation of the classification process can be used to assist teachers or team leaders to detect alterations in the balance of group reactions and to improve their performance by indicating actions to improve the balance. [ABSTRACT FROM AUTHOR]

Copyright of Enl@ce: Revista Venezolana de Información, Tecnología y Conocimiento is the property of Universidad del Zulia, Laboratorio de Investigacion y Sistemas de Informacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Enl@ce: Revista Venezolana de Información, Tecnología y Conocimiento is the property of Universidad del Zulia, Laboratorio de Investigacion y Sistemas de Informacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital libraries are one of the content managers that centers and institutions use more frequently nowadays. The data volume they can store, the diversity of formats that can be used, the many domains of knowledge that are present in their contents, and the complexity of human teams that create and maintain them result in great management difficulties, mostly if the idea is to provide users with a customized service. This paper proposes an architecture to manage the user model in a digital library. Said library is focused on integrating emerging technologies based on semantics and diverse techniques for capturing user preferences, in order to facilitate generation of the user model that interacts with a digital library, to semantically annotate the digital objects a digital library contains, and to manage contents taking into account their meaning according to the semantic preferences of users. [ABSTRACT FROM AUTHOR]