# Computer Architecture for Industrial Training Evaluation
## 2 Literature Review
The research by [PERSON] et al. [30], focusing on the context of industrial assembly tasks, in which experts remotely guide local operators with VR and AR instructions, contrasted a \"typical MR remote collaboration interface [...] (RECI)\" (p. 1) with another of \"adaptive generation of instructions via context awareness (AGICA)\" (p. 1). The use of AGICA, proposed by the researchers, evidenced an increase in the efficiency rates in collaborative work, thanks to the improvements introduced in the experiences experienced by users. For these validations, they used surveys, interviews, video recordings of the processes, and questionnaires. The latter \"quantitatively evaluates the task workload\" (p. 15) using the NASA Task Load Index (NASA-TLX) tool and also offers an evaluation of the usability and the perception of the users of the total system who used the \"System Usability Scale (SUS)\" (p. 17). [PERSON], [PERSON] et al. [31], focused on investigating the impact that AR could have had \"in terms of upgraded workplace understanding, training, and assisting semi-skilled/new workers in an industrial production environment\" (p. 1). The research \"involves booster pump assembly of reverse osmosis (RO) system\" (p. 9) in a sample of 24 engineering students of different levels of education, who were randomly grouped into three groups. These groups were exposed to three modes of displaying the instructions: instructional documents, Virtual/Desktop Mode, and AR Mapping. The research showed that \"the AR-based object mapping approach provides better outcomes than other visualization instruction modes in the assembly environment\" (p. 15). The instruments to evaluate the task load implemented surveys and questionnaires from NASA TLX ([PERSON], [PERSON] et al. [31]); the tool was also used in the research of [PERSON] et al. [30]. In area (4), AR applications for industrial training, studies show that the use of AR applications can diminish the number of errors in task performance [13; 16; 17]. AR can maximize worker efficiency and mobility [32]. These statements are obtained from the reviews of the works of [PERSON] et al. [30] and [PERSON] et al. [31], considering that AR evidenced improvements in usability and reduction of errors in the execution its tasks. However, to demonstrate these benefits, it is necessary to carry out workers training processes. The training of workers with AR technology could: (a) reduce costs and execution times of tasks, (b) increase safety levels in the company, reducing risks and preventing damage to equipment and workers [33; 34] and (c) allow adapting to different learning rates and experiences [32]. [PERSON], [PERSON] et al. [35] focus on the importance of learning in industrial work contexts, where AR plays a key role, unlike traditional training. In tests based on the design of a \"motion capture architecture\" (p. 36), benefits were evidenced in the implementation of AR in training. These benefits were associated with lower cognitive or mental loads, decreased effort and time in the learning processes, and \"a lower tendency to perform errors, resulting in an increment in both productivity and efficiency\" ([35], p. 39). The works discussed in this section are a representative sample that shows the research interest in studying the metrics that affect training processes at an industrial level to improve production processes, assembly, monitoring, and other lines of work that are feasible in these environments. These applied cases demonstrate that AR is not an isolated technology but works complementary with other technologies such as IoT, artificial intelligence (AI), and smart tutors, among others. However, immersive and semi-immersive experiences in industrial environments could improve performance, the worker's experience, and the ability to learn the assigned task more quickly and accurately. For this reason, continuing to explore how to improve this type of training is part of a line of work still in force.