2011 International Conference on Semantic Technology and Information Retrieval 28-29 June 2011, Putrajaya, Malaysia
Measuring Flow in Gaming Platforms Azmi Omar and Nazlena Mohamad Ali School of Information Science, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia
[email protected],
[email protected]
Abstract— This paper describes the work in measuring flow while interacting with multi-platform games interfaces. We identified different gaming platforms which are mobile and non-mobile platform. The objective of the study is to identify and measure the variation of flow in different platforms. The early pilot data are collected through a survey form that are designed based on GameFlow criteria. The GameFlow criteria consist of eight elements which are: concentration; challenge; skills; control; clear goals; feedback; immersion and social interaction. In this exploratory study, the questionnaires of the survey are slightly altered from the GameFlow criteria identified in the previous work. From the early survey data collected, it shows the variation of player’s expression and thought towards different kind of gaming platforms. Finding also shows that PC and Console are preferred by the respondent as their main platform to play games. The finding of this exploratory study can be used as additional guidelines in the future development of games application.
• • • • • •
Clear goals Direct Feedback Concentration on the task at hand The sense of control The loss of self-consciousness The transformation of time
Csikszentmihalyi mentioned that not all of these components are needed for flow to be experienced [5], different platform might result a different findings. This paper will discuss on exploratory studies that has been conducted mainly to measure flow attributes in different gaming platform. At this level, studies are divided into two parts which is pilot study to gather initial information regarding the topic and the second part is controlled lab-experiment.
Keywords: engagement, flow, games
I. INTRODUCTION Engagement was define as a cognitive [4], affective [2] and behavioural [3] state of interaction with a computer application. Engagement is the derivative concept that arises from Flow theory by Mihaly Csikszentmihalyi. Csíkszentmihalyi outlines his theory that people are most happy when they are in a state of Flow, a state of concentration or complete absorption with the activity at hand and the situation [5]. Previous research has been done regarding the topic of GameFlow [9] and measuring the user engagement [7]. Sweetser and Wyeth [ 9] conducted an expert review on two real-time strategy games, the high-rating and the low-rating games, while L.O’ Brien and G. Toms [7] develop and conducted a survey to measure engagement through factor analysis and Structural Equation Modelling (SEM) methodology on an online shopping. According to Csikszentmihalyi, Flow has eight major factors [5], which are: • A challenge activity that requires skills • The merging of action and awareness
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This paper was organized in the following sections. Section II elaborates briefly on the methodology and followed by the results of the evaluation in Section III. Section IV discussed the findings and finally the conclusions are reported in Section V. II. METHODOLOGY The data for early studies are collected by using survey form that consists of two sections. Section A is about demographic information and Section B is on gaming experiences. The questions in Section B are slightly altered based on GameFlow criteria [3]. Participants are approached by: A. Online survey form Direct link to the survey form has been posted in one of the selected gamers forum and social network site for two weeks. User name (if any) and their IP address are recorded to ensure there is no redundant input from the participant. B. Direct approach survey form
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The data also collected by using conveentional hardcopy survey form. Participants are randomly chosen from the university student to participate in the surveey form. Collected data are analysed in SPSS and recoded to their respected value. The missing data on section B are recoded into value “3”. An initial exploration involve frequuencies, mean and standard deviations are conducted to ensure there is no data missing and to analyze the rate of non-responsee for each item. III. RESULTS AND DISCUSSION N A total number of 63 participants have com mpleted the survey. There were 63 participants (N=63) response to the survey, 20 (31.74%) of them are male respondent andd the rest are 43 (68.25%) female respondent. The participantss are vary in ages ranges which is, 15-20 years old (N=20; M= =6, F=14), 21-26 year old (N=32; M=10, F=22), 27-33 years old (N=9; M=3, F=6) and 33 and above (N=2; M=1, F=1).. In employment percentage, most of the participant are studennt (N=39; 61.9%) followed by employed participant (N=20; 31.7%) and unemployed participant (N=4; 6.3%). All the information regarding participants is illustrated in the folllowing Figure 1, Figure 2 and Table I.
25
22
20 14
15
10
Male(M)
10 6
Female(F)
6
5
3 1 1
0 15-20
21-26
27-32
33 and above
Figure 2: Number of participants by ages range.
TABLE I: Percentage of participants by employment.
Freq. Valid
31.74 % 68.25 %
39 20 4 63
% 61.9 31.7 6.3 100.0
Valid % 61.9 31.7 6.3 100.0
Male(M) Female(F)
Figure 1: Percentage of participants by geender.
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student employed unemployed Total
g in the survey question There were four (4) platforms given to choose by the participant which arre: PC, Mobile, Console and Handheld. PC and Console are classified as “Non-Mobile a Handheld classified as Platform (NMP)” while Mobile and “Mobile Platform (MP)”. From thee survey result, most of the participant preferred PC as their maain platform to play games. There are 36.51% (N=23; M=9, F=14) choose PC, followed by 22.22% (N=14; M=2, F=12) choose PC and mobile as their favourite platform to play games. For mobile platform alone, only 12.70% (N=4; M=1, F=3) partticipant choose as their main platform.
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40 35 30
Player Skills
3.23809524
Control
3.13580247
Clear Goals
3.3015873
Social Interaction
2.75661376
25 Male(M)
20
10
Female(F)
14
15
Below are the comparison charts of GameFlow criteria based on platforms.
Percentage
9 5
5
3
1
4
3
3.5
0 Console
Mobile
Figure 3: Preferred platform chosen by participant to play games
As stated in the diagram in Figure 3, PC are the most chosen gaming platform among the participants (N=23; 36.51%). For mobile device, only 4 of them (6.35%) preferred as their gaming platform. Mean for every question item are calculated and grouped accordingly in their respected criteria based on average mean of every criteria. The data are represented in Table II and Table III. It is clearly seen from the diagram that there are some differences between the mobile and non-mobile platforms where non-mobile is slightly higher in mean average.
Mean Average
3 PC
2.5 2 1.5 1
Immersion Challenge Player Skills Control Clear Goals Social Interaction
0.5 0
GameFlow Criteria (Non-Mobile)
Figure 7: Average mean for non-mobile platform TABLE II: Average mean for Non –Mobile Platform (n=63)
Mean Average
4
Immersion
2.868480726
3.5
Challenge
3.549206349
Player Skills
3.428571429
Control
3.430335097
Clear Goals
3.603174603
Social Interaction
3.428571429
3 Mean Average
Criteria
2.5 2 1.5 1
TABLE III: Average mean for Mobile Platform (n=63)
Criteria
Mean Average
Immersion
2.74376417
Challenge
3.38412698
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0.5 0
Immersion Challenge Player Skills Control Clear Goals Social Interaction GameFlow Criteria (Mobile) Figure 8: Average mean for mobile platform
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The data shows some variation for GameFlow criteria for non-mobile platform as shown in Figure 7. The average mean of GameFlow criteria for “Challenge” is 3.54 and “Clear goals” is 3.60, seems to be higher as compared to the other criteria which is Immersion (mean 2.86), Player skills (mean 3.42), Control (mean 3.43), and Social interaction (mean 3.42).
platform such as control, graphic quality, frame per second (fps) and processing capability. In term of control, non-mobile platform such as PC and console could be enhanced by using controllers such as vibration joystick and seat, 3D glasses, and steering wheels. In term of graphic and processing power, nonmobile platform is much higher as compared to mobile platform.
While as can be seen from Figure 8, the mobile platform, the mean averages are slightly lower as compared to non-mobile platform for most of criteria. The score for all criteria are: Immersion (mean 2.74), Challenge (mean 3.38), Player Skills (3.23), Control (3.13), Clear Goals (3.30), and Social Interaction (2.75). We found that the challenge criteria for Mobile platform score lower than non-mobile platform. The reason might because of the limitation of features provided by mobile
There is some limitation in our studies in term of number of respondent. The result would be more accurate if the numbers of respondents are higher. We face some difficulties as some of the respondents are not completing the survey form as they just quit from participating. We do hope to get more samples in our future experiment.
IV. CONCLUSION In conclusion, different gaming platform does give different experiences for user while interacting with an application. By understanding the variation of user experience towards multiplatform gaming devices will provide us more guidelines in gaming system interface development. Future work of this paper will involve a controlled lab experiment with a number of users. Participant will be given some tasks to play predefined game application. After playing games, participant will asked to complete a survey question based on their experiences in the experiment session. Blood pressure device also will be considered to be used to measure their blood circulation and anxiety while playing the games. This data will be used to relate the flow level while interacting and playing games.
REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]
ACKNOWLEDGEMENT This research was supported by university research grant (UKM-TT-03-FRGS0135-2010).
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[9] [10]
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