Proposta de um recomendador de fluxograma personalizado com visualização gráfica das dificuldades das disciplinas baseado em filtragem colaborativa

• Ano de defesa: 2020
• Autor(a) principal: Santos, Igor Nóbrega dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Sistemas de recomendação; Sistemas de e-learning; Filtragem colaborativa; Previsão de notas; Recommendation systems; E-learning systems; Collaborative filtering; Grade prediction; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/18613

The flowcharts are built based on the (NDE) which responsible for the conception of the pedagogical project of a course and may vary depending on the recommendations of NDE itself that uses criteria such as: the minimum amount of credits per period, the duration. of course, the dependencies between disciplines. The flowchart construction stage is of importance, as poorly formulated flowcharts may contain poorly distributed subjects, increasing the time of completion of the course, leading to higher financial expenses for universities. Students have uncertainties in choosing an ideal set of subjects over a period, as they do not know a priori the difficulties they will face in each of them. Given this, the need arises for the development of custom flowchart recommendation systems. The present work proposes the development of a personalized flowchart recommendation based on a recommendation algorithm by the collaborative filtering technique with the use of graphical flowchart visualization. Colors (red, yellow and green) will be used to alert the student about the difficulty level of each discipline. In this way, one can make an association between the predictions of grades and the levels of difficulties for each of them. The following parameters were used: Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the confusion matrix analysis to test the efficiency of the recommender. The flowchart recommender was tested with a set of 298 active students from the Computer Engineering course at UFPB, making subject recommendations for all periods of the course, resulting in an RMSE grade prediction of 1.74 and the MAE of 1.33. In addition, 88.71 % accuracy, 98.52 % specificity and 55 % sensitivity were obtained. As possible results of this proposal, it is expected to reduce the risk of failure in the subjects, since the student will have, in advance, flowchart information with easy and intuitive visualization about the difficulties of the subjects through a colored flowchart, so plan your dedication better to each of them.