Game-based learning verse traditional instruction on student affective
outcomes in Taiwan: A meta-analysis
Yuen-kuang Cliff Liao Department of Education
National Taiwan Normal University, Taiwan, R.O.C yliao@ntnu.edu.tw
Abstract
A meta-analysis was performed to synthesize existing research comparing the effects of game-based learning (GBL) versus traditional instruction (TI) on students’ affective outcomes in Taiwan. Twenty-six studies were located from five sources, and their quantitative data were transformed into Effect Size (ES). The overall grand mean of the study-weighted ES for all 26 studies was 0.64. The results suggest that GBL has a greater effect than TI on promoting student affective outcomes in Taiwan. In addition, five of the twelve moderator variables selected for this study had a statistically significant impact on the mean ES. The results from this study suggest that the effects of GBL are positive compared to TI. The analyses of moderator variables also provided some valuable implications for verifying the effects of implementing game-based learning in educational settings.
1. Introduction
The educational potential of games has been claimed by researchers and educators for quite a long time. Piaget [1], for instance, stated that the process of playing games could help children master the environments that they live in and create their worlds of imagination. Others also claimed that games could support, reinforce and accelerate the learning process, and support higher-order cognitive development [2], [3], [4], [5]. Walliser [6] believed that game-based learning stimulated critical thinking, information gathering and sharing, and collective problem solving. In fact, there was a fairly wide consensus in the game study literature that the values of discovery and achievement were fundamental to the nature of games [7].
Researchers have also argued that game-based learning can be more enjoyable, more interesting, and, thus, more effective than traditional learning modes [8], [9] ,[10]. Papastergiou [11], for example, addressed the potential benefits of computer and video games: (a) they can support multi-sensory, active, experiential, problem-based learning, (b) they favor activation of prior knowledge given that players must use previously learned information in order to advance, (c) they provide immediate feedback enabling players to test hypotheses and learn from their actions, (d) they encompass opportunities for self-assessment through the mechanisms of scoring and reaching different levels, and (e) they are increasingly becoming social environments involving communities of players. (p.603)
In addition, several studies have found that educational games could promote students’ affective outcomes, such as attitudes, interests and motivation to learning or specific subject matters. Virvou, Katsionis, and Manos [12], for example, designed VR-ENGAGE computer game for teaching geography to fourth grade students and examined its effectiveness. Results showed that VR-ENGAGE was more effective when compared to educational software without game characteristics; in addition, poor performing students benefited more from the game environment than the good performing students. Tüzün, Yılmaz-Soylu, Karakus, Inal, Kızılkaya [13] designed a three-dimensional educational computer game for 24 students in fourth and fifth grades to examine their achievement and motivation. Results indicated that students made significant learning gains, higher intrinsic motivations and lower extrinsic motivations by participating in the game-based learning. Liu and Chu [14] investigated how ubiquitous games influence English learning achievement and motivation through a context-aware ubiquitous learning environment. Results demonstrated that incorporating ubiquitous games into the English learning process could achieve a better learning outcomes and motivation than using non-gaming method. A positive relationship between learning outcomes and motivation was also found.
1.1 Purposes of study
In spite of claims regarding the potential benefits of using games in fostering students’ affective outcomes, research results comparing the effects of game-based learning and traditional instruction on students’ affective outcomes in Taiwan are conflicting. For example, Cheng [15], Huang [16], Ke [17], Lin [18], Wang [19], and Wu [20] all report significant gains for game-based learning over traditional instruction. On the other side, Chen [21], Lai [22], Lin [23], Lo [24], and Wang [25] have found no significant differences between game-based learning and traditional instruction. Owing to the contradictory evidence provided by existing research in the area, and very little, if any, thorough quantitative synthesis of game-based learning in Taiwan has been done, it is important to conduct a meta-analysis to clarify the research conclusions.
2. Procedures
The research method used in this study is a meta-analytic approach similar to that suggested by Hedges and Olkin [26]. The purpose of this study was to synthesize and analyze the research on the effects of two instructional approaches. It is important to define these approaches so as to provide for selection of appropriate studies:
Game-based Learning (GBL) -- classes using games (including non-computer games, computer-based games , and web-based games) as instructional approaches to teach students, and
Traditional Instruction (TI) -- classes using traditional classroom instruction to teach students.
2.1 Data Sources
The studies considered for use in this meta-analysis came from five sources. One large group of studies came from computer searches of the Index to Taiwan Periodical Literature System. A second group of studies came from the National Digital Library of Thesis and Dissertation in Taiwan (NDLTD). A third group of studies was retrieved from the Government Research Bulletin (GRB) of Taiwan. The fourth group of studies was from the Educational Document Online. The last group of studies was retrieved by branching from bibliographies in the documents located through review and computer searches.
Twenty-six studies were located through these search procedures; 23 studies came from the National Digital Library of Thesis and Dissertation in Taiwan, and only 3 studies were retrieved from published journals. According to Glass, McGaw, and Smith [27], using only published research in a meta-analysis can inflate the mean Effect Size (ES). In their meta-analysis on computer-assisted instruction (CAI), Christman, Badgett, and Lucking [28] included 13 dissertations and 2 unpublished papers in an effort to reduce the inflation of ES resulting from publication
bias. However, since the current study retrieved more than 90% of its studies from dissertations/theses that may cause the so-called publication bias, Rosenthal’s [29] Fail-safe Number was calculated. The result shows that Fail-safe Number was larger than the Tolerance level, indicating that publication bias in the present synthesis does not exist.
Several criteria were established for inclusion of studies in the present analysis:
1. Studies had to compare the affective outcomes (e.g., motivation, attitudes, and interests toward learning or/and subject matters) of GBL vs. TI on students’ achievement.
2. Studies had to provide quantitative results from both GBL and TI classes so that the ES could be estimated. 3. Studies had to be retrievable from
university or college libraries by interlibrary loan or from GRB, or Dissertation and Thesis Abstract System of Taiwan.
4. Studies had to use Taiwanese students as subjects.
There were also several criteria for eliminating studies or reports cited by other reviews: (a) studies did not report sufficient quantitative data in order to estimate Effect Size; (b) studies reported only correlation coefficients -- r value or Chi-square value; (c) studies could not be obtained through interlibrary loans or from standard clearinghouses.
2.2 Outcome Measures
The affective outcomes measured most often in the 26 Studies was student attitudes/interest toward learning, as indicated on standard or researcher-develop affective questionnaire at the end of the program of instruction. For statistical analysis, outcomes from a variety of different studies with a variety of different instruments had to be expressed on a common scale. The transformation used for this purpose was the one recommended by Hedges and Olkin [26]. To reduce measurements to a common scale, each outcome was coded as an Effect Size (ES), defined as the difference between the treatment and control means, divided by the pooled standard deviation. Furthermore, when a sample size in a study is small, Hedges’s g+ (unbiased estimate of ES) was calculated to remove possible sample bias (Hedges and Olkin, [26], p.81). For those studies that did not report means and standard deviations, F values or t values were used to estimate the ES. Also, in studies which used one-group pretest-posttest design, in which a control group did not exist, an alternative approach suggested by Andrews, Guitar, and Howie [30] was used. In their approach, the ES is estimated by comparing the post-treatment mean with the pre-treatment mean, and dividing by pooled standard deviation.
In most cases, the application of the formula given by Hedges and Olkin was quite straightforward.
But in some cases, when more than one value was available for use in the formula of ES, the value which measured outcomes most correctly was selected. For example, some studies reported both differences on posttest measures and differences in pre-post gains, and some studies reported both raw-score differences between groups and covariance-adjusted differences between groups. In such cases, pre-post gains and covariance-adjusted differences were selected for estimating ES.
In addition, several subscales and subgroups were used in measuring a single outcome (e.g., those that reported separate data by gender or grade). In such cases, each comparison was weighted in inverse proportion to the number of comparisons within the study (i.e., 1/n, where n = number of comparisons) so that the overweighing of ES of a study could be avoided (see, for example, [31], p. 230).
2.3 Study Features Coding
Twelve study features were coded for each study in the present synthesis. These variables are listed in Table 1. Each of these study features was placed in one of the following set of characteristics: (a) study characteristics, (b) methodological characteristics, and (c) program characteristics. The first two study features in the study characteristics were coded so that potential different effects for subjects with different backgrounds could be detected. The other two study features (i.e., type of publication and year of publication) in the study characteristics were coded because it is important to know how effects are related to sources of information over time. Three study features placed in the methodological characteristics were coded so that effects related to characteristics of research procedures could be detected. The last five study features in the program characteristics were coded because it is critical to know how effects are related to nature and design of the primary study.
Table 1: The Assignments of Study
Features in Each Characteristic
Characteristics Variables Study Characteristics Grade Level Subject Area Type of Publication Year of Publication Methodological Characteristics Instructor Bias Sample Size
Type of Research Design
Program Characteristics
Duration of Treatment Purpose of Instruction
Group Size for Treatment Occasion of Treatment Type of Game
2.4 Data Analysis
For the total set of 26 studies being investigated, Hedges and Olkin’s [26] homogeneity procedures were employed in aggregating and analyzing the effect sizes. Each effect size was weighted by the inverse of its sampling variance. Thus, more weight was given to findings that were based on larger sample sizes. The weighted ESs were then aggregated to form an overall weighted mean estimate of the treatment effect (d+).
The significance of the mean ES was judged by its 95% confidence interval (95% CI). A significantly positive (+) mean ES indicates that the results favor GBL; a significantly negative (–) mean ES indicates that the results favor TI.
To determine whether the findings in each dataset shared a common ES, the set of ESs was tested for homogeneity by the homogeneity statistic (QT).
When all findings share the same population ES, QT
has an approximate distribution with k – 1 degrees of freedom, where k is the number of ES. If the obtained QT value was larger than the critical value, the
findings were determined to be significantly heterogeneous, meaning that there was more variability in the ESs than chance fluctuation would allow [26].
Next, a series of subgroup moderate variable analyses were conducted. A mixed effects model was used for these analyses to model within-group variation. A between-group heterogeneity statistic (QBetween) was computed to test for statistical
differences in the weighted mean ESs for various subsets of the effects (e.g., studies implemented in a large group as opposed to a small group).
2.5 Coder Reliability
To obtain more reliable outcomes from coding, the researcher of this study and two research assistants coded the studies. Each of the two research assistants coded half of the studies on each of the study features. As a check for accuracy, the researcher coded each of the studies independently. The inter-coder agreement rate for ES calculation and study feature coding were 85.3% and 84.6%, respectively. In addition, disagreements between the two coders were resolved through discussion. A final agreement had to be reached after discussion.
3. Results and Discussion
3.1 Overall effects
In all, 26 studies representing 2345 students were analyzed in this meta-analysis. Table 2 illustrates the 26 ESs derived from the 26 studies. Of the 26 ESs included in the present synthesis, 24 (92%) of the study-weighted ESs were positive and favored the GBL group, while 2(8%) of them were negative and favored the TI group. The range of the study-weighted ESs was from -0.08 to 2.61. The mean weighted ES (d+) was 0.64 (95% CI is 0.552to 0.721). When this
mean weighted ES was converted to a Z-value, the Z-value was 14.83 (p < .0001). In addition, when this
mean weighted ES was converted to percentiles, the percentiles on students' cognitive achievement were 74 for the GBL group and 50 for the TI group. The overall grand median for all 26 ESs was 0.56, suggesting that percentiles on students' achievement were 71 for the GBL group and 50 for the TI group. The standard deviation of 0.53 reflects the medium variability of ESs across studies. The results indicate that, on average, there was a medium significant effect on affective outcome; GBL group had a significant higher effect than TI group.
The results of this meta-analysis indicate that GBL has moderately positive effects on students’ affective outcomes over TI in Taiwan. An effect is said to be medium when ES = 0.5 and large when ES = 0.8 [32]. The effectiveness of GBL was also confirmed by its 92% positive study-weighted ES values and significant Z-value. With nearly2400 subjects included in this meta-analysis, the generalization of this study is considered stable. The moderateness of the effect must be kept in mind since the overall study-weighted mean ES of 0.64 only indicates 24 percentile scores higher than the TI group.
The homogeneity statistics (QT = 103.35, df=25,
p<.0001), however, indicate that findings on affective outcome were significantly heterogeneous, suggesting that the study-weighted mean ES of 0.64 may not be representative of the findings integrated and that other study features may moderate the magnitude of the ESs. A series of subgroup analyses of the moderator variables were then conducted.
3.2 Study features analyses
able 3 presents the results of the homogeneity statistics (QT) analysis for individual study features.
Of the 12 study features analyzed,5 moderate variables were T significantly related to the variability in the affective outcome. The analysis of each of the significant study features is described in the following section.
3.2.1 Instructor bias.
For instructor bias, studies were grouped into two subsets, same instructor and different instructor, denoting whether the same or different teachers taught both the GBL and TI classes. After reviewing several meta-analysis of media comparison research, Clark [49] suggested that the positive effects of media seemed to be the uncontrolled effects of instructional method or content differences between treatments that were compared; he concluded that effects more or less disappeared when the same instructor delivered all treatments. For the present meta-analysis, the mean ES for studies using different instructors was significantly higher than studies using sameinstructors for treatments (QB = 13,05, df = 3, p < .05). The
findings do not seem to support Clark’s view. The mean ESs for both subsets were all positive and significantly different from zero (95% CI are 0.32 to 0.58, and 0.64 to 0.89, respectively), indicating that students’ affective outcomes in GBL were
Table 2: Descriptive data of 26 ESs
Author(s) Year Effect size 95% CI Z-Value N g+ SE L. Limit U.Limit Chen [21] 2006 0.351 0.277 -0.191 0.894 1.269 53 Chen [33] 2007 0.435 0.244 -0.042 0.912 1.787 69 Cheng [34] 2009 0.895 0.291 0.325 1.465 3.077* 52 Huang [16] 2009 1.288 0.263 0.773 1.803 4.904* 70 Hung [35] 2006 0.683 0.373 -0.048 1.413 1.832 30 Kao [36] 2009 0.730 0.313 0.117 1.343 2.336* 44 Ke [17] 2006 1.428 0.185 1.066 1.791 7.723* 149 Lai [22] 2005 0.064 0.172 -0.272 0.401 0.374 138 Lai [37] 2007 0.549 0.129 0.295 0.802 4.244* 249 Li [38] 2009 0.518 0.359 -0.187 1.222 1.440 16 Lin [23] 2004 -0.075 0.265 -0.595 0.444 -0.284 58 Lin [39] 2008 0.338 0.187 -0.029 0.704 1.805 116 Lin [40] 2009 0.349 0.248 -0.137 0.835 1.407 66 Lin [18] 2010 2.611 0.357 1.910 3.311 7.305* 60 Liu [41] 2005 0.738 0.146 0.452 1.023 5.065* 218 Lo [24] 2007 -0.004 0.196 -0.389 0.381 -0.020 104 Pan et al. [42] 2003 0.389 0.176 0.044 0.735 2.208* 131 Shu [43] 2007 0.566 0.400 -0.218 1.350 1.416 26 Shyu [44] 2006 0.878 0.288 0.314 1.441 3.050* 53 Su et al. [45] 2005 0.419 0.266 -0.102 0.941 1.577 58 Tseng [46] 2007 0.522 0.250 0.031 1.013 2.085* 66 Wang [25] 1998 0.319 0.430 -0.523 1.161 0.742 24 Wang et al. [47] 2005 0.991 0.148 0.700 1.282 6.683* 204 Wang [19] 2005 0.806 0.201 0.412 1.201 4.007* 107 Wang [48] 2007 0.809 0.177 0.462 1.156 4.565* 138 Wu [20] 2007 0.804 0.307 0.203 1.405 2.622* 46 Mean ES (d+) 0.636 0.043 0.552 0.721 14.832* 2345 Standard Deviation 0.53 N of study 26 Homogeneity Statistic（QT） 219.426* *p<.05 significantly positive compared to TI regardless of whether the treatment used the same or different instructor. The tests of Qw-statistics for the two subsets
of studies were significantly heterogeneous, suggesting that the mean ES may not be representative of the study's findings and that other study features may influence the magnitude of the ESs.
3.2.2 Type of research design
For type of research design, studies were divided into four subsets: one group repeated measure design, pretest-posttest control group design, nonequivalent control group design, and posttest only control group design. More than half of studies used nonequivalent control group design. The mean ES for studies employed posttest only control group design was significantly higher than studies employed pretest-posttest control group design and nonequivalent control group design (QB = 33.13, df =
3, p<.05). Also, the mean ESs for studies for all four subsets were positive and significantly different from zero, indicating that students’ affective outcomes in GBL were significantly higher than in TI regardless what types of research designs were used.
Furthermore, the tests of Qw-statistics for studies
employed pretest-posttest control group design, nonequivalent control group design, and posttest only control group design were all heterogeneous, indicating that the mean ESs may not be representative of the findings integrated and that other study features may moderate the magnitude of the ESs.
3.2.3 Group size for treatment
The size of groups used in a collaborative learning has often been discussed. Several studies reported that small groups function better because the members can interact more intimately and cohesively ([50], [51]). Harasim [52] also claimed that groups of 2–4 people are preferred by students for online group work for task-oriented activities. Of the 26 Studies in the present synthesis, 9 (35%) of the ESs employed large group (over 5 people) instruction for the GBL, 5 (19%) of the ESs used a small group (2-4 people), while 4 (15%) of the ESs used individual setting; there were 8 (31%) ESs that did not report group size. The test of QB-statistics was significantly different from zero (QB
= 13.38, df = 3, p<.05); the mean ES for studies emplo yed large group size (d+ = 0.86) was
Table 3: Results of Individual Study Features Analysis Study Feture QBa Kb d+c 95% CId QWe Study Characteristics Grade Level 6.15 26 Elementary 17 0.62 0.52 - 0.72 59.04* Secondary 6 0.55 0.39 - 0.71 12.35*
College & Adult 3 1.32 0.93 - 1.71 18.8*
Subject Area 1.61 26
Language arts/Social work 4 0.74 0.46 - 1.02 1.27
Math/ Science/Technology 14 0.62 0.52 - 0.73 81.85* PE/Health Ed 4 0.57 0.37 - 0.77 17.44* Other 4 0.74 0.48 - 1.01 1.18 Type of Publication 2.23 26 Published journal 3 0.74 0.52 - 0.96 6.82* Unpublished dissertation 23 0.63 0.54 - 0.72 94.29* Year of Publication 2.26 26 1998-2005 8 0.56 0.42 - 0.69 27.04* 2006-2010 18 0.69 0.58 - 0.80 74.05* Methodological Characteristics Instructor Bias 13.05* 26 Same instructor 15 0.45 0.32 - 0.58 51.46* Different instructor 8 0.76 0.64 - 0.89 38.70* Unspecified 3 0.79 0.51 - 1.07 0.10
Type of Research Design 33.13* 26
One group repeated measure 5 0.81 0.60 - 1.02 3.85
Pretest-posttest control group 3 0.53 0.29 - 0.77 7.48*
Nonequivalent control group 14 0.50 0.39 - 0.60 32.03*
Posttest only control group 4 1.24 1.00 - 1.49 26.86*
Sample size 0.17 26 1-49 6 0.64 0.35 - 0.92 1.09 50-99 10 0.67 0.50 - 0.83 49.21* Over 100 10 0.63 0.52 - 0.73 52.88* Program Characteristics Duration of Treatment 7.55 26 0-1 month 9 0.82 0.65 - 0.98 38.78* 1-2 months 9 0.56 0.43 - 0.68 39.38*
Over 2 months 7 0.56 0.39 - 0.73 17.63* Unspecified
1 0.81 0.41 - 1.20 0.00 Purpose of Instruction 0.63 2 Replacement of traditional 18 0.66 0.56 - 0.77 97.33* Instruction Supplement to traditional 6 0.59 0.44 - 0.74 4.55 Unspecified 2 0.64 0.15 - 1.13 0.00
Group size for Treatment 13.38* 26
Large group (> 5 persons) 9 0.86 0.71 - 1.00 26.18*
Small group (2-4 persons) 5 0.54 0.33 -0.74 8.06
Individual 4 0.51 0.34 - 0.69 42.35* Unspecified 8 0.53 0.37 - 0.69 13.37 Occasion of Treatment 6.44* 26 In class 20 0.59 0.50 - 0.68 82.61* After class 4 0.94 0.68 - 1.20 14.23* Unspecified 2 0.76 0.29 - 1.22 0.06 Type of Game 15.46* 26
Web-based game 3 1.08 0.80 - 1.35 21.94* Computer-based
game 7 0.45 0.29 - 0.60 10.39
Non-computer game 16 0.66 0.55 - 0.77 55.56*
a
the between-class homogeneity statistics.
b the total number of studies in each subgroup. c
the weighted mean ES.
d
the 95% confidence interval for d+.
e
the within-class goodness-of fit statistics. *p<.05
significantly higher than studies used small group (d+
= 0.54) and individual setting (d+ = 0.51), indicating
students’ affective outcomes were higher in a large group instruction. These results do not quite consistent with findings from previous studies of collaborative learning. In addition, the mean ES for all three subsets of studies were all positive and significantly different from zero, indicating that students’ affective outcomes in GBL were significantly positive compared to TI for all types of group sizes. The tests of Qw-statistics for
studies with a small group size was homogeneous (Qw
= 8.06, p > .05), indicating that the ESs were consistent across the findings, whereas the tests of Qw-statistics for studies with large and individual
group sizes were significantly heterogeneous, suggesting that the mean ES may not be representative of the study's findings and that other study features may influence the magnitude of the ESs.
3.2.4 Occasion of treatment
For occasion of treatment, studies were divided into two subsets: in class and after class. Of the 26 studies in the present synthesis, 20 (80%) of the ESs were coded as in class and only 4 (15%) of the ESs were coded as after class. The test of QB-statistics was
significantly different from zero (QB = 6.44, df = 2,
p<.05); the mean ES for studies coded as after class (d+ = 0.94) was significantly higher than studies coded
as in class (d+ = 0.59), indicating students gained more
affective outcomes in an after class GBL. This is probably because students may feel “playing games”, as compare to “learning”, in an after class situation that results in a greater affective outcome. The mean ES for both subsets of studies were all positive and significantly different from zero, indicating that students’ affective outcomes in GBL were significantly positive compared to TI for both occasions of treatment. However, the tests of Qw-statistics for both subsets were all significantly
heterogeneous, suggesting that the mean ES may not be representative of the study's findings and that other study features may influence the magnitude of the ESs.
3.2.5 Type of game
For type of game, studies were divided into three subsets: web-based game (on-line game), computer-based game (off-line game), and non-computer game. Of the 26 studies in the present synthesis, 16 (62%) of the ESs employed non-computer games, 7 (27%) of the ESs used computer-based games, and only 3 (12%) employed web-based games. The test of QB-statistics was
significantly different from zero (QB = 15.46, df = 2,
p<.05); the mean ES for studies employed web-based games (d+ = 1.08) was significantly higher than mean
ESs of studies used computer-based games (d+ = o.45)
and non-computer games (d+ = 66), indicating
students had higher affective outcomes such as learning motivation and attitudes toward web-based games. Yet, there were only 4 studies that used
web-based games; therefore, the result should not be over-generalized. The mean ES for all three subsets of studies were all positive and significantly different from zero, indicating that students’ affective outcomes in GBL were significantly positive compared to TI regardless the types of games. The tests of Qw-statistics for studies employed computer-based
games was homogeneous (Qw = 10.39, p > .05),
indicating that the ESs were consistent across the findings, whereas the tests of Qw-statistics for studies
used web-based games and non-computer games were significantly heterogeneous, suggesting that the mean ES may not be representative of the study's findings and that other study features may influence the magnitude of the ESs.
4. Conclusion
The results from this study suggest that the effects of game-based learning are positive over traditional instruction on students’ affective outcomes in Taiwan. Several moderate variables were also analyzed to verify the effects while implement game-based learning in the educational settings. As many educators devote tremendous efforts with great expectation that technology will dramatically promote students’ affective outcomes and increase students’ academic achievement, the results of this study provide to classroom teachers an accumulated research-based evidence for positive outcomes of using technology in instruction. Left unanswered is the question of which factors truly contribute to the positive affective outcomes. Studies of this question will require further clarification of the exact relationship between educational games and learning. This meta-analysis points out only that improvements of students’ affective outcomes are possible. That information by itself is useful.
References
[1] Piaget J.,1962, “Play, Dreams and Imitation in Childhood,” New York: W. W. Norton.
[2] Green S.C. and Bavelier D., 2003, “Action video game modifies visual selective attention,” Nature Vol. 423, pp. 534–537.
[3] Klabbers J., 2003, “The gaming landscape: A taxonomy for classifying games and simulations,” In M. Copier, M. and Raessens, J. (Eds.) Level Up Digital Games Research Conference (p. 54–67). The Netherlands: University of Utrecht.
[4] de Freitas S., 2005, “Review of the uptake and embedding of digital content: Internal report,” UK: Becta, Coventry.
[5] de Freitas S. and Oliver M., 2006, “How can exploratory learning with games and simulations within the curriculum be most effectively evaluated?,” Computer & Education Vol. 46, pp. 249–264.
[6] Walliser , B., 1998, “A spectrum of equilibration processes in games,” Journal of Evolutionary Economics, Vol. 8, pp. 67–87.
emergence and games of progression,” In Mayra F. (Ed.), Computer Game and Digital Cultures Conference Proceedings (p.323–329). Finland: Tampere University Press.
[8] Gee, J. P., 2003, “What video games have to teach us about learning and literacy,” New York: Palgrave Macmillian.
[9] Malone, T., 1980, “What makes things fun to learn? A study of intrinsically motivating computer games,” Technical report no. CIS-7 (SSL-80-11), Xerox Palo Alto Research Center.
[10] Prensky, M., 2001, “Digital game-based learning,” New York: McGraw-Hill.
[11] Papastergiou, M., 2009, “Digital game-based learning in high school computer science education: impact on educational effectiveness and student,” Computers & Education, Vol. 52, No. 1, pp. 1-12.
[12] Virvou, M., Katsionis, G. and Manos, K., 2005, “Combining software games with education: Evaluation of its educational effectiveness,” Educational Technology & Society, Vol.8, No.2, pp. 54–65.
[13] Tüzün,H., Yılmaz-Soylu, M., Karakuş, T., İnal, Y. and Kızılkaya, G., 2009, “The effects of computer games on primary school students’ achievement and motivation in geography learning,” Computers & Education, Vol. 52, No. 1, pp. 68-77.
[14] Liu, T. and Chu, Y., 2010, “Using ubiquitous games in an English listening and speaking course: Impact on learning outcomes and motivation,” Computers & Education, Vol. 55, No. 2, pp. 630-643.
[16] Huang, Y., 2009, “A study of the effects of science play instruction with assisted scientific toys on the learning of “air” unit for the third-grade students,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[17] Ke, H., 2006, “A study of the effect of science instruction with toy-playing approach on elementary students’ attitudes,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[18] Lin, C., 2010, “A study of motivation effects of gesture and game-based mobile learning system -an example for botany misconception,” Unpublished master’s dissertation, National Hsinchu University of Education, Hsinchu, Taiwan.
[19] Wang, C., 2005, “A study of the effects of science play instruction with assisted scientific toys on fifth-grade students for their scientific literacy,” Unpublished master’s dissertation, National Taipei Teachers College, Taipei, Taiwan.
[20] Wu, T., 2006, “Development of a gamed-based learning system for enhancing learner''s motivation and self-perception in energy education,” Unpublished master’s dissertation,
National Central University.
[21] Chen, L., 2006, “A study of cooperative learning on mathematics teaching for the first-grade of elementary school in Taipei City,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[22] Lai, P., 2005, “The study of game teaching with 7th grade students on epistemological beliefs in mathematics, learning motivation and academic achievement,” Unpublished master’s dissertation, Da Yeh University, Changhua, Taiwan.
[23] Lin. J., 2004, “A study on the effects of incorporating game into the elementary mathematics teaching,” Unpublished master’s dissertation, National Taipei Teachers College, Taipei, Taiwan.
[24] Lo, G., 2006, “A study of the effectiveness of learning nutrition through computer game enrichment,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[25] Wang, I., 1998, “The effect of Adlerian group play therapy on the improvement of elementary school,” Unpublished master’s dissertation, National Tainan Teachers College, Tainan, Taiwan.
[26] Hedges, L. V. and Olkin, I., 1985, “ Statistical methods for meta-analysis” Orlando: Academic Press.
[27] Glass, G. V., McGaw, B. and Smith, M. L., 1981, “Meta-analysis in social research,” Beverly Hills, CA: Sage Publications.
[28] Christman, E., Badgett, J. and Lucking, R., 1997, “Progressive comparison of the effects of computer-assisted instruction on the academic achievement of secondary students,” Journal of Research on Computing in Education, Vol. 29, No. 4, pp. 325 – 337.
[29] Rosenthal, R.,1991, “Meta-analytic procedures for social research (Rev.ed.),” Beverly Hills: Sage.
[30] Andrews, G., Guitar, B. and Howie, P., 1980, “Meta-analysis of the effects of stuttering treatment,” Journal of Speech and Hearing Disorders, Vol. 45, pp. 287-307.
[31] Waxman, H. C., Wang, M. C., Anderson, K. A. and Walberg, H. J., 1985, “Adaptive education and student outcomes: A Quantitative synthesis,” Journal of Educational Research, Vol. 78, N0. 4, pp. 228-236.
[32] Cohen, J., 1977, “Statistical power analysis for the behavioral science (Revised Edition),” New York: Academic Press.
[33] Chen, J., 2007, “The effects of the scientific game activity on motivation toward science learning for the 8th grade students,” Unpublished master’s dissertation, National Taiwan Normal University, Taipei, Taiwan.
applied to the art appreciation curriculum in elementary school,” Unpublished master’s dissertation, National Hsinchu University of Education, Hsinchu, Taiwan.
[35] Hung, H., 2006, “Design and application of a game-based mobile guiding system,” Unpublished master’s dissertation, National Taiwan Normal University, Taipei, Taiwan.
[36] Kao, C., 2009, “The effectiveness of digital game-based learning on junior high school students' learning motivation, problem solving, and academic achievement,” Unpublished master’s dissertation, National Cheng Kung University, Tainan, Taiwan.
[37] Lai, J., 2007, “The effect of E-learning in scientific attitude of different earning style students,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[38] Li, C., 2009, “A action study of integrating games into the teaching of English for junior high students in a physical education school: A case of the Taitung Physical Education Secondary School,” Unpublished master’s dissertation, National Dong Hwa University, Hualien, Taiwan. [39] Lin, Y., 2008, “A design and study of simulation
game system：the case of learning lever principles of elementary students,” Unpublished master’s dissertation, National Taiwan Normal University, Taipei, Taiwan.
[40] Lin, Y., 2009, “Learning achievement of game-based learning on elementary school nutrition education,” Unpublished master’s dissertation, Leader University, Tainan, Taiwan. [41] Liu, Y., 2005, “the effect of Interesting Scientific
Competition on eighth-grade students’ achievement and attitudes toward science learning,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[ 4 2 ] Pan, I and Wang, M., 2003, “A study on the effects of the play-based elementary science teaching,” Journal of Taipei Municipal Teachers College, vol. 34, pp. 157-172.
[43] Shu, F., 2006, “The action research of games integrated into chindren's learning of mathematics after class-an example of disadvantaged fifth graders,” Unpublished master’s dissertation, National Taichung University of Education, Taichung, Taiwan.
[44] Shyu, J., 2006, “Effects of implementing the computer games on English vocabulary learning and attitudes,” Unpublished master’s dissertation, National Taipei University of Education, Taipei, Taiwan.
[45] Su, H. and Hsieh, H., 2007, “ The study of integrating science game into elementary school
natural science and living technology instructions on fourth graders' science attitudes,” C u r r i c u l u m & I n s t r u c t i o n Q ua r t e r l y , vo l . 1 0 , no . 1 , p p. 111-129.
[46] Tseng, K., 2006, “Influences of singing games applied Fulao children's songs to students' music learning interests and achievement,” Unpublished master’s dissertation, Taipei Municipal University of Education, Taipei, Taiwan.
[47] Wang, W., Chou, C., Peng, H. and Yeh, I., 2005, “ The implementation and effectiveness of initiative game activities program on social interaction in fifth and sixth grade students,” Bulletin of Physical Education, vol. 38, no. 3, pp.51-62.
[48] Wang, Y., 2007, “The effect of game teaching applied to national defense general education in vocational high schools,” Unpublished master’s dissertation, National College of Physical Education, Taipei County, Taiwan.
[49] Clark, R. E.,1983, “Reconsidering research on learning from media,” Review of Educational Research, Vol. 53, No. 4, pp. 445-459.
[50] Holubec, E. J., Johnson, D. W. and Johnson, R. T., 1995, “Cooperative learning in reading and language arts,” In Rabinowitz, M., Antonacci, P. and Hedley, C. N. (Eds.), Thinking and literacy: The mind at work (p. 229–240). NJ: Lawrence Erlbaum Associates.
[51] Johnson, D. W. and Johnson, R. T., 1999, “Making cooperative learning work,” Theory into Practice, Vol. 38, No. 2, pp. 67–73.
[52] Harasim, L. M., 1993, “Collaborating in cyberspace using computer conferences as a group learning environment,” Interactive Learning Environments, Vol. 3, No. 2, pp. 119–130.
Acknowledgements
This research is supported by a grant from the National Science Council of Taiwan (grant no. NSC 96-2520-S-003-011).
