Análise visual para monitoramento de alunos de cursos à distância

• Ano de defesa: 2016
• Autor(a) principal: Weiand, Augusto
• Orientador(a): Manssour, Isabel Harb
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
• Departamento: Faculdade de Informática
• País: Brasil
• Palavras-chave em Português: EDUCAÇÃO A DISTÂNCIA; AMBIENTES VIRTUAIS DE APRENDIZAGEM; MINERAÇÃO DE DADOS (INFORMÁTICA); INFORMÁTICA
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/7111

With the technology advancement, distance education has been very discussed in recent years, especially with the emergence of several kinds of Virtual Learning Environments (VLE’s). These environments used in distance education courses, usually generate a lot of data due to the high number of students and the various tasks which involve their interactions. Thus, arises the need to search efficient and intelligent ways to find relevant information. Data mining techniques help in the discovery of implicit knowledge that can support decision making. However, eventually appear difficulties in understanding the obtained results of the mining due to the analyzed volume. In these cases, the use of visualization and interaction techniques assists in this task. The main goal of this work is to present the development of a visual analysis approach that uses data mining algorithms and visualization techniques to help monitoring students of distance learning courses in the institutions that use virtual learning environments. These students are classified considering their performance, providing ways to investigate and predict possible approvals, disapprovals and evasions. The visualizations aim to improve the understanding of the generated data by the mining algorithms, providing different ways of interaction. It is possible to analyze both the general behavior of students in a selected course, as their individual behaviors. Performance comparisons of a student between different courses, and from interactions performed in a set of courses are also allowed. Initial tests demonstrated that it was possible to make predictions in a satisfactory way, as well as enable visualizations and interactions to the users for interpreting the information resulting from mining algorithms.