Uma Abordagem de Arquitetura em Nuvem para Dados Educacionais em um Sistema Tutor Inteligente em contexto de Big Data

Abstract

The teaching and learning process in computer education systems has grown sharply in recent years and Intelligent Tutoring Systems (STI) have gained prominence by propo- sing the customization of environments to the student and thus improving the teaching and learning process. As a consequence of the increase in users of teaching platforms, large volumes of data have been generated, which have been research objectives to pro- pose performance improvement in data retrieval, storage and processing in the Big Data context. This work aims to propose a reference architecture approach in Big Data using cloud microservices for STI-LINA. For this, this approach focuses on mapping the pro- cesses of ingestion, storage, capture and enrichment of data and define adequate services for each of them. As a result of this work, it was possible to propose architectures based on microservices using AWS services, for all with models that make up the STI-LINA (Student, Domain and Pedagogical). In addition, it was possible to propose a new mo- dule/component that will provide analytical information to help the teacher follow the student’s trajectory in the teaching and learning process. Finally, a proposal was made to include the metric access method, called Slim-Tree-Lina, to optimize the recovery of learning objects offered to the student.