O objetivo do projeto é buscar a integração prática e conceitual entre a demanda por virtualização do processo de ensino e os recursos da nuvem. Os resultados esperados são novos métodos, extensões dos métodos existentes, e novos métodos para personalizar o processo de ensino universitário. Nossa tese é que um processo personalizado e assistido por computador pode significar um grande avanço para o ensino universitário, aumentando a precisão da avaliação, recuperando a falta de conteúdo assumido como pré-requisito e aumentando a motivação dos estudantes. Além do aumento da eficiência no ensino é possível ainda manter um bom custo/benefício. Um estudo de caso está sendo estudado usando containers e conteúdo adaptável, mapeado em "knowledge graphs", para uma disciplina de Inteligência Artificial ministrada para alunos de Engenharia. 

Pesquisador Responsável

Jose Reinaldo Silva

reinaldo@usp.br

lattes: http://lattes.cnpq.br/9317869378701106

Bachelor in Physics in UFBA, MSc in Physics in UFPE, MA in Computer Science, Mills College, USA, PhD in Computer Engineering, USP, pos-doc in Computer Science and in Systems Design Engineering, Univ. of Waterloo, Ca. Associate Professor of the Mechatronics and Mechanical Science Dept. of Escola Politécnica, USP. Research interests are in Engineering Design, Requirements Engineering, Service Design Management and Engineering, Artificial Intelligence, Planning & Scheduling, Formal methods methods applied to IIoT and Industry 4.0.

Colaboradores

Elinilson Vital

vital@usp.br

lattes: http://lattes.cnpq.br/7197861299928406

Master's student in Mechatronics Engineering at the Polytechnic School of the University of São Paulo, focusing on the theme Cloud Manufacturing Services (CMfgS): Modeling of automated production systems in the cloud. Graduated in Applied Mathematics in Mechanical Control and Automation from the Institute of Mathematics and Statistics of the same university, focusing on formal requirements analysis, service science, distributed systems, knowledge bases, and data spaces. Demonstrates ability to apply mathematical and computational knowledge to solve complex problems and promote technological innovations. Practical experience includes data science, data grouping and their applications, as well as in modeling and analysis of automation systems, also in programming and simulation, including algorithms and data structures, concurrent and parallel programming, and numerical methods for differential equations. The main research interests are currently focused on deepening knowledge in Service Science and Data Spaces, aiming to develop innovative solutions that optimize business processes and increase technological efficiency in distributed environments, uniting mathematics, computing, and engineering.

Arquivos