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
