Evaluating Game Enjoyment using Gaze Data
and Self Report
Abstract
In both game-based and flow-based research, there has been a lot of interest in developing an
appropriate model for evaluating user enjoyment of video game play. The goal of this study
was to find out which game the children preferred, as well as whether or not there was a link
between the length of time spent playing the game and the children's rating and enjoyment of
the game (or vice versa). The goal of this research is also to discover that youngsters spent
less time overall playing game C than they did playing game A. Each participant played an
interactive video game and subsequently evaluated their gaming experiences using the
updated Flow scale, which included the dimensions of time, smileyometer, and favourite
game. This research had a total of 28 participants. It was possible to get an average of the
data, game comparisons, and a frequency table. It has also been established which games are
popular with both boys and girls in the younger age group.
Introduction
To provide students with up-to-date training in many skill sets that will be important in the
future, science must emphasise the use of abundant data. Researcher states that educators and
game designers may and should use these gaming skills seen in young people into
educational scientific games. When analysing the impact of gaming on education, researchers
use both quantitative and qualitative data-gathering methods. The flow-based and gamebased research of video game player enjoyment has grown throughout the years. This study
sought to discover which video games children preferred and to investigate whether there was
a correlation between amount of video game play and children's evaluation and enjoyment.
With the use of eye-tracking data from an educational game, it is investigated the visualattention patterns of poor and high performers and how their demographics influence these
patterns. Low and high performers had distinct visual-attention patterns. (Bowers, Bullinger,
Weger, & Norris, 2014)Also, self-reported scientific, gaming, and navigational competence
associated with many gaze metric characteristics.
Background Literature
From the research paper by Janet C Read and Stuart MacFarlane,2006 “Using the fun toolkit
and other survey methods to gather opinions in child computer interaction”, the researcher
has noted that it goes on to detail four recognised issues and how they may affect research in
child computer interaction. It goes on to examine the use of surveys to collect child computer
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interaction data, and the Fun Toolkit is mentioned in the results. Three new research projects
investigate the usability and effectiveness of the Fun Toolkit, and these findings lead to many
suggestions for the toolkit's future uses. (Janet & Stuart, 2006)
Narrow attention visualisation patterns may differentiate pupils from various performance
groups. The relatively new study on gaze measurements for learning-prediction algorithms
adds to the complexity. (Lu, He, Urban, & Griffin, 2021)
From the analysis of “Validation of EGame Flow: A Self-Report Scale for Measuring User
Experience in Video Game Play”, by Shu-Hui Chen2018, the baseball game was designed to
be fully participatory for the subjects. The sensor technologies enabled the individuals to
engage in a virtual game universe. Unlike other multiplayer video games, player motions in
the digital realm mirrored physical actions. The stations for pitching and hitting were set up
such that the subjects could see each other while playing. In this way, the two players felt
more connected outside of the virtual gaming environment. Using a non-commercial game
guaranteed that the respondents had no previous familiarity with the game or platform.
(Chen, Wann-Yih, & Dennison, 2018)
In the research, by Zhan Wu, Video Games: A critical Analysis, it was observed that it is
necessary to look critically about computer games in our community, not simply reject them
as frivolous things that people may get hooked to. (Wu, 2013)
Research Method
A quantitative research is conducted. There were 3 game kinds. Game A, B, and C.
Participants were told to take turns playing. Then they were divided into and given a short
explanation of the game. After they play the game, their responses were recorded. The
responses are obtained from the 28 children who played the game. Among these 28 children,
14 were girls and 14 were boys. They were divided into 4 groups.
Results
The average of data is calculated and with the help of it, the graphs below are plotted.
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AverageTime in
Seconds
Smileyometer
Reading
110
20000
10000
100
AverageTi
me in…
0
Smileyometer
Reading
90
Game Game Game
A
B
C
Game Game Game
A
B
C
Graph 1: Average Time in Seconds
Graph 2: Smileyometer Reading
Kids Interest in Games
60
50
40
Kids Interest in Games
30
20
10
0
Game A
Game B
Game C
Graph 3: Graph Showing Kids Interest
Discussions
From the above graphs, it can be observed that most of the time spent by children was on
Game A. But from the second graph, it can be observed that the Smileyometer reading shows
that the Game B rating is higher. However from the third graph, it can be observed that the
Kids interest was higher in Game B. It was also observed that the boys and girls have
different Preferences.
Frequency of Games
Bin
Frequency Frequency Frequency
of Game
of Game
of Game
A
B
C
4
101
201
301
401
501
601
701
0
1
2
7
15
1
1
0
2
1
18
5
1
0
0
2
15
7
2
1
0
Conclusion
This report adds to the growing body of knowledge on gaming pleasure. A series of statistical
studies demonstrated the game's validity and reliability as a research tool. The researcher
supports children's game-based learning skills. These varied skills are further related to the
intended features of a scientific game setting data obtained from the games. Based on the
aforementioned data, we can conclude that the majority of the time that children are engaged
in is on Game A. Furthermore, it can be seen from the second graph that the Game B rating is
greater than the Game A rating. However, it can be seen from the third graph that interest in
the Kids for Game B was greater. Girls and boys had distinct preferences.
Works Cited
Bowers, A., Bullinger, C., Weger, H., & Norris, A. E. (2014). Quantifying Engagement:
Measuring Player Involvement in Human-Avatar Interactions. Compute Human Behaviour ,
1-11.
Chen, S.-H., Wann-Yih, W., & Dennison, J. (2018). Validation of EGameFlow: A Self-Report
Scale for Measuring User Experience in Video Game Play. Computers in Entertainment , 115.
Janet, & Stuart. (2006). Using the fun toolkit and other survey methods to gather opinions in
child computer interaction. Association for Computing Machinary , 81-88.
Lu, W., He, H., Urban, A., & Griffin, J. (2021). What the Eyes Can Tell: Analyzing Visual
Attention with an Educational Video Game. Association for Computing Machinery , 1-7.
Wu, Z. (2013). Video Games: A Critical Analysis. Augmenting Realities .
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