Making the invisible visible in constructionist learning tasks: an explanation framework based on a Pedagogical Virtual Machine (PVM)

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 s 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 understand the operation of these deep technologies. In working towards a solution to this challenge, this thesis combines computing and pedagogical models in a novel way to improve learning and teaching of computer science. This framework (a combination of computational and pedagogical models) is the core contribution of my thesis and has been given the name a Pedagogical Virtual Machine or PVM). It aims to extract learning-related information from the underlying computers that make up the education focus by providing a layered analysis of the technical and pedagogical processes that interact together for any given learning activity (in the context of learning about embedded computing). It adopts an object-oriented perspective that deconstructs computation and learning into objects, while taking inspiration from the Java Virtual Machine ideas, thereby building on existing paradigms of 'learning objects', 'object oriented programming' and virtual machines. In this way it addresses the challenge of linking both computing and learning activities in a standardised way across a multiplicity of computing and learning environments. The use of augmented reality (AR), and its ability to reveal deep technologies, further improves the effectiveness of the PVM framework introduced above by superimposing data, in real-time, concerning the invisible computational processes being explored by the learners. Applications that learners and developers might use this PVM tool for are typified by topics such as the Internet of Things, pervasive computing and robotics. The study presented in this thesis is based on the latter, robotics. The learning effectiveness of an AR based PVM approach was evaluated in two educational experiments that concerned students learning to program a desk-based robot (which was used as an example of an embedded-computer). The two experiments were conducted with computer-science students from the University of Essex and differed in the level of complexity. The results showed that PVM with AR significantly improved the students’ learning achievement and performance than those who used traditional learning environments. In addition, the PVM with AR made a positive difference to students learning experience, supporting the use of PVM with AR in educational activities that involved dealing with abstract technologies.