Engagement in a Virtual Reality Game with Gesture Hand Interface. An Empirical Evaluation of User Engagement Scale (UES)

Landa-Avila, Cecilia; Cruz, María-Luisa

doi:10.1007/978-3-319-58637-3_33

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Moreover, after completing 10 stages in each scenario, a player was given a questionnaire. This questionnaire was adapted from previous research [40], [41] that contains 31 items. The User Engagement Scale (UES) questionnaire was used to measure player engagement in each scenario.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Fibonacci Level Adjustment for Optimizing Player’s Performance and Engagement

Masyfa

Tolle²,

Tibyani³

et al. 2023

IJSG

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Players’ engagement intensity in computer games is influenced by the level of difficulty the game offers. Traditional game-level plots adopt linear increases that sometimes do not match the users’ skill growth, causing boredom and hampering the users’ further skill growth. In this study, a nonlinear level adjustment scenario was proposed based on the Fibonacci sequence that provides gradual increases in the early stages of the games but more drastic changes in later phases. Here, the game’s difficulty level was automatically decided by a machine learning method. To test the proposed method, comparisons between four level adjustments in computer games: traditional plots, self-selected plots, linear adaptive plots, and the proposed nonlinear adaptive plots were run. The experiment was carried out with 40 testers. The experiment results show that the best player’s peak level in the proposed nonlinear adjustment was twice as high as that of linear adjustment. Also, the number of stages required to reach the peak under the proposed scenario was half that of linear games. This high playing performance goes hand in hand with deep playing engagement. The results demonstrate the efficiency of the proposed level adjustment algorithm.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Fibonacci Level Adjustment for Optimizing Player’s Performance and Engagement

Masyfa

Tolle²,

Tibyani³

et al. 2023

IJSG

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“…In this survey, users are asked to answer questions about their experience with the product. This survey was later adapted by Landa-Avila and Cruz [18] for a virtual game to analyze six aspects: endurability, novelty, perceived usability, aesthetics, felt involvement, and focused attention. After data analysis, the authors found that the UES questionnaire can be used to measure the UE in a video games context; however, there were objections about the test's duration and misunderstandings about some items.…”

Section: An Overview Of User Engagement In Video Gamesmentioning

confidence: 99%

User Engagement Comparison between Advergames and Traditional Advertising Using EEG: Does the User’s Engagement Influence Purchase Intention?

et al. 2022

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In the context of human–computer interaction (HCI), understanding user engagement (UE) while interacting with a product or service can provide valuable information for enhancing the design process. UE has been a priority research theme within HCI, as it assesses the user experience by studying the individual’s behavioral response to some stimulus. Many studies looking to quantify the UE are available; however, most use self-report methods that rely only on participants’ answers. This study aims to explore a non-traditional method, specifically electroencephalography, to analyze users’ engagement while interacting with an advergame, an interactive form of advertising in video games. We aim to understand if a more interactive type of advertising will enhance the UE and whether, at the same time, it would influence the user’s purchase intention (UPI). To do this, we computed and compared the UE during the interaction with an advergame and a conventional TV commercial while measuring the participants’ brain activity. After the interaction with both types of advertising, the UPI was also evaluated. The findings demonstrate that a more interactive advertisement increased the participants’ UE and that, in most cases, a UE increment positively influenced the UPI. This study shows an example of the potential of physiological feedback applications to explore the users’ perceptions during and after the human–product interaction. The findings show how physiological methods can be used along with traditional ones for enhancing the UE analysis and provide helpful information about the advantages of engagement measurement in HCI applications.

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“…Engagement was measured using O'Brien et al's [14] User Engagement Survey, which consists of thirty 5-point Likert style questions measuring four latent variables -focused attention, perceived usability, aesthetic appeal and reward factor and has been used previously to measure engagement in VR games [33]. Each of these sub scores are averaged for each category and the totals summed together to derive an overall score.…”

Section: Metricsmentioning

confidence: 99%

Logibot: Investigating Engagement and Development of Computational Thinking Through Virtual Reality

Sims

Rutherford

Sukumaran

et al. 2021

2021 7th International Conference of the Immersive Learning Research Network (iLRN)

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In this study we assess the effects of teaching fundamental programming concepts through a virtual reality (VR) block-based programming application and its impact on engagement. As a comparison study, participants played an existing desktop-based game (LightBot) and the developed VR game (LogiBot) covering similar gameplay mechanics and blockbased programming. Initial results indicate that traditional desktop applications are currently more engaging than VR for teaching programming. We thus identify the need for careful design of interaction methods to support ease of use and reward factors to promote engagement in VR-based learning applications beyond the initial wow-factor.

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Engagement in a Virtual Reality Game with Gesture Hand Interface. An Empirical Evaluation of User Engagement Scale (UES)

Cited by 5 publications

References 19 publications

Fibonacci Level Adjustment for Optimizing Player’s Performance and Engagement

Fibonacci Level Adjustment for Optimizing Player’s Performance and Engagement

User Engagement Comparison between Advergames and Traditional Advertising Using EEG: Does the User’s Engagement Influence Purchase Intention?

Logibot: Investigating Engagement and Development of Computational Thinking Through Virtual Reality

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