Evaluating User Experience in Games: Concepts and Methods - Lirmm

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96 P. Lemay and M. Maheux-Lessard Fig. 6.1 Example of the semantic differential question sheet 3 2 1 0 1 2 3 Healthy o o o o o o o Unhealthy Affordable o o o o o o o Costly Slow o o o o o o o Fast Developing o o o o o o o Devaluing Stressful o o o o o o o Relaxing Easy o o o o o o o Difficult Demotivating o o o o o o o Motivating Popular o o o o o o o Unpopular Exciting o o o o o o o Boring Funny o o o o o o o Serious Good o o o o o o o Bad Distressing o o o o o o o Reassuring Pleasant o o o o o o o Unpleasant 6.3.5 Description of Participants (Descriptive Analyses) From the 105 participants who begun the online survey, 80 completed it during spring 2007. Participants’ profiles were as follows: there were slightly more women (44) than men (36). Thirty-seven participants were in the 18–25 years old category, 23 were 26–35 years old, 12 were 36–45 years old, 5 were 46–55 years old, and 3 were 56 years old and over. Fifty participants were students at the time, 27 were employed, and 1 was unemployed; 2 specified the “other” category. Regarding the participants’ use of their free time, the participants’ four most frequent leisure activities were reading (92.5%), Internet use (87.5%), watching TV (86.2%), and doing physical activity (78.8%). Playing videogames was an activity that reached 48.8% of the participants. The least frequent leisure activities were doing crosswords/sudokus (32.5%) and practicing a musical instrument (20%). Delving into the patterns of use of videogames, 24 out of the 39 participants who indicated that they play videogames played less than 5 h a week, while 9 participants played from 5 to 9 h a week, 2 played from 10 to 14 h. Four of them enjoyed videogames for 15 h and more. 6.3.6 Multidimensional Analyses of Attitudes Toward Leisure and Games Once the global profile of the population sample was performed and the pattern of leisure use was established, the basic semantic profile for each leisure activity and for each pair of adjectives was computed. Medians were used as a descriptive statistic instead of the typical average because answers must be considered as ordinal data, not continuous ones.
6 Investigating Experiences and Attitudes Toward Videogames 97 How did participants perceive the seven leisure activities? The statistical median for each pair of adjectives and each activity was computed and plotted on graphics. When all seven activities are plotted together, some patterns could be detected, but they are more difficult to perceive. There seems to be a general trend for which most leisure activities were evaluated. Graphically, no activity seems to be perceived radically different from the others. A very few activities have gathered medians of ±3, which means that more than 50% of the participants evaluated the leisure in a strong manner; those activities that gathered such high marks are physical activities, reading, and watching TV. In order to make sense of a particular activity, it can be plotted individually. Let’s use the videogame activity as an example, as shown in Fig. 6.2. Fig. 6.2 Semantic profile for the videogame activity
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Human-Computer Interaction Series E
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Regina Bernhaupt Editor Evaluating
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For Philippe
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viii Foreword Chapter 3, Presence I
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x Foreword personality which evolve
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Contents Part I Introduction to Eva
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Contributors Regina Bernhaupt IRIT,
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Contributors xvii Manfred Tscheligi
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xx About the Authors Emily Brown is
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xxii About the Authors a post-docto
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xxiv About the Authors Christina Ko
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xxvi About the Authors MIT Media La
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xxviii About the Authors Theft Auto
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Chapter 1 User Experience Evaluatio
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1 User Experience Evaluation in Ent
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1 User Experience Evaluation in Ent
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Chapter 2 Enabling Social Play: A F
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2 Enabling Social Play 13 It is als
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2 Enabling Social Play 15 observati
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2 Enabling Social Play 17 for playe
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2 Enabling Social Play 19 to provid
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2 Enabling Social Play 21 Colwell J
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Chapter 3 Presence, Involvement, an
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3 Presence, Involvement, and Flow i
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150 K. Poels et al. in games. This
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152 K. Poels et al. their game char
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154 K. Poels et al. If we apply thi
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156 K. Poels et al. Participants ge
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158 K. Poels et al. 9.3.2 Exploring
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160 K. Poels et al. If our train is
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162 K. Poels et al. would be intere
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Chapter 10 Evaluating User Experien
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Chapter 11 Evaluating Exertion Game
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226 M. Brown et al. Count 450 400 3
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228 M. Brown et al. feedback. Thus,
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230 M. Brown et al. Desurvivre H, C
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Chapter 13 Using Heuristics to Eval
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Index A Advanced interaction games,
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