A three‐step model for the gamification of training and automaticity acquisition. (21st February 2021)

• Record Type: Journal Article
• Authors: Jamshidifarsani, Hossein; Tamayo‐Serrano, Paul; Garbaya, Samir; Lim, Theodore
• Is Part Of: Journal of computer assisted learning. Volume 37:Number 4(2021)
• Page Start: 994
• Page End: 1014
• Publication Date: 2021-02-21
• Subjects: automaticity -- game modelling -- gamification -- skill acquisition -- training; Computer-assisted instruction -- Periodicals; 371.334
• DOI: 10.1111/jcal.12539
• Languages: English
• ISSNs: 0266-4909

Abstract

Abstract: 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. Lay Description: What is already known about this topic: Automaticity is omnipresent in our daily lives. It includes a wide variety of physical or cognitive abilities. Training principles for knowledge acquisition are different from automaticity acquisition. Fostering automaticity needs a substantial amount of repetition, which in turn, requires motivation and engagement. Game‐based approaches can improve motivation and engagement. However, their designs have remained largely arbitrary and do not follow clear design guidelines. What this paper adds: Current serious game‐design models target knowledge acquisition or learning in general. However, the presented model focuses specifically on automaticity acquisition. This paper provides an updated and comprehensive overview of the key training elements required for the acquisition of automatic skills. This three‐step model is proposed based on the evidence in the literature; it suggests the appropriate training elements, and game mechanics for each phase of automaticity acquisition. The results of the experimental study validated the effectiveness of the proposed model. Implications for practice and/or policy: Medical conditions, in which an automatic skill (physical or cognitive) is affected, can greatly benefit from this serious game design model. In designing serious games, a differentiation should be made between knowledge acquisition and skill acquisition (including automaticity acquisition). Automaticity acquisition goes though phases and this should be reflected in both training design, as well as, serious game design. Some training design choices are task‐specific or depend on the target group (e.g., age). Hence, their designs should follow recommendations extracted from the literature.