Authors:
Klaus de Geus
1
;
Rafael T. Beê
1
;
Vinícius M. Corrêa
1
;
Ricardo C. R. dos Santos
2
;
Alexandre P. de Faria
2
;
Elton M. Sato
3
;
Vitoldo Swinka-Filho
3
;
Awdry F. Miquelin
3
;
Sergio Scheer
2
;
Paulo H. Siqueira
2
;
Walmor C. Godoi
3
;
Matheus Rosendo
3
and
Yuri Gruber
3
Affiliations:
1
Copel Geração e Transmissão S. A., Rua José Izidoro Biazetto, 158, 81.200-240 Curitiba, Brazil
;
2
Universidade Federal do Paraná, PPGMNE, CESEC, Centro Politécnico, 81.530-900 Curitiba, Brazil
;
3
Lactec, Rod BR 116, 8.813, Jardim das Américas, 81531-980 Curitiba, Brazil
Keyword(s):
Virtual Reality, Gamification, Learning Theories, Gagné’s Learning Model, Electrical Energy Maintenance, Critical Activities.
Abstract:
This paper describes a virtual environment solution for the training of electricians in critical activities, namely, live-line maintenance, in electrical energy substations. The main concept of the virtual environment is the mapping between virtual reality technology and gamification methods with learning theories, in particular, Gagné’s cognitive model. In order to explore the benefits of gamification, the system uses concepts established by the Flow Theory, the Magic Circle concept as well as the Player Experience of Need Satisfaction (PENS) theory. 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. However, they may use misleading information in order to induce the trainee to make mistakes and thus provide a means of exercising decision making in adverse conditions, which is an important stage in the learning process, especially in the context of critical activities. Add
itionally, an automatic feedback system based on the visualization of error patterns highlights not only the mistakes made in the virtual experience, but also the strategy for solving the proposed problem. Tests were carried out aiming at measuring several aspects, ranging from usability, perception of benefits and learning effectiveness. A trainee classification process is proposed based on the analysis of human error patterns during the execution of a task. The modelling of knowledge is based on the literature on human reliability and results from the application of tools such as task analysis and knowledge extraction from expert users when interacting with the system (expert elicitation). Clustering techniques applied to error patterns allows for the identification of prototypes of performance classes and their visualization in the form of distinct groups. Results of different assessment processes, based on the view of potential users, are presented, analysed and discussed. Future work includes the conclusion of the automatic evaluation process, based on the analysis and visualization of human error.
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