Liu, E. Z.-F., Lee, C.-Y., & Chen, J.-H. (2013). Developing a New Computer Game Attitude Scale for Taiwanese Early
Adolescents. Educational Technology & Society, 16 (1), 183–193.
Developing a New Computer Game Attitude Scale for Taiwanese Early
Adolescents
Eric Zhi-Feng Liu, Chun-Yi Lee* and Jen-Huang Chen
Graduate Institute of Learning and Instruction, Center of Teacher Education, Research Center for Science and Technology for
Learning, National Central University, Jhongli City, Taoyuan, Taiwan // Tzu Chiang Junior High School, Jhongli City, Taoyuan,
Taiwan // Ruei Fong Elementary School, Hsinchu County, Taiwan // [email protected] // [email protected] //
[email protected]
*
Corresponding author
(Submitted April 05, 2011; Revised August 27, 2011; October 10, 2011)
ABSTRACT
With ever increasing exposure to computer games, gaining an understanding of the attitudes held by young
adolescents toward such activities is crucial; however, few studies have provided scales with which to
accomplish this. This study revisited the Computer Game Attitude Scale developed by Chappell and Taylor in
1997, reworking the overall structure of the instrument, and increasing the number of items to 22. The revised
scale covers five factors: learning, confidence, liking, participation, and leisure, grouped into three subscales of
cognition, affection and behavior. Data gathered from 354 elementary school students has demonstrated the
validity and reliability of the proposed scale for investigating the attitudes of elementary students toward
learning through computer games, providing additional insight into the influence of gender and Internet usage.
Keywords
Computer game, Computer game attitude scale, Taiwanese early adolescents, Early adolescents
Introduction
Playing games is a natural activity for elementary school students (or early adolescents), helping them to understand
the world and widen their experience by exploring the full range of possibilities open to them (Eisner, 1982). The
social skills of children are developed through interaction with their playmates, and their desires and needs are
fulfilled through the games they engage in, enabling them to develop into healthy well-adjusted adults (Montessori,
1917). According to educational psychologist Jean Piaget, human cognitive development from birth to age fourteen,
is divided into four stages: the sensorimotor stage, preoperational stage, concrete operational stage, and formal
operational stage (Dembo, 1994). Sixth graders (approximately 13 years old), are in the formal operational stage of
development, fully capable of the abstract thought required to cognize abstractions represented in a virtual computer
environment. In recent years, computer games have become one of the most popular leisure activities for adolescents
and young adults, providing a space in which to develop critical thinking and engage in active learning. (Griffiths,
2010). Virtual worlds are perceived as a safe environment, in which learners can develop skills, concepts, and
strategies. The limitless potential of computer games in education has been widely recognized and the notion of
developing games with more serious themes has emerged in the fields of education and training (Prensky, 2001).
Many researchers have attempted to incorporate computer games into school curricula as a means to enhance
academic performance (Lee & Chen, 2009), cognitive development (Jenkins, 2002), motivation toward learning (Liu
& Lin, 2009), and attention span and concentration (McFarlane, Sparrowhawk, & Heald, 2002). Computer games
enhance the process of learning by promoting the use of visualization, experimentation, and creativity in play (Betz,
1995). Computer games also challenge students and provide them with feedback, while allowing them to satisfy their
curiosity and earn a sense of achievement (Chiang, Lin, Cheng, & Liu, 2011; Liu & Lin, 2009). Imaginary worlds
provide an environment in which students may fulfill their basic human needs, experiencing the taste of victory, the
joy of success, and the satisfaction that comes from collaboration or competition with others. Satisfying the
instinctual yearnings of participants makes learning more interesting and imbues the process with meaning (Cagiltay,
2007; Gee, 2003; Liu & Lin, 2009).
Based on the viewpoint of learning theory, behaviorist Doob (1947) considered attitude an implicit response, which
is both anticipatory and mediating in reference to patterns of overt responses. In this manner, attitudinal responses do
not differ from other responses and can be gained through conditioning. Social psychologist Allport (1935) defined
attitude as a mental and neural state of readiness, exerting a directive or dynamic influence on the response of
individuals to all objects and situations within which they are situated. For instance, the mental readiness of a runner
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183
anticipating the sound of the starting pistol is an attitude. Thurstone and Chave (1928) defined attitude as a scale of
affection related to one’s opinions, in which particular objectives are favored or rejected. In principle, attitudes are
personal; however, they occasionally fulfill a social function. People growing up in the same culture and society are
influenced by the same cultural traditions, social norms, customary practices, and other factors. It is not surprising
that in such circumstances individuals tend to exhibit similar traits and a high degree of consistency in their attitudes.
Attitudes are learned, and as such, are closely related to one’s experiences in the process of learning. In summary,
attitude can be defined as the outward manifestation of an individual’s evaluation of an entity, based on previous
knowledge and beliefs.
In educational research, the attitude of students towards subject matter is often viewed as an important variable in
predicting performance. It would therefore be unwise to suggest integrating computer games into classrooms without
fully investigating how students view them (Bourgonjon, Valcke, Soetaert, & Schellens, 2010). Reece and Gable
(1982) proposed that computer attitude comprises three components: cognition, affection and behavior. Brown,
Brown, and Baack (1988) also described how computer attitude includes a behavioral component, entailing the
specific actions of an individual toward computers; an affective component, representing the internal feeling of an
individual about computers; and a cognitive component, comprising the beliefs of an individual regarding computers.
Campbell (1987) pointed out that computer attitude is the general impression, opinions, and cognition of computers.
A summary of previous research results has led to the conclusion that computer attitude comprises cognitive,
affective, and behavioral components. The cognitive component represents an individual’s impression and opinion of
computers. The affective component represents an individual’s personal feelings about computers. The behavioral
component represents specific behaviors, such as an inclination to use computers.
Although a number of instruments have been developed to measure attitudes toward computers, far fewer
instruments have been developed for measuring attitudes toward computer games. Bonanno and Kommers (2008)
developed a tool to measure four components of attitude toward gaming: affective components, perceived control,
perceived usefulness, and behavioral components. Their survey, including 21 statements scored on a 5-point Likert
scale, was used with a sample of college students to investigate the influence of gender and gaming competence on
attitudes toward gaming. However, this instrument requires further refinement and validation to ensure reliability and
construct validity. Chappell and Taylor (1997) developed an instrument called the Computer Game Attitude Scale
(CGAS) to evaluate the attitudes of students toward educational computer games. Their study provided evidence
supporting the reliability and factorial validity of the scores of the CGAS and its two subscales: comfort and liking.
However, using principle component factor analysis indicated that both the comfort and liking subscales explained
only 44 % of all variables. At less than 50 %, this scale clearly requires further validation.
Previous studies (Bonanno & Kommers, 2008; Chappell & Taylor, 1997) addressing attitudes toward computer
games have tended to emphasize the affective component. However, the integration of information technology and
computer games provides early adolescents with another avenue for learning (Jonassen, 1996). There are a great
many types of computer games, but not all of them have a practical application in education (Can & Cagiltay, 2006).
For this reason, game playing as well as game based learning should be considered within the context of schooling.
Developing an instrument for measuring attitudes toward computer based activities would enable the expansion of
the discussion beyond the aspect of affection, to include cognition and behavior regarding computer games (Adcock
& Van Eck, 2005). This study built upon the model by Reece and Gable (1982), to develop a New Computer Game
Attitude Scale (NCGAS) for elementary school students, by revising the CGAS and adding new items based on the
following three subscales: (1) a cognition subscale representing the impressions and opinions of individuals
regarding the use of computer games; (2) an affection subscale representing the feelings of individuals regarding
computer games; and (3) a behavior subscale representing specific behaviors, such as an inclination to use
computers.
Previous studies have suggested that separating the experience students have with computer games from gender
issues is nearly impossible. Bonanno and Kommers (2008) described how males tend not only to play games more
often, but to play different types of games. Males also tend to exhibit significantly different attitudes toward the use
of computer games (Bonanno & Kommers, 2008). In contrast, Karakus, Inal, and Cagiltay (2008) found no
significant gender differences in whether games are viewed as useful in education. Gender differences may already
be changing, and with the ongoing expansion of online gaming and considerable increases in the number of female
players, computer games may be equally effective and motivating for both male and female students (Papastergiou,
2009). Therefore, we also analyzed variables including gender and experience using the internet.
184
Method
Participants
The Computer Game Attitude Scale was developed for sixth and seventh graders; therefore, we selected 354 sixth
graders as the participants in this study. The students were from five elementary schools in Taipei County, Hsinchu
County, and Taichung City. Among the respondents, 168 were female and 166 male. Although the sample size of
these young adolescents was relatively small, they were representative of various demographic and academic
backgrounds.
Materials
To develop the New Computer Game Attitude Scale (NCGAS), items were adapted from Chappell and Taylor’s
(1997) Computer Game Attitude Scale, with additional items contributed by the authors of this study. Chappell and
Taylor (1997) proposed the following two subscales for computer game attitudes: comfort and liking, including a
total of 20 items. The self-writing section listed 60 items, comprising cognition (including learning and confidence),
affection (including liking), and behavior (including participation, leisure and negative behavior) toward computer
games. All items were tested for face validity and content validity by an expert in the design of questionnaires and
six highly experienced elementary school teachers. In the end, 60 items were included in the New Computer Game
Attitude Scale. The 60 items were presented to 354 elementary students. Many elementary students select a neutral
response without thinking; therefore, a four-point Likert scale in which 4 = strongly agree, 3 = agree, 2 = disagree,
and 1 = strongly disagree was used to avoid this situation. Items demonstrating poor reliability or validity were
deleted, leaving only 22 items in the New Computer Game Attitude Scale. The final questionnaire contained three
subscales and five factors, and the cumulative explained variance of each exceeded 50 %. Items were retained only
when they exceeded +0.40 or were less than -0.40 for relevant factors and less than the absolute value of ±0.40 for
non-relevant factors. The three subscales were cognition (learning and confidence), affection (liking), and behavior
(participation and leisure). The five factors were learning (coded as LRN), confidence (CON), liking (LIKE),
participation (PAR) and leisure (LEI) (refer to Table 1). A detailed description of the five factors is presented below:
1. Learning: used to measure the perception of students as per the positive impact of the computer game on
learning.
2. Confidence: used to measure the confidence of students to independently control the computer game.
3. Liking: used to measure how much the students liked using the computer game.
4. Participation: used to measure the degree to which students actual participated in the activities related to the
computer game.
5. Leisure: used to measure the degree to which the computer game was incorporated in the leisure time of the
students and their perceptions of the game as a leisure activity.
Subscale
Cognition
Affection
Table 1. The verified items of the Computer Game Attitude Scale
Factor
Item
LRN
1. I was able to install other software on my computer after installing computer
games.
2. I learned how to look up software instructions by playing computer games.
3. I would be more willing to take classes if computer games were used in class.
4. Playing computer games improves my hand-eye coordination.
5. Playing computer games lets me use my imagination.
6. Playing computer games increases my typing speed.
CON
1. Playing computer games is not my specialty (-).
2. Playing computer games is easy to me.
3. I am not good at playing computer games (-).
4. I am skilled at playing computer games.
LIKE
1. I am very interested in solving problems in computer games.
2. If there are any unresolved issues in computer games, I will continue thinking
of them at another time.
3. If I encounter a problem that I do not understand while playing computer
games, I will keep trying until I find the answer.
185
Behavior
PAR
1. If there is a computer game club in school, I will participate in it.
2. If there is a computer game camp at school, I will participate in it.
3. At a computer show, I would visit a computer game booth.
LEI
1. Playing computer games makes me feel happy.
2. Playing computer games is part of my life.
3. I want to play computer games after each examination.
4. I kill time by playing computer games.
5. I talk about computer games with my friends in my spare time.
6. I can chat with others when playing computer games and won’t feel lonely.
(-): The score of these items should be reversed when performing analysis
Results
General findings
The main aim of the present paper was to assess the construct validity of the attitudinal scales and to determine their
reliability. In this study, exploratory principal components factor analysis with varimax rotation was used for
analysis of validity and Cronbach’s alpha was used as for the analysis of reliability. For the cognition subscale, the
eigenvalues of the factor, Learning, (exceeding 1: 2.651) and the factor, Confidence, (exceeding 1: 2.385) were
determined using principal components analysis with varimax rotation, as shown in Table 2. These two factors
accounted for 50.352 % of the variance. The internal reliability index (alpha coefficient) was sufficient for Learning
(0.737) and Confidence (0.77) as well as the entire subscale (0.819).
Table 2. Rotated factor loadings and Cronbach’s alpha values for the cognition subscale of the Computer Game
Attitude Scale
Item
Factor 1: Learning
Factor 2: Confidence
Factor 1: Learning alpha = 0.737
LRN1
0.537
LRN2
0.629
LRN3
0.557
LRN4
0.657
LRN5
0.79
LRN6
0.59
Factor 2: Confidence alpha = 0.77
CON1
0.666
CON2
0.74
CON3
0.813
CON4
0.715
Eigenvalue
2.651
2.385
% of variance
26.505%
23.847%
Overall alpha=0.819, total variance explained is 50.352%
For the affection subscale, the eigenvalue of the factor Liking (exceeding 1: 1.964) was determined using principal
components analysis without rotation, as shown in Table 3. The factor of Liking accounted for 65.455 % of the
variance. The internal reliability index (alpha coefficient) was sufficient for the factor of Liking (0.736).
Table 3. Factor loading and Cronbach’s alpha for the affection subscale of the Computer Game Attitude Scale
Item
Factor 1: Liking
Factor 1: Liking alpha = 0.736
LIKE1
0.791
LIKE2
0.807
LIKE3
0.829
Eigenvalue
1.964
% of variance
65.455%
186
For the behavior subscale, the eigenvalues of the factor, Participation, (exceeding 1: 3.259) and the factor, Leisure,
(exceeding 1.878) were determined using principal components analysis with varimax rotation, as shown in Table 4.
These two factors accounted for 50.073 % of the variance. The internal reliability index (alpha coefficient) was
sufficient for Participation (0.649) and Leisure (0.839), as well as the entire subscale (0.822).
Table 4. Rotated factor loadings and Cronbach’s alpha for the behavior subscale of the Computer Game Attitude
Scale
Item
Factor 1: Participation
Factor 2: Leisure
Factor 1: Participation alpha = 0.649
PAR1
0.84
PAR2
0.751
PAR3
0.646
Factor 1: Confidence alpha = 0.839
LEI1
0.788
LEI2
0.734
LEI3
0.729
LEI4
0.729
LEI5
0.64
LEI6
0.731
Eigenvalue
3.259
1.878
% of variance
36.21%
20.863%
Overall alpha = 0.822, total variance explained is 50.073%
The intercorrelation matrix showed significant correlation coefficients among five factors of computer game attitude
and three subscales (Table 5). The correlation coefficients among the five factors ranged between 0.405** and
0.669**, and all correlations reached a significance level of 0.01. The correlation coefficients among the three
subscales ranged between 0.644** and 0.762**, and all of the correlations reached a significance level of 0.01. The
three subscales and five factors were used to provide a coherent measurement of computer game attitude.
Factor
LRN
CON
LIKE
PAR
LEI
Subscale
Cognition
Affection
Behavior
** P < 0.01
Table 5. Intercorrelation matrix of five computer game attitude factors and three subscales
LRN
CON
LIKE
PAR
0.533**
0.645**
0.482**
0.521**
0.434**
0.562**
0.669**
0.555**
0.541**
0.405**
Cognition
Affection
Behavior
0.653**
0.762**
0.644**
-
LEI
Table 6. Descriptive statistics of scores on the five factors and three subscales of the Computer Game Attitude Scale
Factor
Items
Range
Mean
SD
Skewness
LRN
6
1-4
3.093
0.638
-0.803
CON
4
1-4
2.834
0.757
-0.231
LIKE
3
1-4
3.095
0.755
-0.783
PAR
3
1-4
2.528
0.797
0.049
LEI
6
1-4
3.105
0.706
-0.872
Subscale
Cognition
10
1-4
2.99
0.602
-0.533
Affection
3
1-4
3.095
0.755
-0.783
Behavior
9
1-4
2.913
0.628
-0.668
187
Student scores on the scale
Table 6 presents descriptive statistics or the five factors and the three subscales. Among the five factors, students
scored highest on the Leisure factor, followed by the Liking and Learning, with Confidence and Participation scoring
the lowest. Among the three subscales, students scored highest on the Affection subscale, followed by Cognition and
Behavior. These results imply that these students tended to learn from the computer game, liked using the computer
game, and played the computer game in their leisure time. The relatively lower scores for the Confidence and
Participation factors suggest that some of the students might have had difficulty playing the computer game alone
and joining related activities. For elementary school students, it is difficult to pay the fees required to join related
activities or independently accomplish the difficult missions encountered while playing the computer game.
Gender differences on the scale
This study compared the scores of male and female students for the five factors and three subscales of the NCGAS.
The results of t-tests are presented in Table 7, revealing that male students expressed stronger positive attitudes
toward the computer game than their female counterparts for all five factors and three subscales. In other words, the
male students believed more strongly that computer games have a positive impact on learning and showed greater
confidence, liked the games more, and participated in the games more than the females students did. The male
students were also likely to use the computer games more frequently than female students in their leisure time.
Factor
LRN
CON
LIKE
PAR
LEI
Subscale
Cognition
Affection
Behavior
Table 7. Comparison of gender on the factors and subscales of NCGAS
Gender
Mean
S. D.
Male
3.028
0.722
Female
2.746
0.568
Male
3.002
0.842
Female
2.349
0.811
Male
3.147
0.717
Female
2.703
0.672
Male
2.909
0.881
Female
2.522
0.758
Male
3.278
0.689
Female
2.885
0.712
Male
Female
Male
Female
Male
Female
3.017
2.587
3.147
2.703
3.155
2.764
0.714
0.583
0.717
0.673
0.636
0.611
t
3.966***
7.207***
5.833***
4.301***
5.132***
6.027***
5.833***
5.726***
***p < 0.001
Internet experience and NCGAS
This study also examined the relationship between the attitudes of students toward computer games and their
experience using the Internet. Internet experience is defined in this study as the weekly Internet usage by students at
home. We divided the sample into five groups according to their Internet experience: no usage, less than two hours,
2–4 hours, 4–6 hours, and more than 6 hours. In the sample, 12.7 % of the students did not use the Internet at home,
23.2 % used the Internet for less than 2 hours per week, 32.2 % for 2–4 hours, 13.8 % for 4–6 hours, and 18.1 % for
more than 6 hours. Table 8 compares the groups according to Internet experience and their attitudes toward the
computer game. ANOVA testing revealed that Internet experience played a role in the factors of Learning,
Confidence, Liking, Participation and Leisure, and influenced the subscales of Cognition, Affection, and Behavior.
Through a series of Scheffe tests, it was determined that students who use the Internet more tended to have
statistically higher scores on the Cognition, Affection and Behavior subscales as well as the factors of Learning,
Confidence, Liking, Participation, and Leisure. These relationships indicate that the students with more Internet
188
experience tended to have positive perceptions of learning with computer games, demonstrate a high level of
confidence playing them, enjoy these activities, and participate in them both during class as well as in their leisure
time. Furthermore, they tend to have higher scores for Cognition, Affection, and Behavior related to learning with
computer games. Increasing one’s experience with the Internet could greatly help students to develop favorable
attitudes toward using computer games for learning.
Internet
experiences
(1)no usage
(2)less than
2 hours
(3)2–4
hours
(4)4–6
hours
(5)more
than 6
hours
F
(ANOVA)
Scheffe test
LRN
Table 8. Analysis of Internet experience and NCGAS
CON
LIKE
PAR
LEI
Cognition
Affection
Behavior
2.304
(0.779)
2.720
(0.679)
2.915
(0.500)
3.119
(0.538)
3.263
(0.573)
2.069
(0.807)
2.503
(0.973)
2.568
(0.745)
2.883
(0.731)
3.434
(0.618)
2.592
(0.749)
2.878
(0.765)
2.877
(0.677)
3.109
(0.636)
3.214
(0.731)
2.533
(0.853)
2.638
(0.775)
2.655
(0.722)
2.823
(0.948)
3.026
(0.964)
2.492
(0.804)
2.815
(0.754)
3.091
(0.634)
3.361
(0.538)
3.625
(0.447)
2.210
(0.725)
2.633
(0.714)
2.776
(0.517)
3.025
(0.542)
3.331
(0.523)
2.592
(0.749)
2.878
(0.765)
2.877
(0.677)
3.109
(0.636)
3.214
(0.731)
2.506
(0.703
2.756
(0.688)
2.945
(0.557)
3.181
(0,546)
3.425
(0.497)
15.370***
18.312***
5.348***
2.587*
20.755***
20.426***
5.348***
15.784***
(4)>(1)
(4)>(2)
(5)>(2)
(5)>(3)>(1)
(5)>(2)
(5)>(3)>(1)
(5)>(4)>(1)
(4)>(1)
(5)>(1)
(5)>(1)
(4)>(2)
(5)>(2)
(5)>(3)>(1)
(4)>(2)>(1)
(5)>(2)>(1)
(5)>(3)>(1)
(4)>(1)
(5)>(1)
(4)>(1)
(5)>(1)
(4)>(2)
(5)>(2)
(5)>(3)>(1)
*p < 0.05 ***p < 0.001
Discussion
NCGAS was developed that reliably measures three subscales (cognition, affection, and behavior) and five factors
(learning, confidence, liking, participation and leisure) of students’ computer game attitudes. Scales formed from the
items chosen to represent these constructs have good validity and internal consistency reliability. The instrument
consists of an economic 22 items. It can be used as a core for larger questionnaires designed to address the role of
other variables that impact on game-based learning, or it can be used in a stand-alone fashion to examine the impact
of interventions intended to change these five factors and three subscales.
This study also revealed that male students have a more positive attitude toward using computer games for learning,
demonstrate greater confidence in using computer games, enjoy these kinds of activities, and freely participate in
these activities both in a classroom setting as well as in their leisure time at home. Male students earned higher
scores in the Cognition, Affection and Behavior subscales than their female counterparts. These results complement
those of previous studies indicating that males demonstrate very positive attitudes toward computer games while
females show a less positive or neutral attitude on all the four subscales: affective components, perceived control,
perceived usefulness, and behavioral components (Bonanno & Kommers, 2008; Chou & Tsai, 2007). Male students
feel more strongly than females that computer games are useful learning tools. One possible explanation could be
that females are always more skeptical than males about the instructional potential of computer games, considering
the availability of other resources to provide what can be learned from computer games. In contrast, boys perceive
computer games as a unique engaging experience, lacking entirely in other computer applications (Bonanno &
Kommers, 2008). Another explanation could be that male students are more likely to embrace a manipulationoriented information processing approach (Casey, 1996), a command strategy for executing tasks, and a competitive
social comportment when using computer games (Singh, 2001; Rommes, 2002). With this in mind, computer games
should be designed to satisfy these learning needs.
189
Second, the male students demonstrated greater confidence in controlling computer games by themselves. This is
consistent with the findings of Bonanno and Kommers’ (2008) indicating that males feel much more in control of
computer games, making them better able to perform demanding actions. The reason for this may be that boys tend
to have more experience participating in computer games than girls, and such extensive exposure to computer games
develops a higher level of confidence in using them (Brosnan, 1998). Success builds confidence; therefore, when
playing a new game or difficult game level, females require on-going reinforcement and more guidance than males.
In other words, female students may require more task-related support to build competence and confidence.
Third, male students reported deriving more enjoyment from the computer games than their female counterparts.
This is in line with the study of Chou and Tsai (2007), and might be due to the presentation of game content. It
appears that boys enjoy competition and gaining high scores; while girls prefer cooperation and completion. Kafai
(1996) proved that most commercially-available computer games do not reflect the interests or tastes of girls.
Therefore, a biography of gaming should be compiled and analyzed to identify the genres of computer games that
make girls feel uncomfortable. Further studies could explore how girls perceive incompetence in computer gaming
and self-exposure in collaborative computer game playing. Instructional computer game designs that are perceived as
less threatening should be identified and introduced during the initial phase to support game based learning.
Finally, male students were more likely to participate in computer game related activities and play the computer
game in their leisure time. This is consistent with the study of Chou and Tsai (2007) in which male students are
motivated by the aspect of entertainment in playing computer games to a higher degree than female students are. One
possible reason could be that females enjoy a greater variety of leisure activities, preferring those that are less
intensely focused. This could be the reason that females feel less motivated to participate in computer game activities
or play computer games in their leisure time. In contrast, playing computer games constitutes the main leisure
activity for many male adolescents, contributing to the quality of their friendships (Griffiths & Hunt, 1995). For this
reason, the findings of previous studies and the on-going habits of students should be considered when developing
effective game based learning environments.
Gender differences were observed for all five factors, and male students showed stronger positive attitudes with
regard to the Cognition, Affective and Behavior subscales. The most important contributing factor may be the
stronger motivation of males to play computer games (Chou & Tsai, 2007). It is necessary therefore, to consider
gender differences when dealing with the cognitive, affective, and behavioral aspects of incorporating computer
games into school instruction. Female students should be provided continual reinforcement and support when
participating in a computer game based learning environment.
The experience students have using the Internet was another variable to which researchers have paid considerable
attention. This study revealed that the influence of Internet experience on the attitudes of students toward computer
games parallels the findings in previous studies (Smith, Caputi, & Rawstorne, 2000; Tsai, Lin, & Tsai, 2001).
Students with more experience using the Internet generally have a more positive attitude toward computer games.
Our results indicate that prior experience with the Internet plays an important role in shaping attitudes toward
computer games. Familiarizing students with the Internet may provide a practical means to shift the attitudes of
students in a positive direction. Based on our empirical observations, many students use the Internet to play on-line
games in their leisure time; therefore, the negative impact of computer games, such as Internet addiction disorder
(Lin & Tsai, 2002; Tsai & Lin, 2003) or online aggression (Liu, Ho, & Song, 2011), should be noted when designing
game-based learning environments.
Conclusion
The CGAS presented in a previous study (Chappell & Taylor, 1997) explains only 44 % of all variables; however,
our New CGAS has demonstrated higher validity, while covering a broader range of factors, most of which were
developed in this study. Previous researchers addressing these issues tended to emphasize the affective component;
however in this study, the investigation has been expanded to include components of cognition and behavior. In the
past, gender differences in learning, confidence, participation, and leisure sub-scales were scarcely mentioned, while
this study has provides a number of unique insights into this aspect of computer gaming. We have also initiated an
investigation into the influence of Internet experience on NCGAS.
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The results of this study largely parallel those of previous research addressing attitudes related to technology. Male
students demonstrated greater enthusiasm for computer games than their female counterparts, as did all students with
more experience using the Internet. Clearly, software developers should consider gender preference and Internet
experience when developing educational computer games. The inclusion of female protagonists and suitable learning
support for learners with various degrees of experience using the Internet would be good options. Computer games
represent a fundamental element in the lifestyles of young adolescents, while learning environments and attitudes
strongly influence the utilization of technology (Lee & Chen, 2008; Liu, 2010) Gauging the attitudes of students
toward computer games is an important topic of research into game-based learning, and this study represents an
important first step. The NCGAS is a valid and reliable tool, providing a comprehensive framework with which to
gauge the attitudes of students toward computer games. Researchers involved in game-based learning could use this
tool in collaborative learning environments to organize groups according to attitudinal preferences, particularly those
found on the cognitive subscale: learning and confidence.
The next logical step in this investigation should focus on continued modification. Future researchers could develop
additional attitude factors to address issues unique to computer games, such as addiction and aggression. This pilot
study applied exploratory factor analysis to verify the structure of NCGAS, and we would encourage the use of
confirmatory factor analysis (Chang et al., 2011; Tsai, Lin, & Tsai, 2001) to further examine the validity and
reliability of the newly developed scale. Researchers are also encouraged to apply this scale in different countries
and cultural backgrounds. In the future, NCGAS could be used to identify the attitudes of individuals of various ages
and educational levels toward learning with computer games. A more extensive study should be conducted to reveal
the most advantageous approach to using these games within existing educational systems. Subsequent studies could
investigate the relationships among gaming attitudes, the structure of games, the setting of playing games, game
types and educational objectives. A careful exploration of the attitudes of students regarding computer games in
these areas could provide additional insight into the design of serious instruction based games.
Acknowledgements
This study was supported by the National Science Council, Taiwan, under contract numbers NSC 100-2631-S-008001, NSC 100-2511-S-008-017-MY2 and NSC 100-2511-S-008-006-MY2.
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