Abstract. The data generated by the use of systems such as Virtual Learning Environments contain information that can be used to support teachers in decision making, which may be required even before obtaining historical data. The learner Learning Style can complement this data covering the gap through the intersection with the historical behavior patterns. Thus, this work aims to propose an evaluation of the potential use of Learning Style in the early learner behavior diagnosis in the distance learning to support decision making. The results, of the experiment focused on the dropout behavior, suggest that the learning style can be used in the learner behavior prediction.Resumo. Os dados gerados pela utilização de sistemas como Ambientes Virtuais de Aprendizagem contêm informações que podem ser utilizadas para auxiliar os professores na tomada de decisão, que em alguns casos, pode ser requerida antes mesmo de se obter dados históricos. O Estilo de Aprendizagem pode complementar esses dados através do cruzamento com os padrões de comportamento histórico. Neste sentido, este trabalho propõe um modelo de utilização do Estilo de Aprendizagem no diagnóstico antecipado do comportamento de aprendizes na modalidade de Educação a Distância para o suporteà tomada de decisão. Os resultados alcançados em um experimento que focou na evasão sugerem que o Estilo de Aprendizagem pode ser usado para antecipação desse comportamento.
This paper explores the factors explaining the adoption of e-government platforms by Portuguese citizens. Since it is still a challenge the adoption of e-government, e-participation and the e-citizenship in Portuguese society, one of the goals of this study is to create a conceptual model based on previous research to measure what are the factors that can lead to the intention of using an e-government platform. Such factors are trust, privacy, social influence, security, relative advantage, web design and perceived ease of use. Hypotheses were created based on these factors, so that future research can be applied and evaluated, and thus prove these assumptions.
Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.