Um plugin do tipo report para a identificação do risco de evasão na educação superior a distância que usa técnicas de visualização de dados

• Ano de defesa: 2019
• Autor(a) principal: Brito, Maria Tatiane de Souza
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Evasão; Moodle; Learning Analytics; Visualização de dados; Indicadores; Dropout; Data Visualization; Indicators; Programa de computador - Módulo; Moodle - Plugin; Learning analytics; Ambiente virtual de aprendizagem - Análise de dados; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/15990

This work belongs to the field of educational research known as Learning Analytics and aims to provide a system that presents students in dropout risk of courses in the Virtual Learning Environment (VLE) Moodle. The system uses social, cognitive, and behavioral indicators, created on the basis of VLE data and displays them through a data visualization tool. VLEs generate reports and logs on student activities, however they are often difficult to understand for tutors, teachers and educational managers. Thus, they do not allow the identification of dropout problems in a more objective way. Therefore, it is believed that the use of a solution that collects data of indicators related to the accesses, interactions and grades of the students in a VLE and presents them through infographics, can help teachers, tutors and managers to identify students who may be in the process of leaving a distance course. A report plugin for Moodle VLE was designed and implemented, containing filteringfeatures,sendingnotificationsandinteractivegraphicsgeneratedbytheGoogleCharts tool. The focus group method was used to evaluate the plugin with teachers, tutors and managers of one higher education online course, followed by a qualitative analysis of the data collected. Itwasconcludedthatthepluginenhancestheteachers’awarenessofthestudents dropoutrisk,makingiteasierandmoreobjectivewhileallowingthegraphicalvisualization of students’ strategic cognitive, social and behavioral data. It was considered that the development of Learning Analytics tools needs to prioritize strategic data of students, such as access data, interactions and their grades in disciplines, since these can help professionals in the identification of the risk of students dropout.