Towards the Construction of a Multi-agent Approach for Discovering the Meaning of Natural Language Collaborative Conversations

On the one hand, natural language is the main communication media for humans. It has a complex construction, based on the diversity of meaning for words and expressions according to the context. On the other hand, computers are not prepared to handle this ambiguity. The present work aims at presenting a multi-agent approach for dealing with the problem of discovering the meaning of expressions written in Spanish, based on a flexible recovery system and Bayesian principles. At a first stage, agents are supposed to identify the role of the words composing a sentence. At a second stage, a second set of agents is supposed to coordinate among them in order to assemble a meaning. Our research forms part and contributes to the analysis of collaborative conversations among participants in a web-based collaborative learning environment. © 2008 IEEE

An integrated approach for analysing and assessing the performance of virtual learning groups

Collaborative distance learning involves a variety of elements and factors that have to be considered and measured in order to analyse and assess group and individual performance more effectively and objectively. This paper presents an approach that integrates qualitative, social network analysis (SNA) and quantitative techniques for evaluating online collaborative learning interactions. Integration of various different data sources, tools and techniques provides a more complete and robust framework for group modelling and guarantees a more efficient evaluation of group effectiveness and individual competence. Our research relies on the analysis of a real, long-term, complex collaborative experience, which is initially evaluated in terms of principled criteria and a basic qualitative process. At the end of the experience, the coded student interactions are further analysed through the SNA technique to assess participatory aspects, identify the most effective groups and the most prominent actors. Finally, the approach is contrasted and completed through a statistical technique which sheds more light on the results obtained that far. The proposal draws a well-founded line toward the development of a principled framework for the monitoring and analysis of group interaction and group scaffolding which can be considered a major issue towards the actual application of the CSCL proposals to real classrooms.Peer ReviewedPostprint (author's final draft

A neural approach for modeling the inference of awareness in computer-supported collaboration

Individuals interacting in a computer supported collaborative learning (CSCL) environment produce a variety of information elements during their participation; these information elements usually have a complex structure and semantics, which make it rather difficult to find out the behavioral attitudes and profiles of the users involved. This work provides a model that can be used to discover awareness information lying underneath multi-user interaction. This information is initially captured in log files and then is represented in a specific form in events-databases. By using data mining techniques, it is possible to infer both the users' behavioral profiles and the relationships that occur in a CSCL environment. In this work we combine different data mining strategies and a neural-based approach in order to construct a multi-layer model that provides a mechanism for inferring different types of awareness information from group activity and presenting it to the interested parties. � Springer-Verlag Berlin Heidelberg 2006

A neural approach for modeling the inference of awareness in computer-supported collaboration

Individuals interacting in a computer supported collaborative learning (CSCL) environment produce a variety of information elements during their participation; these information elements usually have a complex structure and semantics, which make it rather difficult to find out the behavioral attitudes and profiles of the users involved. This work provides a model that can be used to discover awareness information lying underneath multi-user interaction. This information is initially captured in log files and then is represented in a specific form in events-databases. By using data mining techniques, it is possible to infer both the users' behavioral profiles and the relationships that occur in a CSCL environment. In this work we combine different data mining strategies and a neural-based approach in order to construct a multi-layer model that provides a mechanism for inferring different types of awareness information from group activity and presenting it to the interested parties. © Springer-Verlag Berlin Heidelberg 2006

Touristic coastal urban regions: Conceptualization and first results for the construction of an analysis model [Regiones urbanas turísticas costeras: Conceptualización y resultados preliminares para la construcción de un modelo de análisis]

On the one hand, natural language is the main communication media for humans. It has a complex construction, based on the diversity of meaning for words and expressions according to the context. On the other hand, computers are not prepared to handle this ambiguity. The present work aims at presenting a multi-agent approach for dealing with the problem of discovering the meaning of expressions written in Spanish, based on a flexible recovery system and Bayesian principles. At a first stage, agents are supposed to identify the role of the words composing a sentence. At a second stage, a second set of agents is supposed to coordinate among them in order to assemble a meaning. Our research forms part and contributes to the analysis of collaborative conversations among participants in a web-based collaborative learning environment. " 2008 IEEE.",,,,,,"10.1109/CISIS.2008.40",,,"http://hdl.handle.net/20.500.12104/45413","http://www.scopus.com/inward/record.url?eid=2-s2.0-53149095420&partnerID=40&md5=21c222766efc73df45b72ab74a2b1b2b",,,,,,,,"Proceedings - CISIS 2008: 2nd International Conference on Complex, Intelligent and Software Intensive Systems",,"47

A quantitative treatment to data from computer-supported collaboration: An ontological approach

Collaborative activity performed over specific platforms, designed for such purpose, can provide a deep knowledge about the roles, intentions and effects regarding participants and their interaction among themselves and with the knowledge objects available. This study aims at proposing a structured process for gathering the semantics of the activity hidden behind the raw data collected in log files from CSCL platforms. The proposal is based on exploring the semantic elements (activity awareness) through a social networks analysis (SNA). The main focus of our work is to match different behavioral profiles detected in the collaborative activity from CSCL with the formal profiles identified inside a complex concept-network. This network defines an ontology that describes the behavior expected when collaborating in different scenarios and types of activity. When a certain activity sequence matches with a predefined pattern, the concepts related to the pattern are then bound to the real activity sequence. © 2008 IEEE