Training design for automatic skills has a vast domain of application, such as education, physical and cognitive rehabilitation, as well as sports, arts and professional training. Gamification concept used in technology-assisted training has the potential to increase motivation, engagement and adherence to the training programme. Currently, the general gamification models of learning, did not take into account the temporal specificity of the game elements for automaticity acquisition training. In order to address this problem, an extensive overview of the key training attributes that impact automaticity acquisition was carried out. Then, based on this review, the three steps of a proposed model were presented. The first step of this model, named Task Analytics, helps with task-specific training decisions. The second step provides descriptive and prescriptive approaches for the three phases of automaticity acquisition (fast learning, slow learning and automatization). The descriptive part characterizes each phase using psychological and performance-related qualities, while the prescriptive part recommends the appropriate training elements for each phase. Based on the prescriptive part, a game-design model is proposed in the third step, which classifies the game mechanics and maps them onto each phase of automaticity acquisition. Finally, to validate this approach, a mobile game was designed based on the proposed gamification model, and it was compared to control design. The two approaches are tested with 49 participants. The results showed that the experimental group had a significantly better engagement and higher performance. Furthermore, the experimental group showed significantly better performance in a multitasking challenge designed to evaluate the automaticity. The main contribution of this article is the proposed game design model that takes into account the temporal specificity of game elements during the acquisition of automaticity.
- game modelling
- skill acquisition
ASJC Scopus subject areas
- Computer Science Applications