Chapter Ⅳ DATA ANALYSIS
4.11 Discussion
PHASE 1
DGBL group have more cognitive load in the proof of affective computing technique
This study used affective computing technique to evaluate and compare the motivation
score, affective experiences and cognitive load of learners in digital-game based learning and traditional static e-learning environments. The results showed that DGBL group have more cognitive load than traditional static e-learning group, learners’ cognitive capacities were in high demand in GBLE (Huang, 2011). Many studies have investigated the effects of Digital Game-Based Learning (DGBL) on learning and motivation (Erhel & Jamet, 2013). Virvou et al.(2005) also concluded that computer games could promote motivation, especially for at-risk students or for students with motivational problems. Many studies proved that learners got better attention and learning motivation in DGBL environment by questionnaire, but we couldn’t prove this argument by the proof of affective computing technique. In addition, Clark et al. (2011) found that majority of the eighth and ninth grade students in Taiwan and United States liked their affective experiences using SURGE game. SURGE game is a serious educational game not a commercial game, so maybe college student couldn’t feel interesting like eighth and ninth grade students.
DGBL group have better academic achievement but have no significant differences
The major reviews of digital games seeking to explore the issue of academic achievement have reported contradictory or ambiguous findings (Papastergiou, 2009). For instance, in a
student learning can be found between learning environments that involve games and those without game elements (Leonard A. Annetta et al., 2009; Papastergiou, 2009). For explaining this result, Huang (2011) found that learners’ cognitive capacities were in high demand in the online GBLE. Since researchers on cognitive load have concluded that an overloaded cognitive capacity can de-motivate learners. We also found the proof of affective computing technique in this study.
Thus, this paper argues that the target DGBL might overload learners’ cognitive capacity thus lead to a fairly unsatisfactory learning experience.
PHASE 2
The goal of PHASE 2 is to compare the science problem solving strategy of learners in different working memory capacity learning style (active vs. reflective, sensing vs. intuitive, sequential vs. global), learning environments (DGBL vs. traditional static e-learning), major (non-science vs. science) and performance (low performance vs. high performance). And several arguments were found as follow.
High working memory capacity learning style group knew better where to look the key factors.
We found that high WMC group paid more visual attention not only in key factors but also in text. This suggested that high WMC group have more cognitive resources (Daneman &
Carpenter, 1980; Mayer, 2001) and knew better where to look the key factors. It is likely that working memory capacity affects cognitive efficiency due to the processing and storage requirements necessary to solve mental problems (Hoffman & Schraw, 2009).
DGBL group knew better where to look the key factors
DGBL group knew better where to look the key factors and correct option than traditional
static e-learning group, so DGBL can help students gain skills to solve problems. Liu et al. (2011) proposed simulation game is helpful in creating a flow experience in which the students are motivated to apply trial-and-error, learning-by-example, and analytical reasoning strategies to learn the computational problem solving skills. Renkl, Atkinson, Maier, and Staley (2002) also found that using the learning-by-example strategy in the initial problem solving stage can help students gain skills to solve problems. Thus, DGBL has been argued that digital games provide a meaningful framework for solving problems (L. A. Annetta, 2008), since students are placed in scenarios in which they must synthesize diverse information and analyze strategies, leading to a greater understanding of the causal links between decision-making behaviors (Ebner &
Holzinger, 2007). Therefore, digital games can be seen as a good tool for understanding the link between cause and effect (Kiili, 2005).
Science major group couldn’t know better where to look the key factors, but Non-science majors’ learners need more clues to solve problem
F.-Y. Yang et al. (2013) found the earth-science majors’ students performed generally better than did the non-earth-science majors learners in earth science class. They supported that prior knowledge affects concept learning in the processes of both information decoding and integration. And the earth-science students who had better concept gains knew better where the earth science PPT to look. However, we couldn’t prove any group could know better where to look the key factors or correct option. However, we found that Non-science majors’ learners need more clues to solve problem so they spend more visual attention on each LookZone.
group, so successful problem solvers are able to recognize and concentrate on relevant cues more than unsuccessful problem solvers. The successful problem solvers, with higher levels of metacognitive strategies, are able to recognize and concentrate on relevant cues in a problem-solving learning task (Tsai et al., 2012).
Successful problem solvers inspected the factors in a different pattern from unsuccessful problem solvers.
This study found that participants in the low performance group read the key factor and incorrect option repeatedly which may indicate that they have difficulties in deducing the correct option from key factor. In addition, high performance students did not pay much attention to the problem title, but low performance students did. This may imply that low performance students have difficulties in comprehending the problem. This suggested the successful problem solvers, with higher levels of metacognitive strategies, are able to recognize and concentrate on relevant cues in a problem-solving learning task. The unsuccessful problem solvers, with lower levels of metacognitive strategies, have difficulties in comprehending the goal of a task, distinguishing relevant factors from irrelevant factors, and concentrating on handling the relevant factors to solve a problem (Tsai et al., 2012).
Learning style couldn’t be measure by physiology signals when learners learning
For finding the physiology signals significant difference between learning style group (active vs. reflective, sensing vs. intuitive and sequential vs. global), paired t-test was used. The dependent variables were the physiology signals during learning including learning attention (AT), affective experiences (PE) and cognitive load (TFD, NF, AFD, PVT, FSP). The results showed that all physiology signals didn’t have significant difference with each learning style
group, so these physiology signals when learners learning couldn’t explain learners’ learning style. This study only found that physiology signals significant difference in cognitive load (TFD, NF, AFD and PVT) between static e-learning and DGBL environment. The finding was shown in Table 50.
Table 50.physiology signals significant difference during learning between each group
Learning attention Affective experiences Cognitive Load
AT PE TFD NF AFD PVT FSP
AT = attention score, PE = occupied percentage of positive, TFD = total fixation duration, NF = number of fixations, AFD = average fixation duration, PVT = percentage of viewing time, FSP = frequency of saccade path.
＊ denotes significant difference in this variable
Learning style (active vs. reflective) could be measure by eye movement variables when learners solving problem.
To analyze the attention distributions on different look-zones between learning style (active vs. reflective), this study used 4 eye movement variables including percentage of time spent in zone (PTSZ), fixation count (FC), percentage of total fixation (PTF), percentage of time fixated related to total fixation duration (PTFRTFD). This study found that reflective learning style group paid more attention distributions (PTSZ, FC, PTF and PTFRTFD) on text and key factor
problem but active group only focus on the correct option. According to this finding, this study considered that learning style (active vs. reflective) could be measure by eye movement variables (PTSZ, PTF and PTFRTFD).
Table 51.physiology signals significant difference when solving problem between each group Attention distributions
PTSZ FC PTF PTFRTFD
Learning Environment Static vs. DGBL Learning Style
Active vs. Reflective * * *
Sensing vs. Intuitive Sequential vs. Global Major
Non science vs. science * *
Problem 1 performance Low vs. High
Problem 2 performance Low vs. High
PTSZ = percentage of time spent in zone, FC = fixation count, PTF = percentage of total fixation, PTFRTFD = percentage of time fixated related to total fixation duration.
＊ denotes significant difference in this variable
Chapter Ⅴ
CONCLUSION AND IMPLICATIONS
According to the experiment results, Section 5.1 showed 5 findings. In addition, the education implications were showed in section 5.2. Finally, we proposed 2 issues for future studies in section 5.3.
5.1 Conclusion
Digital Game-Based Learning (DGBL) is thought to be an effective tool for learning.
Games that encompass educational objectives and subject matter are believed to hold the potential to render learning of academic subjects more learner-centered, easier, more enjoyable, and more interesting. Although games are believed to be motivational and educationally effective, the empirical evidence to support this assumption is still limited and contradictory.
For verifying the possibility of playing digital game to learn the Newton's laws of motion, this study used a quasi-experimental design to examine the effectiveness of Digital Game-Based Learning (DGBL) and traditional static e-learning on students’ learning motivation, affective experiences, cognitive load, academic achievement and problem solving skills. In phase 1, when student learning, their physiology signals were measured by affective computing technique for analyzing their learning states. After learning, learners took a posttest to find the difference in academic achievement between DGBL and static e-learning. In phase 2, this study found that there had difference problem solving strategy between different working memory capacity learning style (active vs. reflective, sensing vs. intuitive, sequential vs. global), learning
motivation score, affective experiences and cognitive load of learners in digital-game based learning and traditional static e-learning environments. The results showed that DGBL group have more cognitive load in the proof of affective computing technique. Although many studies proved that learners got better attention and learning motivation in DGBL environment by questionnaire, but we couldn’t prove this argument by the proof of affective computing technique. In addition, college student couldn’t feel interesting like eighth and ninth grade students in SURGE game, a serious educational game.
The second finding, DGBL group have better academic achievement but have no significant differences. Base on the proof of cognitive load, this paper argues that the target GBLE might overload learners’ cognitive capacity thus lead to a fairly unsatisfactory learning experience.
The third finding, high working memory capacity learning style group, DGBL group and high problem-solving group knew better where to look the key factors. In addition, science major group couldn’t know better where to look the key factors, but we found that Non-science majors’
learners need more clues to solve problem.
The fourth finding, Successful problem solvers inspected the factors in a different pattern from unsuccessful problem solvers. This suggested the successful problem solvers, with higher levels of metacognitive strategies, are able to recognize and concentrate on relevant cues in a problem-solving learning task. The unsuccessful problem solvers, with lower levels of metacognitive strategies, have difficulties in comprehending the goal of a task, distinguishing relevant factors from irrelevant factors, and concentrating on handling the relevant factors to solve a problem.
The fifth finding, learning style (active vs. reflective) could be measure by eye movement variables when learners solving problem. This paper found that when learners solving problem, reflective group try to search more cues in the problem but active group only focus on the correct
option. According to this finding, this study considered that learning style (active vs. reflective) could be measure by eye movement variables (PTSZ, PTF and PTFRTFD). However, all physiology signals during learning didn’t have significant difference with each learning style group. That meant that physiology signals including learning attention, affective experiences and cognitive load couldn’t explain learners’ learning style.
5.2 Implications
5.2.1 The necessary feature of DGBL environment
In this study, we only found that DGBL group had more cognitive load, but had no significant differences in learning motivation, affective experiences, and academic achievement.
This study considered that maybe we didn’t provide the enough feature of DGBL environment, specifically for appropriate challenges (Liu et al., 2011) and full learning time (Y. T. C. Yang, 2012).
Appropriate challenges
Liu et al. (2011) found the students who felt bored in the simulation game only learned to solve the problem at a superficial level. Educators may need to apply strategies to engage the students in in-depth reasoning of their solutions. For instance, the teacher may increase the appropriate challenges enabling learners to experience a feeling of self-efficacy, so that the student may need to analyze the solution critically in order to solve the problem.
Full learning time
The DGBL strategy was clearly effective in promoting students’ problem solving skills, but
need to teach the amount of time required to fully evaluate the learning effect.
5.1.2 The relationship between affective computing and learning style
To analyze the attention distributions on different look-zones between learning style (active vs. reflective), this study used 4 eye movement measures and HotZone image. We found that high working memory capacity learning style group knew better where to look the key factors, especially in active/reflective group. Thus, this study considered that learning style (active vs.
reflective) could be measure by eye movement variables (PTSZ, PTF and PTFRTFD). Maybe educator can measure leaners’ learning style by else affective computing technique. This is helpful for developing the adaptive learning system.
5.3 Future Study
Since Affective Computing was proposed, there has been a burst of research that focuses on creating technologies that can monitor and appropriately respond to the affective states of the user. Because this new Artificial Intelligence area, computers able to recognize human emotions in different ways. In this study, we used the affective computing technique to evaluate the performance of Digital Game-Based Learning. This is a new approach in Digital Game-Based Learning issue. We propose several issues for future studies as follow.
Increase else physiology signals to measure learning state of learners
For using affective computing technique to evaluate and compare the attention score, affective experiences and cognitive load of learners in digital-game based learning environments in this study, the attention was measured by brain wave, the affective experiences was measured by heartbeat and the cognitive load was measured by eye movement. The future
studies may increase else physiology signals to measure learning state of learners. For instance, facial expression detection (C. H. Yang, 2012), verbalization (Leony, Muñoz-Merino, Pardo, &
Delgado Kloos, 2013), pressure mouse (Leony et al., 2013).
Provide better DGBL environment
This study considered that maybe we didn’t provide the enough feature of DGBL environment, specifically for appropriate challenges (Liu et al., 2011) and full learning time (Y. T.
C. Yang, 2012). The future studies may increase the appropriate challenges enabling learners to experience a feeling of self-efficacy, and need to teach the amount of time required to fully evaluate the learning effect.
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