01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
Navegar
2 resultados
Resultados da Pesquisa
Item Utilização de Game Learning Analytics para verificação do aprendizado em jogo sério voltado ao ensino de zoologia(2019) Farias, Laura Lobo de; Cysneiros Filho, Gilberto Amado de Azevedo; http://lattes.cnpq.br/0534822491953359; http://lattes.cnpq.br/5281211059562823Digital games are tools capable of aiding in learning, games developed exclusively for this purpose are called Serious Games. Serious Games are used in several areas of learning including in the field of Zoology, which aims to train people who know the characteristics of animals and relate ethically with them. However, Serious Games face some problems, one of these difficulties is to understand how players interact with the game and how the learning process takes place. With Game Learning Analytics it is possible to collect and analyze data from the Serious Games with the aim of improving their practical applicability and thus to develop more effective educational games. In this project, we developed a Serious Game capable of assisting learning in Zoology and its effectiveness will be analyzed through the use of Game Learning Analytics.Item Uma abordagem de Game Learning Analytics para identificação de habilidades de leitura e escrita no ensino infantil(2018) Oliveira Neto, José Rodrigues de; Rodrigues, Rodrigo Lins; Amorim, Américo Nobre Gonçalves Ferreira; http://lattes.cnpq.br/7962263612352589; http://lattes.cnpq.br/5512849006877767; http://lattes.cnpq.br/3879751025550218The power that video games have to capture their players’ attention has brought with it the idea of using them with the main objective of reinforcing learning in educational context. Recent studies demonstrate that it is possible to analyze the interactions of players in such games, called Serious Games, to conclude and measure the learning obtained during interaction in those games. Given this context, this work aims to develop an analysis of data obtained from the interaction of players in one game, out of 20, applied during a research that proved their positive impact on the development of reading and writing skills of 4-years-old children. Three classifiers were selected: Naive Bayes, Support Vector Machines (SVM) and Logistic Regression, which were trained with the data resulting from the interaction of these players with the game and demonstrated the hit rate of each of the classifiers. In addition, this work also makes an analysis of the interactions considered more relevant by one of the models, finding relationships between the words proposed as challenge in the test and those present in the game, raising reflections that can be taken into account during the development of a educational game that aims to improve children’s reading and writing skills in early childhood education.