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All Journal JGGAG (Journal of Games, Game Art, and Gamification)
Towle, Bradford
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Arts Computer Science & IT Other

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Predicting Preference with Sparse Data in Personalized Gamification via Deep Learning Wilson, Philip; Towle, Bradford
Journal of Games, Game Art, and Gamification Vol. 10 No. 2 (2025)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v10i2.13037

Abstract

Personalized gamification is a practice that is relatively well defined and improves the effectiveness of a gamified system. However, in practical application the improvement is not as significant as expected. The process of personalizing a gamified system is taxing and relatively unfeasible, with far too many aspects to consider to produce an effective result. Artificial intelligence, and neural networks, can step in to alleviate much of the work, but even still results are inconsistent at best. This project seeks to remove this inconsistency by attempting to personalize only one aspect of a gamified system, rather than the entire system as a whole.  By attempting the personalization problem in this manner the amount of individual characteristics to consider is reduced dramatically, thus allowing for a neural network to more quickly and accurately determine personalization characteristics and apply them for any given user. Results show that an RNN can detect preference patterns and apply user preferences to a scheduling system. These results were produced with little run time and a more sparse dataset than normally expected for a neural network, which showcases the novel fact that detecting user preference does not require large datasets.
Effective and Immersive Teleoperation with Real-World Constraints Geary, Brendan; Towle, Bradford
Journal of Games, Game Art, and Gamification Vol. 11 No. 1 (2026)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v11i1.13749

Abstract

This study investigates the development of a telepresence system that leverages standard, industry-available hardware to support teleoperation in a cost-effective and accessible manner. Conventional telepresence solutions often rely on advanced technologies such as 360-degree or stereoscopic cameras, high-end haptic feedback devices, and specialized robotic platforms. While these approaches can deliver highly immersive experiences, they frequently involve significant implementation costs, limited accessibility, or insufficient locomotion support, which restrict their broader adoption. Consequently, there is a critical need for a telepresence method that balances usability, immersion, and affordability while maintaining precise and reliable control mechanisms. The proposed solution integrates commercially available virtual reality (VR) equipment with a mobile robotic platform to construct a virtual environment that enhances user interaction and spatial awareness. Real-time video input from the robot’s onboard camera is projected into the VR environment, enabling users to perceive the remote physical space intuitively. To compensate for hardware limitations, the system incorporates visual cues that represent the robot’s orientation, movement direction, and control latency. These cues play a crucial role in improving situational awareness and assisting users in making informed navigation decisions during teleoperation tasks. The study evaluates the system in terms of control simplicity, precision, and overall usability. Particular emphasis is placed on how the virtual environment mitigates latency effects and provides smooth locomotion feedback, resulting in a fluid user experience. The findings demonstrate that effective telepresence can be achieved using standard hardware, offering a practical alternative to more complex and expensive systems while maintaining immersive and accurate teleoperation capabilities.
Co-Authors Geary, Brendan Wilson, Philip