Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet-of-Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model-based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we call the Massive Open Online Laboratory.

Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet of Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model–based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we have called the Massive Open Online Laboratory.

This paper reports a remote laboratory architecture for Education 4.0 applied to Digital Logic Design courses using FPGA devices. The development of the proposed architecture is based on embedded systems, in which the V-model methodology and an Internet of Things (IoT) technological environment are applied. Nine laboratory exercises were designed for the proposed remote laboratory. To evaluate learners’ acceptance of the remote laboratory, the Technology Acceptance Model was used. An evaluation instrument comprising six variables and 21 pre-coded items on a Likert scale was designed and administered to 28 learners via a survey. The Cronbach’s alpha coefficients calculated for the six variables ranged from 0.72 to 0.91, suggesting adequate reliability of the evaluation instrument. The Wilcoxon signed-rank test was applied, yielding a p-value below the significance threshold of 0.001, which indicates statistical support for learners’ acceptance of the remote laboratory. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1173Dimensions.Open Alex.