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Gustavo R. Alves

Researcher at Instituto Superior de Engenharia do Porto

Publications -  225
Citations -  2197

Gustavo R. Alves is an academic researcher from Instituto Superior de Engenharia do Porto. The author has contributed to research in topics: Remote laboratory & Engineering education. The author has an hindex of 21, co-authored 220 publications receiving 2035 citations. Previous affiliations of Gustavo R. Alves include Universidade Federal de Santa Catarina & International Student Exchange Programs.

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Addressing Software Impact in the Design of Remote Laboratories

TL;DR: The main characteristics of a Remote Laboratory are presented, the software technologies to implement the client and server sides in a WebLab are analyzed, and a Service Oriented Laboratory Architecture-based approach is suggested for the design of future Remote Laboratories.

Using remote labs in education: two little ducks in remote experimentation

TL;DR: The Second Best to Being There (S2BE) as discussed by the authors is a book written by pioneers in remote experimentation in 1993 and describes that a student/teacher can access a real experiment through Internet as being in the real lab.
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Informal learning recognition through a cloud ecosystem

TL;DR: The TRAILER project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out, by providing a technological framework using cloud services, a workflow, and a methodology.
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An Integrated Reusable Remote Laboratory to Complement Electronics Teaching

TL;DR: In this paper, an alternative remote lab infrastructure devoted to the study of electronics is presented and may be combined with other web experiments and e-learning strategies, while safeguarding security access issues.
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How Remote Labs Impact on Course Outcomes: Various Practices Using VISIR

TL;DR: The results of integrating the open remote laboratory VISIR into several courses, in various contexts and using various methodologies are presented, showing that this benefit can be maximized under particular conditions and characteristics.