Mostrar el registro sencillo del ítem

dc.contributor.advisorBarbosa, Jorge Luis Victoria
dc.contributor.authorFerreira, João Luiz Cavalcante
dc.date.accessioned2016-04-13T15:28:01Z
dc.date.accessioned2022-09-22T19:19:38Z
dc.date.available2016-04-13T15:28:01Z
dc.date.available2022-09-22T19:19:38Z
dc.date.issued2016-02-25
dc.identifier.urihttps://hdl.handle.net/20.500.12032/59574
dc.description.abstractE-learning in Brazil has been established with many students opting for this type of education to expand their training and professional achievement, however it faces some obstacles, such as resistance from students and educators, organizational challenges, production costs and the question of failure or retention of students. One of the main advantages of e-learning courses is the large amount of data generated by the interactions in the educational environment, which opens up new possibilities to study and understand these interactions. Educational Data Mining (EDM) is an area of interdisciplinary research that deals with the development of methods to explore data that originates in the educational context. Learning Analytics (LA) is another area of emerging research. It seeks to measure, collect, analyze and report data on students. The challenge for researchers is to develop methods to predict the performance of students in order to allow the intervention of teachers and tutors aiming to retrieve the student before failing. This thesis proposes the MD-PREAD, a model for predicting failure of risk groups in a e-learning environment. The decision tree technique was used to enable a difference as to whether the interpretation of the data generated by the use of prediction methods, since other methods such as Artificial Neural Networks that has as disability difficulty in identifying precisely the causes that lead to predictions results. The model was prototyped in RapidMiner mining tool. An experiment was conducted at the Federal Institute of Education, Science and Technology of Amazonas, the Open University of Brazil program in course Philosophy of education. Historical data collection of 10 disciplines from a group of 30 apprentices were made in two consecutive semesters, 2014/2 and 2015/1, the total number of enrolled students was 125, the total raised interactions were 41070, the prediction calculation considered average of 30 apprentices ratings, the standard deviations of the interactions and their situations. These data served to compose the training set required for classification rule defining which had as predominant accuracy of 55% and Kappa reliability 0.22. A second validation process was carried out after the experiment. It was considered the total amount of 125 apprentices and the best classifier found was the J48 with the accuracy of 84.05%, 77.08% of classification precision and recall of 50.23%. It was concluded that the MD-PREAD is a support tool in the prognosis of failure risk groups, since it enabled the generation and weekly availability of these groups to a recommendation system.en
dc.description.sponsorshipNenhumapt_BR
dc.languagept_BRpt_BR
dc.publisherUniversidade do Vale do Rio dos Sinospt_BR
dc.rightsopenAccesspt_BR
dc.subjectEaDpt_BR
dc.subjectLearning analyticsen
dc.titleMd-pread: um modelo para predição de reprovação de aprendizes na educação a distância usando árvore de decisãopt_BR
dc.typeDissertaçãopt_BR


Ficheros en el ítem

FicherosTamañoFormatoVer
João Luiz Cavalcante Ferreira_.pdf1.672Mbapplication/pdfVer/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem


© AUSJAL 2022

Asociación de Universidades Confiadas a la Compañía de Jesús en América Latina, AUSJAL
Av. Santa Teresa de Jesús Edif. Cerpe, Piso 2, Oficina AUSJAL Urb.
La Castellana, Chacao (1060) Caracas - Venezuela
Tel/Fax (+58-212)-266-13-41 /(+58-212)-266-85-62

Nuestras redes sociales

facebook Facebook

twitter Twitter

youtube Youtube

Asociaciones Jesuitas en el mundo
Ausjal en el mundo AJCU AUSJAL JESAM JCEP JCS JCAP