Cognitive load considerations for Augmented Reality in network security training. (February 2022) - Herbert, Bradley; Wigley, Grant; Ens, Barrett; Billinghurst, Mark
Abstract: This paper presents an Augmented Reality (AR)-based network cabling tutoring system that trains users how to interconnect cables within a Virtual Local Area Network (VLAN) on a physical switching rack. AR arrows combined with text-based instruction and a checklist provided assistance during practical learning. When learners made a mistake, an Intelligent Tutoring System (ITS) identified the source of the mistake and provided real-time feedback using text, AR and text-to-speech mechanisms. The design was motivated by the human-cognitive architecture and its five evolutionary principles (proposed by Sweller and Sweller (2006)). Users performed four consecutive network cabling training tasks with assistance from our ITS. We found that users made fewer errors when the AR cues, text-based instruction and checklist solutions were replaced with summarised information and then removed completely in the final task compared to those who used an identical system with fixed instruction. Cognitive Load Theory (CLT) explains our results by suggesting that the instructional mechanisms become redundant as knowledge increases. Implications of the study are discussed as well as how AR can help facilitate knowledge transfer in the network cabling domain. Graphical abstract: Highlights: Several cognitive load considerations for AR-based network training are proposed. A novel AR training system combining fading with an ITS is proposed. Our AR training system reduces mistakes during network security training. In network security training, AR should be faded out as expertise increases.