Marcio Alencar
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Papers by Marcio Alencar
a mobile framework to assist teachers in distance learning
courses. The tool has a MultiAgent System (MAS) in charge of
collecting and analyzing the feelings of texts posted by students
in the activities forums, chats, journal and messages sent to the
tutor. By means of framework it is possible to identify which
students are showing negative feelings, which is the feeling that
predominates in a class, to verify the history of the feelings of
a certain student. As a way to evaluate the project, teachers with
experience in tutoring used the framework with real course data
and then to verify the efficiency of the tool, answered
a questionnaire and classified postings of an VLE, as a result
we obtained the accuracy of 73,88%.
Resumo. Este artigo descreve a arquitetura e funcionamento do agente animado pedagógico 3D TUCUMÃ, integrado a um Ambiente Virtual de Aprendizagem, usando Sistema Multiagente, AIML, Computação Afetiva e Análise de Sentimentos. Uma avaliação parcial do projeto foi realizada com 5 turmas do curso de formação de tutores de uma escola de Educação a Distância com objetivo de analisar o sentimento dos textos publicados nos fóruns e chats do AVA. Os resultados são promissores, evidenciando a importância da abordagem.
educational institutions, it is necessary that coordinators and administrators to
effectively manage the online courses, in order to ensure good monitoring and
higher levels of quality. This paper shows a discussion based on a Multiagent
System to issue of the monitoring of the online courses. Thus, this user profile may
have a managerial, broad and automatic view of each course, plus get reports
organized by intelligent agents, and proactively should also realize LMS in specific
situations and send notifications to the manager immediately, allowing rapid
decision making.
a mobile framework to assist teachers in distance learning
courses. The tool has a MultiAgent System (MAS) in charge of
collecting and analyzing the feelings of texts posted by students
in the activities forums, chats, journal and messages sent to the
tutor. By means of framework it is possible to identify which
students are showing negative feelings, which is the feeling that
predominates in a class, to verify the history of the feelings of
a certain student. As a way to evaluate the project, teachers with
experience in tutoring used the framework with real course data
and then to verify the efficiency of the tool, answered
a questionnaire and classified postings of an VLE, as a result
we obtained the accuracy of 73,88%.
Resumo. Este artigo descreve a arquitetura e funcionamento do agente animado pedagógico 3D TUCUMÃ, integrado a um Ambiente Virtual de Aprendizagem, usando Sistema Multiagente, AIML, Computação Afetiva e Análise de Sentimentos. Uma avaliação parcial do projeto foi realizada com 5 turmas do curso de formação de tutores de uma escola de Educação a Distância com objetivo de analisar o sentimento dos textos publicados nos fóruns e chats do AVA. Os resultados são promissores, evidenciando a importância da abordagem.
educational institutions, it is necessary that coordinators and administrators to
effectively manage the online courses, in order to ensure good monitoring and
higher levels of quality. This paper shows a discussion based on a Multiagent
System to issue of the monitoring of the online courses. Thus, this user profile may
have a managerial, broad and automatic view of each course, plus get reports
organized by intelligent agents, and proactively should also realize LMS in specific
situations and send notifications to the manager immediately, allowing rapid
decision making.
a mobile framework to assist teachers in distance learning
courses. The tool has a MultiAgent System (MAS) in charge of
collecting and analyzing the feelings of texts posted by students
in the activities forums, chats, journal and messages sent to the
tutor. By means of framework it is possible to identify which
students are showing negative feelings, which is the feeling that
predominates in a class, to verify the history of the feelings of
a certain student. As a way to evaluate the project, teachers with
experience in tutoring used the framework with real course data
and then to verify the efficiency of the tool, answered
a questionnaire and classified postings of an VLE, as a result
we obtained the accuracy of 73,88%.