Experience-driven MAR games: Personalising Mobile Augmented Reality games using Player Models

PhD ThesesWe are witnessing an unprecedented growth of Mobile Augmented Reality (MAR) technologies, one of the main research areas being MAR games. While this field is still in its early days, researchers have shown the physical health benefits of playing these type of games. Computational models have been used in traditional (non-AR) digital games to predict player experience (PX). These models give designers insights about PX, and can also be used within games for real-time adaptation or personalised content generation. Following these findings, this thesis investigates the potential of creating models that use movement data and game metrics to predict PX. An initial pilot study is conducted to evaluate the use of movement data and game metrics to predict players’ emotional preferences between different game levels of an exploration-based MAR game. Results indicate that emotional preferences regarding frustration (≈ 93%) and challenge (≈ 93%) can be predicted to a reliable and reasonable degree of accuracy. To determine if these techniques can be applied to serious games for health, an AR exergame is developed for experiments two, three and four of this thesis. The second and third experiments aim to predict key experiential constructs, player competence and immersion, that are important to PX. These experiments further validate the use of movement data and game metrics to model different aspects of PX in MAR games. Results suggest that players’ competence (≈ 73%) and sense of mastery (≈ 81%) can be predicted to a reasonable degree of accuracy. For the final experiment, this mastery model is used to create a dynamic difficulty adaptation (DDA) system. The adaptive exergame is then evaluated against a non-adaptive variant of the same game. Results indicate that the adaptive game makes players feel a higher sense of confidence during gameplay and that the adaptation mechanics are more effective for players who do not engage in regular physical activity. Across the four studies presented, this thesis is the first known research activity that investigates using movement data and game metrics to model PX for DDA in MAR games and makes the following novel contributions: i) movement data and game metrics can be used to predict player’s sense of mastery or competence reliably compared to other aspects of PX tested, ii) mastery-based game adaptation makes players feel greater confidence during game-play, and iii) mastery-based game adaptation is more effective for players who do not engage in physical activity. This work also presents a new methodology for PX prediction in MAR games and a novel adaptation engine driven by player mastery. In summary, this thesis proposes that PX modelling can be successfully applied to MAR games, especially for DDA which results in a highly personalised PX and shows potential as a tool for increasing physical activity

Are You Open? : A Content Analysis of Transparency and Openness Guidelines in HCI Journals

Within the wider open science reform movement, HCI researchers are actively debating how to foster transparency in their own field. Publication venues play a crucial role in instituting open science practices, especially journals, whose procedures arguably lend themselves better to them than conferences. Yet we know lit- tle about how much HCI journals presently support open science practices. We identified the 51 most frequently published-in jour- nals by recent CHI first authors and coded them according to the Transparency and Openness Promotion guidelines, a high-profile standard of evaluating editorial practices. Results indicate that jour- nals in our sample currently do not set or specify clear openness and transparency standards. Out of a maximum of 29, the modal score was 0 (mean = 2.5, SD = 3.6, max = 15). We discuss potential reasons, the aptness of natural science-based guidelines for HCI, and next steps for the HCI community in furthering openness and transparency