The People-Game-Play model for understanding videogames' impact on wellbeing

Given the increasing popularity of videogames, understanding when, how and for whom they have a positive or negative impact on wellbeing is critical. We propose a model for exploring these questions based on existing literature and our own research. The People-Game-Play model identifies player characteristics, game features and the experience of play as key determinants of the impact of videogame play on wellbeing. We propose research exploring the relationships within and between each of these key factors is needed and identify some examples of future research in this space

GameFlow 2020: 15 Years of a Model of Player Enjoyment

The original GameFlow model was first published in 2005 and in the last 15 years it has seen thousands of citations and hundreds of applications to designing and evaluating games and gameful experiences. Previous work has sought to test and validate the model by applying it to different game experiences to further understand those experiences and to expose any weaknesses of the model. In this paper, we survey over 200 applications of GameFlow over the last 15 years, to understand how, where, and why the model has been applied. We found that the model has been applied to a diverse set of experiences, domains, platforms, audiences, and used in a variety of ways. This work lays the foundations for targeting the next version of the GameFlow model towards the most valuable and appropriate applications and to define how it fits within the broader landscape of player experience tools

Investigating Player Experience in Virtual Reality Games via Remote Experimentation

This research explores player experience of virtual reality (VR) games through two stages of study. In both stages, we employed the Player Experience Inventory (PXI), a validated tool designed to evaluate player experience. In Stage 1, player experience of VR games was investigated via an online survey with 100 participants. We found that Audio-Visual Appeal, Immersion, and Ease of Control contributed most to player experience in VR games. We found no relationship between player experience and age, time spent playing, VR experience, or VR headset. Stage 2 used remote experimentation to compare VR and non-VR games with 10 participants. We found that differences in player experience can be explained by the Immersion, Progress Feedback, and Curiosity constructs of the PXI

Prototyping a Grip Pressure-Sensing Controller for Video Games

There have been few changes to the current standard game controllers since the introduction of the Dual Analog Controller for the PlayStation in 1997. Refinements have been made and some unique active control schemes (e.g., Wii Remote) have been released. However, there has been minimal development of passive biometric player inputs (i.e., not directly and consciously controlled by the player). Passive biometric inputs have the potential to enhance player experience by tailoring the game based on the player’s changing physiological state. In this paper, we report on the development and testing of a new prototype pressure sensor designed to be integrated into a game controller. The prototyping and testing undertaken as part of this report has produced a system that shows promise for inferring the activity and state of the player and for implementation into future controller designs. Such a system could be used to read and adapt to the emotional state of a player for a customised play experience

Affording Enjoyment in VR Games: Possibilities, Pitfalls, and Perfection

In this paper, we present research that aims to understand affordances and inhibitors of enjoyment in virtual reality (VR) video games. We apply the GameFlow model to analyse VR and non-VR versions of the same games to identify differences in enjoyment in VR games. Our approach involves conducting qualitative analysis on video game reviews, using GameFlow as a theoretical foundation. We report on our analysis of the games Superhot and Skyrim. We find that affordances are largely consistent between VR and non-VR versions of the same games, with a few key differences related to Feedback, Control, Player Skills, and Immersion. We conclude that GameFlow is applicable to VR games, with the addition of a Comfort element to describe player comfort while playing

The Effect of Using an Auto-Generated Anime Avatar on Player-Avatar Identification

Game avatars are a way to represent players within a virtual game world. Player-avatar identification can improve game enjoyment, where avatar customisation and player-avatar similarity are potential factors that contribute to identification. Recent emerging technologies, such as image translation algorithms, offer new alternatives for avatar customisation. However, its effectiveness remains unknown. Image translation algorithms enable users to automatically generate avatars that look similar to themselves from their self images (selfies). This research aims to investigate how player-avatar appearance similarity can affect identification by conducting three comparative experiments with different avatar appearance within an offline single-player game. Our results indicate a positive effect of using an auto-generated anime avatar compared to using a default avatar. No correlation was found between player-avatar appearance similarity and player-avatar identification in general. We therefore posit that avatar customisation acts as a greater contributor than player-avatar appearance similarity to avatar identification in offline single-player games

Curriculum Generation and Sequencing for Deep Reinforcement Learning in StarCraft II

Reinforcement learning has proven successful in games, but suffers from long training times when compared to other forms of machine learning. Curriculum learning, an optimisation technique that improves a model’s ability to learn by presenting training samples in a meaningful order, known as curricula, could offer a solution for reinforcement learning. Due to limitations involved with automating curriculum learning, curricula are usually manually designed. However, due to a lack of research into effective design of curricula, researchers often rely on intuition and the resulting performance can vary. In this paper, we explore different ways of manually designing curricula for reinforcement learning in real-time strategy game, StarCraft II. We propose three generalised methods of manually creating tasks for curriculum learning and verify their effectiveness through experiments. We also experiment with different curricula sequences, in addition to the most commonly used easy-to-hard order. Our results show that all three of our proposed methods can improve a reinforcement learning agent’s learning process when used correctly. We demonstrate that modifying the state space of the tasks is the most effective way to create training samples for StarCraft II and that reversed curricula can be beneficial to an agent’s convergence process under certain circumstances

Detecting Spam Game Reviews on Steam with a Semi-Supervised Approach

The potential value of online reviews has led to more and more spam reviews appearing on the web. These spam reviews are widely distributed, harmful, and difficult to identify manually. In this paper, we explore and implement generalised approaches for identifying online deceptive spam game reviews from Steam. We analyse spam game reviews and present and validate some techniques to detect them. In addition, we aim to identify the unique features of game reviews and to create a labelled game review dataset based on different features. We were able to create a labelled dataset that can be used to identify spam game reviews in future research. Our method resulted in 5,021 of the 33,450 unlabelled Steam reviews being labelled as spam reviews, or approximately 15%. This falls within the expected range of 10-20% and maps to the Yelp figures of 14-20% of reviews are spam