Abstract
In today's digital world, the use of diverse interconnected physical computer-based devices, typified by the Internet-of-Things, has increased, leaving their internal functionalities hidden from people. In education, these hidden computational processes leave learners with a vagueness that obscures how these physical devices function and communicate in order to produce the high-level behaviours and actions they observe. The current approach to revealing these hidden worlds involves the use of debugging tools, visualisation, simulation, or augmented-reality views. Even when such advanced technologies are utilised, they fail to construct a meaningful view of the hidden worlds that relate to the learning context, leaving learners with formidable challenges to understanding the operation of these deep technologies. Therefore, a pedagogical virtual machine (PVM) model was employed to evaluate the learning effectiveness of the proposed model. We presented the experimental evaluation of the PVM model with AR that concerned students learning to program a desk-based robot (which is used as an example of an embedded computer) and reveal the learning effectiveness of using PVM with AR compared to traditional engineering laboratory methods. Overall, the PVM with AR improved learning and teaching, as compared to traditional environments, and learners preferred the use of the PVM with AR system for doing similar activities.