Ku et al.
Gaming Behavior and Academic Performance
A Study of the Influence of Gaming Behavior
on Academic Performance of IT College
Students
Completed Research Paper
Chih-Hao Ku
Lawrence Technological University
Middle Georgia State College
[email protected]
Myungjae Kwak
Middle Georgia State College
[email protected]
Kirill M. Yurov
Middle Georgia State College
[email protected]
Yuliya V. Yurova
Nova Southeastern University
[email protected]
Abstract
Video-game playing is popular among college students. Cognitive and negative consequences have been
studied frequently. However, little is known about the influence of gaming behavior on IT college
students’ academic performance. An increasing number of college students take online courses, use social
network websites for social interactions, and play video games online. To analyze the relationship between
college students’ gaming behavior and their academic performance, a research model is proposed and a
survey study is conducted. The study result of a multiple regression analysis shows that self-control
capability, social interaction using face-to-face or phone communications, and playing video games using
a personal computer make statistically significant contributions to the IT college students’ academic
performance measured by GPA.
Keywords
Gaming behavior, self-control, gaming genre, academic performance
Introduction
More than half of the American population and a billion people worldwide are playing video games and
the number is increasing because of the fast growing portable devices, e.g., Android-powered and iOS
devices, and social networking websites, e.g., Facebook1,2 (Liu et al. 2013). People play video games for a
number of reasons, e.g., stress relief, challenge and competition, relaxation, enjoyment, social interaction,
and even mentally escaping from the real world (Sherry et al. 2006).
The academic performance and cognitive consequences of playing video games have been an ongoing
debate in both the educational environment and the public. While it is still arguable whether playing video
games actually affects students’ academic performance, recent studies of many educators have considered
digital games as an educational tool to foster students’ learning interests and motivation (Choi et al. 2013;
Jennifer Henderlong Corpus 2009; Van Eck 2006) and deliver different levels of knowledge more
effectively. Studies have shown that motivation is a crucial catalyst for learning success and correlated
with academic achievement (Jennifer Henderlong Corpus 2009; Jurisevic et al. 2008).
(December 2012) eMarketer Gaming for Marketers: Let the Mobile Games Begin by Paul Verna,
http://www.slideshare.net/AleixRisco/emarketer-gaming-for-marketers-let-the-mobile-games-begin#
2 (Aprial 2013) Social Gaming Industry – Statistics & Trends [Infographic], http://www.go-gulf.com/blog/social-gaming-industry/.
1
Twentieth Americas Conference on Information Systems, Savannah, 2014
1
Ku et al.
IS in Education, IS Curriculum, Education and Teaching Cases
The purpose of this study is to analyze the relationship between college students’ gaming behavior and
their academic performance. To serve the purpose, we derived a research model based on literature
review and tested the model by using a survey study. We report here on a survey study conducted to
investigate the influence of gaming behavior on the academic performance of IT college students. A
conceptual model was proposed to examine whether six identified gaming behavioral variables, including
self-control capability, social interaction, gaming platform, gaming frequency, gaming genres, and
motivation, influence on college student’s academic performance.
The analysis results show that self-control, social interaction, and gaming platform make statistically
significant contributions to the academic performance of IT college students. The authors believe that the
results of this study could be used to enhance the classroom experience and to develop educational
gaming software.
Literature Review
Gaming frequency, time, and types of gamer
The gaming frequency and the amount of time spent on playing video games have been studied for years
frequently in association with gaming addiction (Daniel Luke King 2012; Ko et al. 2009), psychological
constructs (e.g., self-concept clarity, self-control, and flow) (Khang et al. 2013; Lee et al. 2012), negative
consequences (e.g., missing school work) (Hellström et al. 2012), and even academic performance or
learning outcome (Furió et al. 2013; Ip et al. 2008).
Ip et al. (2008) analyzed the relationships between gaming frequency and academic performance among
713 students. The study found that frequent gamers, who spend more than 2 hours per day playing video
games, performed less well than infrequent gamers. Hellström et al. (2012) examined the relationship
between gaming time, motives to play, and negative consequences because of playing Massively
multiplayer online role-playing games (MMORPGs). They recruited 7,757 Swedish adolescents and had
them completed a questionnaire and found that time spent on gaming was related to negative
consequences, e.g., “less sleep due to gaming.” Ventura et al. (2012) constructed an online survey with
252 undergraduate students and a positive indication was found between video gameplay and academic
performance. That is, students who spent 11-50 hours playing video games had significantly higher GPAs
than students who spent 0-10 hours playing video games.
Types of gamers are associated with the frequency and amount of time a player has spent on playing video
games. However, the segmentation between types of gamers has not been significantly, rigorously studied.
Two types of gamers, casual and hardcore gamers, have been growing rapidly in recent years. Kuittinen et
al. (2007) discussed the characteristics and differences between casual and hardcore gamers. For
example, hardcore gamers play extremely competitive games and require a much higher degree of
involvement than casual gamers.
Gaming genre and platform
A game genre, a type of games, is classified based on how gamers interact with a game. For example, an
action game, e.g., first-person shooters, usually involves physical challenges, which requires gamers to
react to the game scenario swiftly to achieve goals or to overcome obstacles. The first book dedicated to
computer and game design is the Art of Computer Game Design by Chris Crawford (Crawford 1984). He
recognizes gaming genres change quickly so a complete taxonomy of gaming genre is difficult to develop.
Ventura et al. (2012) investigated the relationship between video gameplay, game genre preference,
personality, and GPA with 319 university students. Both positive and negative relations were found
between video game genre preference and academic performance. For example, two significant negative
correlations to GPA are social media and shooter types of game. In addition, Ip et al. (2008) found that
the number of genres played is associated with academic performance in examinations. That is, gamers
who play four or more game genres generally perform less well in examinations.
The electronic devices or systems used to play video games are called video game platforms. The wellknown gaming platforms include personal computers, consoles, e.g., Nintendo’s Wii, Sony’s PlayStation,
and Microsoft’s Xbox, and even mobile devices, e.g., Android-based and iOS devices. Appel (2012)
conducted a study to examine the association between adolescents’ computer and Internet activities and
2
Twentieth Americas Conference on Information Systems, Savannah, 2014
Ku et al.
Gaming Behavior and Academic Performance
computer literacy, knowledge and skills to complete tasks with a computer technology. Two hundred
participants were recruited at secondary schools in Austria and hierarchical regression analyses were
conducted. The study found that an increase in video game playing on a personal computer was associated
with higher scores on computer knowledge.
Self-control and flow
Self-control, sometimes called self-regulation, is the ability to control emotions, anxiety, and behavior to
gain possible rewards or avoid punishment (Timpano et al. 2013). LaRose et al. (2003) describe deficient
self-regulation as diminishing consciousness of self-control because of lacking awareness and attention to
behaviors. Khang et al. (2013) found that self-control affected users’ flow and addiction in mobile phones,
Internet, and even video games. A detailed distinction of those psychological factors is beyond the scope
of this study; however, it is worth including the factors into our survey questions.
Psychological factors, e.g., flow and self-control have been frequently studied in gaming addiction and
gaming behaviors. Flow, proposed by Csikszentmihalyi (1991) which has been studied over 30 years, is the
mental state of immersion or concentration in an activity. Playing games can give rise to a state of flow
which led to the loss of self-consciousness and sense of time (Csíkszentmihályi 1975; Liu et al. 2013). The
association between flow and video-game playing has been studied in nascent studies with different
findings. For example, it was found that the flow was not related to the amount of time spent on playing
video games (Lee et al. 2007) and to online game addiction (Wan et al. 2006). However, Lee et al. (2012)
conducted a survey with a sample of 330 students drawn from a university in the northwestern in the U.S.
and found that instances of the flow experience were associated to the amount of time spent on gaming.
Model Development and Research Questions
Model Development
Referring to previous related works, the authors developed the research questions of this study along with
a conceptual research model. Since a variety of factors could affect students’ academic or learning
outcome, it is not wise to pinpoint a single factor that can influence on students’ academic performance.
Previous studies showed that various factors shown in Table 1 including gaming frequency, gaming genre
and platform, self-control, social media, personal characteristics, and even motivation to play video games
can be positively or negatively correlated to academic performance and learning outcome. Therefore, our
research model was developed based upon aforementioned previous researches.
Construct
Gaming time and
frequency (Turner et al.
2012; Ventura et al. 2012)
Gamer type (Kuittinen et
al. 2007)
Description
•
•
•
Items
The factors, e.g., video
gameplay, genre
preference, personality,
total and average time
spent on playing video
games, and GPA were
investigated.
•
Causal gamers refer to
people who play games
occasionally for fun.
Hardcore gamers refer to
people who spend
significant time on playing
video games.
•
•
•
On average, how many hours a
week do you play video games?
How many different video games
do you typically play in a year?
I enjoy playing the following
types of video games.
Casual (occasional) gamer,
hardcore (expert) gamer
Twentieth Americas Conference on Information Systems, Savannah, 2014
3
Ku et al.
IS in Education, IS Curriculum, Education and Teaching Cases
Construct
Game genre (Ventura et al.
2012) and platform
(Connolly et al. 2012)
Description
•
•
Self-control (Khang et al.
2013; Tangney et al. 2004)
Ignore school work (Daniel
Luke King 2012; Hellström
et al. 2012; Turner et al.
2012)
•
•
The factors, e.g., game
genre, platform, and
learning outcome were
reviewed.
Positive impacts and
outcomes are associated
with playing video games.
The relationship between
self-control, grade, and
Internet and gaming
addiction have been
studied.
The factors, e.g., frequency
and hours of playing video
games, and modified
version of the Problem
Video Game Playing (PVP)
have been investigated.
Items
•
•
•
•
•
•
•
•
Social media for
communications
(Creighton et al. 2013;
Haagsma et al. 2013)
•
The relationship between
using social media, online
social interaction, selfregulation and academic
performance has been
studied.
•
•
•
Personal information
(Elliott et al. 2012)
•
The relationship between
personal information such
as age and gender and
video game playing has
been studied.
•
Motivations (Paavilainen
et al. 2013; Peng et al.
2012)
•
The relationship between
motivation and game
playing has been studied.
•
Game genre: action, puzzle, roleplaying, strategy, sport, and
adventure games
Platforms: PC, video console,
and online games
Pleasure and fun sometimes
keep me from getting work done
Sometimes I can’t stop myself
from doing something, even if I
know it is wrong
I am always on time
Did you spend an increasing
amount of time playing video
games?
Did you skip school or work, or
lie or steal, or argue with
someone so that you could play
video games?
Did you ignore homework, or go
to bed late, or spend less time
with family and friends because
of your video game playing?
The use of text messaging, email,
social networking sites, cell
phone communication, and
online gaming
I prefer online social interaction
over face-to-face communication
I prefer communicating with
people online rather than faceto-face
Age, gender, race, education,
employment, household income,
etc.
Fun, frustrations, sociability,
competition, escape, etc.
Table 1. The constructs related to academic performance and learning outcome
The main construct listed in Table 1 is comprised of factors in our research model. For example, the ‘social
media’ factor refers to the use of ‘email’, ‘cellphone’, ‘social websites’, and ‘face-to-face’ for social
interactions with college students. Therefore, the six factors described in Table 1 were selected to develop
the conceptual model (see Figure 1).
4
Twentieth Americas Conference on Information Systems, Savannah, 2014
Ku et al.
Gaming Behavior and Academic Performance
Figure 1. The conceptual study model
Research questions
Our interests are to investigate the influence of gamer’s behavior, including gaming frequency/time,
gaming genre/platform, social media, motivation, and self-control capability, on the academic
performance of college students. This research intention led to our major research question:
•
Question: Do various aspects of gamer’s behavior, including gaming frequency/time, gaming
genre/platform, social media, motivation, and self-control capability significantly influence on
college students’ academic performance?
Research Methods and Study Design
Participants
In Fall 2013, three instructors in the School of Information Technology (IT) at a four-year college in the
southern US invited students of their courses to participate in this study. The 221 students from the
thirteen courses, of which seven courses are online, taught by the three instructors were invited to the
study. Students who participate in the study received 10 or 20 bonus points dependent upon the grading
scale of each course. An alternative assignment with bonus points was given to students who did not want
to participate in the study. Participants’ age ranged from 18 to 50. The majority of participants (70%) were
under 33 years old. The academic performance of survey respondents measured by using a 4.0 scale GPA
ranged from 1.82 to 4.0 with the average of 3.033. 78% of the participants were male, 81.20% were full
time students, and 65% had a part or full time job.
Data Collection and Process
An online, anonymous survey and convenient sampling method was used for data collection.
LimeSurvey3, an open source, web-based survey application, was used to conduct the survey and collect
responses. The gaming frequency/time, types of gamers, types of games, social behavior, self-control
capability, and other relevant information were collected through 30 survey questions. Students could opt
out of the survey any time if they find some questions are too sensitive or inadequate. Students’ academic
performance indicators such as GPA were collected from the school’s information system. Any
information related to students’ privacy, including names and GPA was protected and especially students’
names were encoded using a unique numeric code.
A pilot survey was conducted with three undergraduate students. Based on students’ feedback, sensitive
questions were revised. Ten students opted out while taking the survey. The total number of usable
responses was 117 after removing 5 incomplete responses.
3
LimeSurvey, http://www.limesurvey.org/en/.
Twentieth Americas Conference on Information Systems, Savannah, 2014
5
Ku et al.
IS in Education, IS Curriculum, Education and Teaching Cases
Measures and Data Analyses
The survey was conducted by using 30 questions in 4 categories: 1) gaming experience, 2) social and
academic experience, 3) personal information, and 4) open questions. The survey questions were
classified into 6 main areas for the research model (see Appendix A): 1) gaming time/frequency, 2)
gaming genre/platform, 3) self-control capability, 4) social media usage, 5) computer and Internet usage,
6) motivation and bio. Each main area is comprised of subareas and survey questions. For example, selfcontrol includes self-control in academic work and in personal life with the questions:
•
•
Self-control in academic work: Have you missed any deadlines of assignments or exams
because of playing video games?
Self-control in personal life: Have you forgotten things to do because of playing video games?
The survey questions are designed to represent the 6 constructs specified in Table 1. Students’ academic
performance was measured by using cumulative GPA retrieved from school’s student information system.
While each survey question was selected and developed based on previous research studies (Connolly et
al. 2012; Ip et al. 2008; Turner et al. 2012; Ventura et al. 2012), we only selected and revised questions
and items relevant to gaming and academic performance in this study. For other factors, gaming
frequency (Ip et al. 2008), face-to-face vs. online communication (Haagsma et al. 2013; Lepp et al. 2014)
in social media, the number of game genres (Ip et al. 2008), computer gaming on a PC (Appel 2012) in
gaming platform, self-control ability (Tangney et al. 2004), and playing video games to escape from real
world (Hellström et al. 2012) have been studied frequently together with academic performance or
learning outcome and also were used in our model.
The Statistical Package for the Social Sciences (SPSS) was used for statistical analysis. While the
dependent variable, GPA, is continuous data, the independent variables are discrete and categorical data.
Therefore, a multiple regression was conducted to find out how strongly gaming related independent
variables are correlated to the dependent variable, which is academic performance. The analysis also can
show the relative contribution of each variable. Additionally, Pearson’s correlations were examined to
ensure the correlations among independent variables are not too high.
To prepare data for analysis, the categorical variables with more than 3 levels, such as game platforms,
gaming genres, and social communication, were encoded into separate, dichotomous variables with
dummy coding. The self-control ability is comprised of 5 dichotomous questions because some students
were sensitive to the frequency of missing classes and homework based on the feedback from our pilot
study. Therefore, we transformed 5 dichotomous questions into a single predictor with the scale 0 (low
self-control ability) – 5 (high self-control ability).
Analysis Results
First, the results of multiple regression show that a standardized residual was between -2.5 and +2 and
there were no outliers. The correlation between all independent variables is less than .7, shown in Table 2.
Therefore, all variables were retained in the research model. The coefficient of each independent variable
and R2 value (.37) are shown in Table 3. From the R2, it can be argued that our model explains 37% of the
variance in GPA. The largest beta coefficient is .27, which is for gaming platform, followed by self-control
(.26), social interaction using more face-to-face communication (.18), motivation (.10), gaming genres
(.05), and finally gaming frequency (-.02). Among the 6 predictors, three variables: self-control, social
interaction, and gaming platform make a statistically significant contribution to college students’
academic performance.
6
Twentieth Americas Conference on Information Systems, Savannah, 2014
Ku et al.
Gaming Behavior and Academic Performance
GPA
GPA
Selfcontrol
Social
interaction
Motivation
Gaming
frequency
Gaming
genres
Gaming
platform
1
Self-control
Social interaction
(more face-to-face
communication)
Motivation
(Stress relief, etc.)
.17
1
.14
.01
1
.08
.04
.03
1
Gaming frequency
-.01
.44
.11
.09
1
Gaming genres
.03
.36
.07
.09
.55
1
Gaming platform
.19
.26
.11
.02
.39
.36
1
Table 2. Correlations of variables in the study (n = 117)
Unstandardized
Variables
Coefficient
(Constant)
3.057
Self-control
Social interaction
Standardized
Standard Error
Coefficient
.07
.03
.26*
.22
.11
.18*
Motivation
.12
.12
.10
Gaming frequency
-.01
.03
-.02
Gaming genres
.02
.03
.05
Gaming platform
.28
.10
.27*
R2
0.37
* p <.05 (2-tailed)
Table 3. Regression model estimates (n = 117)
Discussion
The analysis results show that while self-control, social interaction (i.e., more face-to-face interaction than
online interaction), and gaming platform make statistically significant contributions to college students’
academic performance, other three variables including motivation, gaming frequency, and gaming genres
are not playing any statistically significant role with regard to academic performance. The results of our
analysis concerning the self-control and gaming platform variable confirm the findings of prior research,
which indicate that the more control students have over their academic work, the higher GPA they can
achieve. The analysis result of self-control variable confirms previous research in that the more control
students have on their academic work, the higher GPA they can achieve. However, the test results of other
two variables including social interaction and gaming platform are somewhat interesting. The similar
result has been found by Appel (2012); an increase in video game playing on a personal computer was
related to higher scores on computer knowledge. The results indicate that students who prefer face-toface or phone communication methods obtain relatively higher GPA than those who like to use online or
text communication methods and students who play games using multiple gaming platforms achieve a
higher GPA than others.
While the aforementioned findings require more in-depth analysis, the result related to social interaction
can implicate that college students who achieve high academic performance use less online social media
such as Facebook and Twitter. It can be justified because online social media usually requires a
considerable amount of time and also are less convenient for in-depth communication with colleagues
Twentieth Americas Conference on Information Systems, Savannah, 2014
7
Ku et al.
IS in Education, IS Curriculum, Education and Teaching Cases
than face-to-face or phone communication methods. Also the study result related to the gaming platform
entails that tech-savvy IT students achieve higher academic performance in IT courses than others.
Conclusion and Limitations
In this study, we developed a research model based on prior research to investigate the influence of
gaming behavior on the academic performance of IT college students. The model was tested by using a
survey study and the results showed that among six independent variables, self-control, social interaction,
and gaming platform have emerged to have statistically significant relationships with academic
performance.
There are several limitations evident in this study. First, the research model was tested using a survey,
which is a self-report that may include biased answers. Second, the study surveyed only IT students and,
therefore, it may not be appropriate to generalize the results to the entire population of college students.
Third, due to time constraints, only one model was reported and tested. To fine-tune our current model,
more alternative models and factors need to be tested.
Future Research
This study investigates the gaming behavior and factors that may influence students’ academic
performance. Potential venues for follow-up research could include developing and testing alternative
models with a focus on the effects of moderation and mediation. In addition, we plan to investigate into
whether other factors, e.g., students’ prior knowledge, learning efficiency, and learning approaches will
affect students’ academic performance. Finally, we are interested in comparing non-educational and
educational games in influencing students’ learning and academic performance because our final goal is
to design and develop educationally effective game software for K-12 and college students.
References
Appel, M. 2012. "Are Heavy Users of Computer Games and Social Media More Computer Literate?," Computers &
Education (59:4), pp 1339-1349.
Choi, B., Huang, J., Jeffrey, A., and Baek, Y. 2013. "Development of a Scale for Fantasy State in Digital Games,"
Computers in Human Behavior (29:5), pp 1980-1986.
Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., and Boyle, J. M. 2012. "A Systematic Literature Review
of Empirical Evidence on Computer Games and Serious Games," Computers & Education (59:2), pp 661686.
Crawford, C. 1984. The Art of Computer Game Design, (McGraw-Hill/Osborne Media: Berkeley, California).
Creighton, J. L., Foster, J. W., Klingsmith, L., and Withey, D. K. 2013. "I Just Look It up: Undergraduate Student
Perception of Social Media Use in Their Academic Success," The Journal of Social Media in Society (2:2).
Csíkszentmihályi, M. 1975. Beyond Boredom and Anxiety, (Jossey-Bass: San Francisco).
Csíkszentmihályi, M. 1991. Flow: The Psychology of Optimal Experience, (HarperPerennial: New York, N.Y.).
Daniel Luke King, P. H. D. 2012. "Clinical Interventions for Technology-Based Problems: Excessive Internet and
Video Game Use," Journal of Cognitive Psychotherapy (26:1) 2012, pp 43-56.
Elliott, L., Golub, A., Ream, G., and Dunlap, E. 2012. "Video Game Genre as a Predictor of Problem Use,"
Cyberpsychology, Behavior and Social Networking (15:3), pp 155-161.
Furió, D., González-Gancedo, S., Juan, M. C., Seguí, I., and Rando, N. 2013. "Evaluation of Learning Outcomes
Using an Educational Iphone Game Vs. Traditional Game," Computers & Education (64) 2013/05//, pp 123.
Haagsma, M. C., Caplan, S. E., Peters, O., and Pieterse, M. E. 2013. "A Cognitive-Behavioral Model of Problematic
Online Gaming in Adolescents Aged 12–22 Years," Computers in Human Behavior (29:1), pp 202-209.
Hellström, C., Nilsson, K. W., Leppert, J., and Åslund, C. 2012. "Influences of Motives to Play and Time Spent
Gaming on the Negative Consequences of Adolescent Online Computer Gaming," Computers in Human
Behavior (28:4), pp 1379-1387.
Ip, B., Jacobs, G., and Watkins, A. 2008. "Gaming Frequency and Academic Performance," Australasian Journal of
Educational Technology (24:4), pp 355-373.
8
Twentieth Americas Conference on Information Systems, Savannah, 2014
Ku et al.
Gaming Behavior and Academic Performance
Jennifer Henderlong Corpus, M. S. M.-G. 2009. "Within-Year Changes in Children’s Intrinsic and Extrinsic
Motivational Orientations: Contextual Predictors and Academic Outcomes," Contemporary Educational
Psychology:2) 2009, pp 154-166.
Jurisevic, M., Glazar, S. A., Pucko, C. R., and Devetak, I. 2008. "Intrinsic Motivation of Pre-Service Primary School
Teachers for Learning Chemistry in Relation to Their Academic Achievement," International Journal of
Science Education (30:1), pp 87-107.
Khang, H., Kim, J. K., and Kim, Y. 2013. "Self-Traits and Motivations as Antecedents of Digital Media Flow and
Addiction: The Internet, Mobile Phones, and Video Games," Computers in Human Behavior (29:6), pp
2416-2424.
Ko, C.-H., Liu, G.-C., Hsiao, S., Yen, J.-Y., Yang, M.-J., Lin, W.-C., Yen, C.-F., and Chen, C.-S. 2009. "Brain
Activities Associated with Gaming Urge of Online Gaming Addiction," Journal of Psychiatric Research
(43:7) 2009/04//, pp 739-747.
Kovačević, I., Minović, M., Milovanović, M., de Pablos, P. O., and Starčević, D. 2013. "Motivational Aspects of
Different Learning Contexts: “My Mom Won’t Let Me Play This Game…”," Computers in Human
Behavior (29:2), pp 354-363.
Kuittinen, J., Kultima, A., Niemelä, J., and Paavilainen, J. Year. "Casual Games Discussion," ACM2007, pp. 105112.
LaRose, R., Lin, C. A., and Eastin, M. S. 2003. "Unregulated Internet Usage: Addiction, Habit, or Deficient SelfRegulation?," Media Psychology (5:3) 2003, pp 225-253.
Lee, C., Aiken, K. D., and Hung, H. C. 2012. "Effects of College Students’ Video-Gaming Behavior on SelfConcept Clarity and Flow," Social Behavior and Personality (40:4), pp 673-680.
Lee, D., and LaRose, R. 2007. "A Socio-Cognitive Model of Video Game Usage," Journal of Broadcasting &
Electronic Media (51:4), pp 632-650.
Lepp, A., Barkley, J. E., and Karpinski, A. C. 2014. "The Relationship Between Cell Phone Use, Academic
Performance, Anxiety, and Satisfaction with Life in College Students," Computers in Human Behavior
(31), pp 343-350.
Linehan, C., Kirman, B., Lawson, S., and Chan, G. Year. "Practical, Appropriate, Empirically-validated Guidelines
for Designing Educational Games," CHI '11 Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems ACM, Vancouver, BC, 2011, pp. 1979-1988.
Liu, D., Li, X., and Santhanam, R. 2013. "Digital Games and Beyond: What Happens when Players Compete?," MIS
Q. (37:1), pp 111-124.
Paavilainen, J., Hamari, J., Stenros, J., and Kinnunen, J. 2013. "Social Network Games: Players’ Perspectives,"
Simulation & Gaming (44:6), pp 794-821.
Peng, W., and Hsieh, G. 2012. "The Influence of Competition, Cooperation, and Player Relationship in a Motor
Performance Centered Computer Game," Comput. Hum. Behav. (28:6) 2012/11//, pp 2100-2106.
Sherry, J. L., Lucas, K., Greenberg, B. S., and Lachlan, K. Year. "Video Game Uses and Gratifications as Predictors
of Use and Game Preference," In P. Vorderer & J. Bryant (Eds.), Playing Computer Games: Motives,
Responses, and Cnsequences 2006, pp. 213-224.
Tangney, J. P., Baumeister, R. F., and Boone, A. L. 2004. "High Self-Control Predicts Good Adjustment, Less
Pathology, Better Grades, and Interpersonal Success," Journal of Personality (72:2), pp 271-324.
Timpano, K. R., and Schmidt, N. B. 2013. "The Relationship Between Self-Control Deficits and Hoarding: A
Multimethod Investigation Across Three Samples," Journal of abnormal psychology (122:1), pp 13-25.
Turner, N. E., Paglia-Boak, A., Ballon, B., Cheung, J. T. W., Adlaf, E. M., Henderson, J., Chan, V., Rehm, J.,
Hamilton, H., and Mann, R. E. 2012. "Prevalence of Problematic Video Gaming among Ontario
Adolescents," International Journal of Mental Health and Addiction (10:6), pp 877-889.
Van Eck, R. 2006. "Digital Game-Based Learning: It's Not Just the Digital Natives Who Are Restless," EDUCAUSE
Review (41:2).
Ventura, M., Shute, V., and Kim, Y. J. 2012. "Video Gameplay, Personality and Academic Performance,"
Computers & Education (58:4), pp 1260-1266.
Wan, C.-S., and Chiou, W.-B. 2006. "Psychological Motives and Online Games Addiction: A Test of Flow Theory
and Humanistic Needs Theory for Taiwanese Adolescents," Cyberpsychology & Behavior (9:3), pp 317324.
Twentieth Americas Conference on Information Systems, Savannah, 2014
9
Ku et al.
IS in Education, IS Curriculum, Education and Teaching Cases
Appendix A
Areas
Gaming time
/frequency
Gaming
platform and
genre
Subareas
Questions
Gaming frequency and
the amount of time
•
How often have you played video game weekly?
•
On average, how many hours per week you have spent
time playing video games?
•
Compared to three years ago, I play video games: more
frequently, less frequently, little change, etc.
Type of player
•
I consider myself as: A non-, novice, occasional, expert
(hardcore) video game player.
Game platform & playing
frequency
•
Types of game platform you have played: PC-based,
Console (Xbox, Wii, etc.), Arcade, Mobile, etc.
•
Which game platform you have played most
frequently? PC-based, Console (Xbox, Wii, etc.),
Arcade, Mobile, etc.
•
You have been playing: Strategy, action, puzzle, roleplaying, sports, massively multiplayer online games,
etc.
•
Which game you have played most frequently?
strategy, action, puzzle, role-playing, sports, massively
multiplayer online games, etc.
•
Please list names of games you have played.
•
Have you played video games during a class?
•
Have you played video games during class breaks?
•
Have you missed any deadlines of assignments or
exams because of playing video games?
•
Have you been late or missed any classes because of
playing video games?
•
Have you forgotten things to do because of playing
video games?
•
Have you skipped your dinner or lunch because of
playing video games?
•
Have you forgotten the time because of playing video
games?
•
How often do you hang out with friends per week?
•
I prefer to communicate with people via: Phone, email.
Social network websites (Facebook, Twitter, etc.), faceto-face, etc.
•
I communicate with people most frequently by: Phone,
email. Social network websites (Facebook, Twitter,
etc.), face-to-face, etc.
•
Type of course I have been taken:
Game genres & playing
frequency
Self-control
Self-control in academic
work
Self-control in personal
life
Social media
Computer and
Internet
10
Social interaction &
frequency
Type of courses (online or
regular)
__ % Online
Twentieth Americas Conference on Information Systems, Savannah, 2014
Ku et al.
Gaming Behavior and Academic Performance
__ % in-Class
__ % Hybrid
Equipment
Personal
information
Motivation
Age, gender, race,
employment status,
household income, and
student status
Motivation to play
videogames
•
Do you have high-speed Internet at home?
•
Do you own personal computer(s) at home?
•
Age range
•
Gender
•
Racial background
•
Student status: full/part time
•
Employment status: full-, part-time, etc.
•
What is your household income?
•
I would like to spend more time playing video games if
I have: a higher income, more friends playing with me,
etc.
•
Please specify your reasons to play games e.g., kill
time, too much pressure, social life (most friends play
it), or escape real life.
Twentieth Americas Conference on Information Systems, Savannah, 2014
11