DESIGN FOR BEHAVIOUR CHANGE: A MODEL-DRIVEN APPROACH FOR TAILORING PERSUASIVE TECHNOLOGIES

People generally want to engage in a healthy lifestyle, to live in harmony with the environment, to contribute to social causes, and to avoid behaviours that are harmful for themselves and others. However, people often find it difficult to motivate themselves to engage in these beneficial behaviours. Even adopting a healthy lifestyle, such as healthy eating, physical activity, or smoking cessation, is hard despite being aware of the benefits. The increasing adoption and integration of technologies into our daily lives present unique opportunities to assist individuals to adopt healthy behaviours using technology. As a result, research on how to use technology to motivate health behaviour change has attracted the attention of both researchers and health practitioners. Technology designed for the purpose of bringing about desirable behaviour and attitude changes is referred to as Persuasive Technology (PT). Over the past decade, several PTs have been developed to motivate healthy behaviour, including helping people with addictive behaviour such as substance abuse, assisting individuals to achieve personal wellness, helping people manage diseases, and engaging people in preventive behaviours. Most of these PTs take a one-size-fits-all design approach. However, people differ in their motivation and beliefs about health and what constitutes a healthy life. A technology that motivates one type of person to change her behaviour may actually deter behaviour change for another type of person. As a result, existing PTs that are based on the one-size-fits-all approach may not be effective for promoting healthy behaviour change for most people. Because of the motivational pull that games offer, many PTs deliver their intervention in the form of games. This type of game-based PTs are referred to as persuasive games. Considering the increasing interest in delivering PT as a game, this dissertation uses persuasive games as a case study to illustrate the danger of applying the one-size-fits-all approach, the value and importance of tailoring PT, and to propose an approach for tailoring PTs to increase their efficacy. To address the problem that most existing PTs employ the one-size-fits-all design approach, I developed the Model-driven Persuasive Technology (MPT) design approach for tailoring PTs to various user types. The MPT is based on studying and modelling user’s behaviour with respect to their motivations. I developed the MPT approach in two preliminary studies (N = 221, N = 554) that model the determinants of healthy eating for people from different cultures, of different ages, and of both genders. I then applied the MPT approach in two large-scale studies to develop models for tailoring persuasive games to various gamer types. In the first study (N = 642), I examine eating behaviours and associated determinants, using the Health Belief Model. Using data from the study, I modelled the determinants of healthy eating behaviour for various gamer types. In the second study (N = 1108), I examined the persuasiveness of PT design strategies and developed models for tailoring the strategies to various gamer types. Behavioural determinants and PT design strategies are the two fundamental building blocks that drive PT interventions. The models revealed that some strategies were more effective for particular gamer types, thus, providing guidelines for tailoring persuasive games to various gamer types. To show the feasibility of the MPT design approach, I applied the model to design and develop two versions of a Model-driven Persuasive Game (MPG) targeting two distinct gamer types. To demonstrate the importance of tailoring persuasive games using the MPG approach, I conducted a large-scale evaluation (N = 802) of the two versions of the game and compared the efficacy of the tailored, contra-tailored, and the one-size-fits-all persuasive games condition with respect to their ability to promote positive changes in attitude, self-efficacy, and intention. To also demonstrate that the tailored MPG games inspire better play experience than the one-size-fits-all and the contra-tailored persuasive games, I measure the gamers’ perceived enjoyment and competence under the different game conditions. The results of the evaluation showed that while PTs can be effective for promoting healthy behaviour in terms of attitude, self-efficacy, and intention, the effectiveness of persuasion depends on using the right choice of persuasive strategy for each gamer type. The results showed that one size does not fit all and answered my overarching research question of whether there is a value in tailoring PT to an individual or group. The answer is that persuasive health interventions are more effective if they are tailored to the user types under consideration and that not tailoring PTs could be detrimental to behaviour change

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

ATITIUDES TOW ARDS COMPUTER GAMES FOR LEARNING AND Pl.A YER ARCHETYPES: AN EXPLORATION OF MEASURES ON PREINTERVENTION PLAYER CHARACTERISTICS FOR SERIOUS GAME-BASED INTERVENTIONS

Serious game-based interventions offer promising health outcome results with the aid of pre-intervention player tailoring and the development of measurements that evaluate pre-intervention player characteristics and subgroups. Video gaming measures can potentially provide helpful tailoring information that discerns individual video gaming preferences which could influence their overall user experience. It is critical that measures that target adolescent video gaming be psychometrically validated. There is a gap in the literature with psychometrically validated measures evaluating adolescent attitudes towards computer games for learning and gaming archetypes. Therefore the aims of this dissertation were to 1) evaluate the psychometric properties (i.e., reliability and validity) of the Attitudes Towards Computer Games for Learning (ATCGFL) adapted from Askar et al.’s Attitudes towards computer-assisted learning (CAL) scale that assessed attitudes towards computer games for learning among a sample of adolescents, and 2) explore and identify the latent class structure (LCA) of the BrainHex measure among the same sample of adolescents. Secondary data analysis of a data set extricated from the “It’s Your Game-Family” study was conducted. Participants were youth aged 11-14 years in Houston, TX, who answered self-guided questionnaires in their home. Exploratory data analysis of the ATCGFL scale was performed. Reliability testing through analyzing internal consistency and test-retest reliability were also performed with the ATCGFL scale. Then, exploratory data analysis of the BrainHex measure was performed through latent class analysis. Results from the exploratory analysis of the ATCGFL scale suggest the adapted attitudes scale supports a 3-factor solution (Satisfaction, Motivation, and Cognition). The 3-factor solution indicates the scale has a mixed quality level of internal consistency because Factor 1 and Factor 2 we have an acceptable level of internal consistency, but Factor 3 has a questionable level of internal consistency. The test-retest reliability of the ATCGFL scale was low, but significant. Last, the latent class analysis of the BrainHex measure results revealed a 3-class model (low probability of gaming element likability gamers, moderate probability of gaming element likability gamers, and high probability of gaming element likability gamers). Overall, these findings suggest the Attitudes Towards Computer Games for Learning scale and BrainHex measure both possess promising utility as measures in tandem with serious game-based interventions, and that further research to conduct confirmatory analysis with both measures is merited

Adaptive Gamification for Learning Environments

(Scimago Q1, ATIEF A+)International audienceIn spite of their effectiveness, learning environments often fail to engage users and end up under-used. Many studies show that gamification of learning environments can enhance learners' motivation to use learning environments. However, learners react differently to specific game mechanics and little is known about how to adapt gaming features to learners' profiles. In this paper, we propose a process for adapting gaming features based on a player model. This model is inspired from existing player typologies and types of gamification elements. Our approach is implemented in a learning environment with five different gaming features, and evaluated with 266 participants. The main results of this study show that, amongst the most engaged learners (i.e. learners who use the environment the longest), those with adapted gaming features spend significantly more time in the learning environment. Furthermore, learners with features that are not adapted have a higher level of amotivation. These results support the relevance of adapting gaming features to enhance learners' engagement, and provide cues on means to implement adaptation mechanisms

The Effect of Games on Engagement and Performance in Intelligent Tutoring Systems

The purpose of this dissertation is to assess the relation between game-like elements, individual differences in gameplay, and engagement within an Intelligent Tutoring System (ITS). The current studies examined the incorporation of a game into an existing ITS, iSTART. The game, Self-explanation Showdown (Showdown) added game-like elements into the iSTART practice sessions. Incorporating games was expected to increase engagement while not affecting participants’ overall performance. However, the results of Experiment 1 indicated that game-based practice (Showdown) was more engaging than the non-game-based practice (Coached Practice), but produced lower quality self-explanation performance. The decrease in performance was attributed to the amount of pedagogical information available during the learning task. In Experiment 2, a second version of Showdown was created that added pedagogical feedback similar to the feedback provided in Coached Practice. The feedback-added version of Showdown (Showdown-FB) was expected to retain the benefits of engagement while mitigating the deficits in performance. Instead, Showdown-FB demonstrated a reduction in participants’ engagement to a level which was no longer significantly different from Coached Practice, and did not increase performance relative to the original version of Showdown. Finally, Experiment 3 investigated whether opponent difficulty would affect gameplay and how those effects may vary as a function of different types of game players (Achievers, Explorers, Socializers, Killers). The results of Experiment 3 indicated that opponent difficulty affected both performance and engagement. Participants were more engaged and produced higher quality self-explanations when playing against a highly skilled opponent. Follow-up analyses indicated that the differences in performance were likely a result of modeling responses from a highly skilled opponent. However, the effects of opponent difficulty were not affected by a participant’s gamer type