부산대학교 도서관

Students of technological fields must practice so as to properly learn a particular field. There are different ways to practice: hands-on-lab in a real environment or a mockup, datasets (and tools for analyzing these datasets), or simulations. Each solution provides different advantages and disadvantages. For example, students might not prefer simulations since they do not always provide accurate real values (and when testing in a real laboratory results differ and the engagement might be higher), but they might be more affordable than real laboratories (depending on the field, there might not be any other affordable solution than a simulation). Datasets of recorded measurements are an equidistant point, where costs are lower and data is real, but no interaction is performed by the users with the reality. When creating remote laboratories, a system that enables students access the final equipment is usually used, but this might not be the best option. Sometimes, every potential input could be recorded and used in the future as a dataset to let users access this laboratory in a scalable way, and hybrid solutions could also be achieved. The focus of this contribution is to classify online laboratories from this perspective.