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 created 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.

本文介绍了一种基于增强现实 (AR) 的网络布线辅导系统，该系统培训用户如何在物理交换机架上的虚拟局域网 (VLAN) 内互连电缆。AR 箭头结合基于文本的指导和清单在实践学习中提供了帮助。当学习者犯错时，智能辅导系统 (ITS) 会识别错误的根源，并使用文本、AR 和文本转语音机制提供实时反馈。该设计的灵感来自于人类认知架构及其由 Sweller 和 Sweller (2006) 创建的五个进化原则。在我们 ITS 的帮助下，用户连续执行了四次网络布线培训任务。我们发现用户在 AR 提示时犯的错误更少，与使用具有固定指令的相同系统的人相比，基于文本的指令和清单解决方案被汇总信息取代，然后在最终任务中被完全删除。认知负荷理论 (CLT) 通过表明随着知识的增加教学机制变得多余来解释我们的结果。讨论了该研究的意义以及 AR 如何帮助促进网络布线领域的知识转移。