Comparison of a Gamified and Non-Gamified Virtual Reality Training Assembly Task

Palmas, Fabrizio; Labode, David; Plecher, David A.; Klinker, Gudrun

doi:10.1109/vs-games.2019.8864583

Cited by 34 publications

(20 citation statements)

References 29 publications

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“…Hypotheses H1a (number of mistakes) and H1b (TTC) were rejected based on our study's experimental data. Our findings contrast with the literature which has identified a reduced number of technical assembly mistakes for gamified virtual reality training [4]. A possible explanation for our nonsignificant result could be an insufficient level of task complexity and the related ceiling effect [87].…”

Section: Discussioncontrasting

confidence: 99%

“…Since learners' motivation is quantified using subjective scales and is further highly dependent on their pre-motivation [74], it is essential to investigate the change in motivation induced by the training session. Thus, the variable training motivation is defined as the ratio of post-training motivation and pre-training motivation, as summarized by formula (4).…”

Section: )mentioning

confidence: 99%

“…Gamification is defined as the use of game design elements in non-game contexts [3]. However, empirical understanding of gamification's potential to enhance virtual training remains limited, particularly in industrial learning contexts [4], [5]. Given manifold design options to implement gamification in virtual training environments, an important but underexplored research question is how different gamification elements affect learning.…”

Section: Introductionmentioning

confidence: 99%

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Gamification Intensity in Web-Based Virtual Training Environments and Its Effect on Learning

Bohne¹,

Heine²,

Muêller³

et al. 2023

IEEE Trans. Learning Technol.

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Gamification approaches to learning use game-inspired design elements to improve learning. Given manifold design options to implement gamification in virtual environments, an important but underexplored research area is how the composition of gamification elements affects learning. To advance research in this area, we systematically identified key design elements that have shown promise in leading to positive learning results. We then conducted an experiment in which we varied gamification intensity in web-based virtual training environments for a procedural industrial task. 355 participants were divided into a baseline group without gamification, a basic, and an advanced gamification group. Analysis of participants' learning included learning outcomes (time-to-completion and number of mistakes), affective learning factors (motivation, self-efficacy, satisfaction), learning system usability and perceived cognitive load throughout the learning process. The results did not show any statistically significant difference favoring the higher levels of gamification intensity with respect to lower ones. Conversely, we found that participants' computer gaming habits and technical equipment (display size and computer pointing device) significantly influenced learning.

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Section: Discussioncontrasting

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gamification Intensity in Web-Based Virtual Training Environments and Its Effect on Learning

Bohne¹,

Heine²,

Muêller³

et al. 2023

IEEE Trans. Learning Technol.

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“…In [24], Bertram et al evaluated 10 assistance systems for assembly operations from 6 aspects relevant for intelligent working stations and concluded that none of the 10 systems fully covers the automated generation of work plans, flexible integration in production, or autonomous learning ability. The benefits of gamification (e.g., faster training, less errors) versus a non-gamified approach were revealed in a study involving 50 participants by Palmas et al [25]. In [26], several heuristic-based approaches were used in order to optimize picking orders in fulfillment warehouses.…”

Section: Related Workmentioning

confidence: 99%

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Gellért

Sorostinean

Pirvu

2022

Sensors

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Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

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“…The immersive setup of VR provides a real environment-like experience to users that enables them to interact with virtual contents by performing certain tasks [ 32 ]. Gamified VE attracts the attention of a participant more effectively than a game alone due to the addition of immersion with gamification [ 33 ]. Further, the incorporation of challenges, avatars, and feedback in the form of rewards or points as gamification elements into VR-based games facilitates a higher rate of user attraction and motivation [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space

Khan

Muhammad

Hussain

et al. 2021

Sensors

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Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.

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Comparison of a Gamified and Non-Gamified Virtual Reality Training Assembly Task

Cited by 34 publications

References 29 publications

Gamification Intensity in Web-Based Virtual Training Environments and Its Effect on Learning

Gamification Intensity in Web-Based Virtual Training Environments and Its Effect on Learning

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space

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