2022-11-252022-11-252022-10-20BARROS, B. M. Aprendizado de máquina automático aplicado à predição da evasão no ensino superior. 2022. 100 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2022.http://repositorio.bc.ufg.br/tede/handle/tede/12454Academic dropout is a problem that affects many public and private university students in Brazil and around the world. Machine learning techniques have been used to mitigate the problem, but still require a lot of manual adjustments. We present in this work, a proposal of an automatic machine learning framework to predict academic dropout, with the goal of obtaining good results without the need for human intervention. This data processing framework includes the following stages: pre-processing, feature vector creation, data splitting into testing and training sets, clustering of data from different degrees for training, model selection, model parameter tunning and explainability. Additionally, we formalize temporal data splitting approaches for train and test datasets, as this task is not adequately addressed in most of the previous works.Attribution-NonCommercial-NoDerivatives 4.0 InternationalAprendizado de máquina automatizadoSegmentação temporal de dadosPredição da evasão acadêmicaMineração de dados educacionaisAutomated machine learningTemporal data splittingAcademic dropout predictionEducational data miningCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAODissertação