Game Mechanism Design Research Articles

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers’ knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with stroke survivors. The methodologies utilized professional medical experiences and real-life evidence data gathered during the duration of the iTrain project to create the scenarios for the game’s deep reinforcement caregiver behavior improvement model, as well as the design of game mechanics, game images and game characters, and gameplay implementation. Furthermore, the results of the game’s direct impact on caregivers (n = 25) and stroke survivors (n = 21) in Lithuania using the Geriatric Depression Scale (GDS) and user experience questionnaire (UEQ) are presented. Both surveys had favorable outcomes, showing the effectiveness of the approach. The GDS scale (score 10) revealed a low number of 28% of individuals depressed, and the UEQ received a very favorable grade of +0.8.

Designing cognitive-based game mechanisms for mobile educational games to promote cognitive thinking: an analysis of flow state and game-based learning behavioral patterns

ABSTRACT This study proposed a cognitive-based game mechanism based on the revised Bloom's taxonomy of cognitive processing dimensions. Moreover, a mobile Chinese history educational game, Void Broken: The Qing Dynasty, was developed based on the cognitive-based game mechanism to promote learners’ cognitive thinking in history learning. This empirical study conducted an experiment with a quasi-experiment design and the participants were 70 high school freshmen from northern Taiwan, who were selected by convenience sampling. The present study analyzed the participants’ historical knowledge gains and flow state. This study proposed an operational behavior coding scheme to analyze learning behaviors and evaluate the proposed cognitive-based game mechanism design and conducted sequential analyses to explore their learning behavioral patterns and their collaborative problem solving (CPS) behaviors in the game-based learning (GBL) process. The results suggested that the mechanism helped learners’ historical knowledge gains, flow, and CPS behaviors. The high flow groups showed more multifaceted and active problem-solving behavioral patterns than the low flow groups. We suggest educational game designers consider including the cognitive-based game mechanism and flow antecedents into their game designs.

Serious Game iDO: Towards Better Education in Dementia Care

We describe the iDO serious game developed during implementation of the Innovative Digital Training Opportunities on Dementia for Direct Care Workers (IDO) project. The project targets formal and informal caregivers of persons with dementia in order to improve caregiver knowledge and competences skills with a non-traditional source of training. This paper describes the steps faced to define the iDO caregiver behavior improvement model, design of game mechanics, development of game art and game characters, and implementation of gameplay. Furthermore, it aimed to assess the direct impact of the game on caregivers (n = 48) and seniors with early signs of dementia (n = 14) in Lithuania measured with the Geriatric Depression Scale (GDS) and Dementia Attitudes Scale (DAS). The caregivers’ GDS scores showed a decrease in negative answers from 13.4% (pre-game survey) to 5.2% (post-game survey). The seniors’ GDS scores showed a decrease in negative answers from 24.9% (pre-game survey) to 10.9% (post-game survey). The overall DAS scores increased from 6.07 in the pre-game survey to 6.41 in the post-game survey, statistically significant for both caregivers and seniors (p < 0.001), respectively. We conclude that the game aroused positive moods and attitudes for future caregivers of persons with dementia, indicating a more relaxed status and a decreased fear in accomplishing the caring process.

Designing With Game-Based Learning: Game Mechanics From Middle School to Higher Education

Background. The use of game-based learning strategies in higher education has shown promise to increase student motivation and achievement. Little is known about which game mechanics are most appropriate and effective for learners at different academic levels. Aim. This article examines teacher selection and implementation of game mechanics in 27 courses from middle school to higher education designed with game-based learning. Method. 27 educators participated in an open-ended survey on the design of their course. Measures included an open response survey on patterns and design of game mechanics with results validated through member checking. Results. Findings included different choices of game attritubes and game elements in courses in middle school, high school, and higher education. Teacher selection and inclusion of game mechanics changed over time to better meet the needs of students. Conclusions. The structure of game-based learning at different levels will vary to meet the developmental and academic needs of students, but more work is needed in determining which strategies are most effective for learning.

Social networked game dynamics in web 2.0 language learning communities

This paper critically examines the role of game dynamics facilitated by social networks, and their potential for language learning and peer support. The context of investigation is made up of four types of web 2.0 language learning communities. The design of game mechanics as incentives for digital activity across the community types is scrutinized in terms of the extent to which these mechanics support language learning, regarding learners' roles, expected behaviours and attitudes to language learning. We situate our analysis in the broader web 2.0 context and link it to the crowdsourcing trend (Howe, 2006) whereby, as two sides of the same coin, user engagement is fostered in an unrestricted and collaborative manner, and the latent talent of the crowd can then be exploited by commercial companies for no remuneration. Our findings show that game mechanics model user activity by following a sophisticated incentive-centred approach, not always made clear to users before entering these communities. The analysis clearly demonstrates that game mechanics are the driving force of overall online activity, targeting language learning although not exclusively, and that game mechanics may benefit language learning as well as the commercial interests of these communities supported by web 2.0 start-ups.