TY - JOUR
T1 - A Methodology for Training Toolkits Implementation in Smart Labs
AU - Zamiri, Majid
AU - Sarraipa, João
AU - Ferreira, José
AU - Lopes, Carlos
AU - Soffer, Tal
AU - Jardim-Gonçalves, Ricardo
N1 - Funding Information:
This study was supported by the Center of Technology and Systems (CTS-UNINOVA).
Funding Information:
European Commission ERASMUS + through grant number ° 2021-1-RO01-KA220-HED-000032176 LLSF.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/2/27
Y1 - 2023/2/27
N2 - Globally, educational institutes are trying to adapt modernized and effective approaches and tools to their education systems to improve the quality of their performance and achievements. However, identifying, designing, and/or developing promising mechanisms and tools that can impact class activities and the development of students’ outputs are critical success factors. Given that, the contribution of this work is to propose a methodology that can guide and usher educational institutes step by step through the implementation of a personalized package of training Toolkits in Smart Labs. In this study, the package of Toolkits refers to a set of needed tools, resources, and materials that, with integration into a Smart Lab can, on the one hand, empower teachers and instructors in designing and developing personalized training disciplines and module courses and, on the other hand, may support students (in different ways) in developing their skills. To demonstrate the applicability and usefulness of the proposed methodology, a model was first developed, representing the potential Toolkits for training and skill development. The model was then tested by instantiating a particular box that integrates some hardware to be able to connect sensors to actuators, with an eye toward implementing this system mainly in the health domain. In a real scenario, the box was used in an engineering program and its associated Smart Lab to develop students’ skills and capabilities in the areas of the Internet of Things (IoT) and Artificial Intelligence (AI). The main outcome of this work is a methodology supported by a model able to represent Smart Lab assets in order to facilitate training programs through training Toolkits.
AB - Globally, educational institutes are trying to adapt modernized and effective approaches and tools to their education systems to improve the quality of their performance and achievements. However, identifying, designing, and/or developing promising mechanisms and tools that can impact class activities and the development of students’ outputs are critical success factors. Given that, the contribution of this work is to propose a methodology that can guide and usher educational institutes step by step through the implementation of a personalized package of training Toolkits in Smart Labs. In this study, the package of Toolkits refers to a set of needed tools, resources, and materials that, with integration into a Smart Lab can, on the one hand, empower teachers and instructors in designing and developing personalized training disciplines and module courses and, on the other hand, may support students (in different ways) in developing their skills. To demonstrate the applicability and usefulness of the proposed methodology, a model was first developed, representing the potential Toolkits for training and skill development. The model was then tested by instantiating a particular box that integrates some hardware to be able to connect sensors to actuators, with an eye toward implementing this system mainly in the health domain. In a real scenario, the box was used in an engineering program and its associated Smart Lab to develop students’ skills and capabilities in the areas of the Internet of Things (IoT) and Artificial Intelligence (AI). The main outcome of this work is a methodology supported by a model able to represent Smart Lab assets in order to facilitate training programs through training Toolkits.
KW - skills development
KW - smart labs
KW - toolkits
KW - training
UR - http://www.scopus.com/inward/record.url?scp=85149825638&partnerID=8YFLogxK
U2 - 10.3390/s23052626
DO - 10.3390/s23052626
M3 - Article
C2 - 36904827
AN - SCOPUS:85149825638
SN - 1424-8220
VL - 23
JO - Sensors
JF - Sensors
IS - 5
M1 - 2626
ER -