Abstract
This chapter addresses methods and techniques explored in the development of a virtual environment solution for the training of electricians in critical activities, focusing on research topics, such as user experience and learning evaluation. The main concept of the virtual environment is the mapping between virtual reality technology and gamification methods with learning theories. User experience (UX) is used to assess how the system is perceived by the user. Non-player characters are modelled to assist the trainee in the learning process, focusing on decision making in adverse conditions. An automatic feedback system based on the visualization of error patterns highlights both the mistakes made in the virtual experience and the strategy for solving the proposed problem. A trainee classification process is proposed based on the analysis of human error patterns during the execution of a task. Clustering techniques applied to error patterns allow for the identification of prototypes of performance classes and their visualization in the form of distinct groups.
TopIntroduction
Virtual reality, along with gamification, has played an important role in alternative learning in recent years, especially in the context of critical activities. Activities of this type require a rigorous management process and, due to the danger involved, demand sensitive decisions, especially those that involve risk to people's lives. Similarly, professionals who actually perform critical activities constantly face very complex situations and constantly optimize the decision-making process, otherwise serious impairment can occur. Therefore, recent technological and methodological advances in the area of virtual training have had a great impact on the decision-making process in different professional contexts.
This chapter presents concepts, methods, and tools, resulting from scientific research, related to the learning and training of power substation maintenance activities, and the corresponding validation through tests in the virtual environment developed along a series of R&D projects.
The general benefits of a complementary virtual process include:
- 1.
safety, since there are no risks and, more precisely, there is no health and life related threat in a virtual environment, as opposed to the traditional maintenance activity;
- 2.
psychological effects, since trainees know there are no risks involved and they can concentrate solely in the learning process;
- 3.
logistics, because the actual training process requires special arrangements, favourable weather conditions, the involvement of a whole team of professionals and displacement of people and equipment, whereas the virtual process can be used at any time and can be transported easily;
- 4.
the innovation of effective learning through the mediation of the virtual reality technology involved.
The most important benefits, however, are related to the improvement of the training process, addressing the following issues:
- 1.
the fact that the traditional training can only be performed sporadically, at specific occasions, due to the constraints implied in the process, which makes it very difficult for the trainees to recall learning, and thus reducing the effectiveness of the training process;
- 2.
the use of methods, concepts, and cognitive aspects with great potential to improve the learning process;
- 3.
risky activities require close attention as they are by nature very dangerous. However, experienced professionals tend to rely on their muscle memory, when they have already mastered the activity to be performed. This may lead to accidents due to minor mistakes. In a virtual environment, error-inducing mechanisms can be used to bring the issue from the muscle memory back to the cognitive system, leading the professional to resume mental processes that were delegated to automatic reasoning, thus preventing errors that could lead to serious accidents.
The immersive virtual environment developed along a series of R&D projects is the basis for the research work described here and relies on a few concepts that address learning factors not normally dealt with in traditional training processes and, thus, providing an alternative learning experience capable of a greater degree of knowledge retention, exploring motivation as a key element, which can ultimately result not only in better quality of services but also make a positive contribution to workplace security in the relevant field.
Another important feature a virtual learning system must come up with is an adequate means of assessing the effectiveness of learning. However, learning evaluation is not a simple task, and it normally considers the progress the learner has made since the beginning of the training. Automatic evaluation is presented to the user as part of the gamification process. Leaderboards are one of the gamification elements players appreciate. Having their scores displayed leads to an increased motivation to carry on until they reach the desired level.
Key Terms in this Chapter
User Experience: How the user feels towards a system, taking into consideration its attractiveness and its hedonic (subjective) and pragmatic (objective) attributes.
Live-Line Maintenance: In electrical engineering, live-line maintenance refers to maintenance of high voltage energised electrical equipment.
Trainee Walk: A trainee walk is a graph representation of the sequence of task execution by a trainee.
Flow: The mental state in which practitioners enter when they take part in an activity that they deem engaging, creating a mental state which elicits deep concentration.
Gagné's Learning Model: A learning model developed by Robert Gagné, which divides the learning process in stages and treats the instructional design as a process of its own.
Error Pattern Clustering: Unsupervised technique of searching the correlated sequence of similar errors.
Gamification: The use of game-design elements and principles in contexts other than games to provide game-like experiences, with the aim of motivating and engaging users.
Model of the Trainee: In instructional design, it is the model that stores the data about the knowledge state of the trainee.
Path Graph and Task Graph: The path graph represents in graph form any feasible task sequence execution and the Task graph is the union of all of them.