Chapter Ⅲ METHOD
3.1. Research hypotheses
PHASE 1
For using affective computing technique to evaluate and compare the attention score,
affective experiences and cognitive load of learners in digital-game based learning and traditional static e-learning environments, the attention in ARCS model was measured by NeuroSky, the affective experiences was measured by emWave and the cognitive load was measured by eye-tracker. Academic achievement was measured by pre-tests (Force Concept Inventory, FCI) and post-test (Mechanics Baseline Test, MBT). In addition, the following two different learning environments were designed: (1) Digital Game-Based Learning, learners studied the physics problem by SURGE physics game; (2) traditional static e-learning, the learners studied by text description, coordinates and formula. To fairly compare how learning environments affect learning attention, emotions, strategy and, the two learning environments had the same learning content and learning objectives, that is, the same learning materials are presented in different methods. Table 6 shows the hypotheses and reference. Figure 1 shows the relationship framework in PHASE 1.Table 6.Hypotheses and reference in PHASE 1
Hypotheses Reference
H1. DGBL group have better attention score than traditional static e-learning group and have significant difference.
(Derbali & Frasson, 2010)
H2. DGBL group have better affective experiences than traditional static e-learning group and have significant difference.
(Baldaro et al., 2004; e-learning group and have significant difference.
(Huang, 2011)
H4. DGBL group have better academic achievement than traditional static e-learning group.
H1. DGBL group have better attention score than traditional static e-learning group and have significant difference.
Brain wave measurement tool manufactured by Neurosky was adopted, which was one type of non-invasive brain wave measurement instrument. Derbali and Frasson (2010) investigated players’ motivation during serious game play. It is based on a theoretical model of motivation (John Keller’s ARCS model of motivation) and EEG measures, and the results showed that power spectral analysis showed EEG waves patterns correlated with increase of motivation during different parts of serious game play.
H2. DGBL group have better affective experiences than traditional static e-learning group and have significant difference.
Ravaja, Turpeinen, Saari, Puttonen, and Keltikangas-Jarvinen (2008) found the game events did in fact lead to emotion state. This indicated that physiological and emotional changes take place while playing games (Baldaro et al., 2004). Based on these studies, we can assume Digital Game-Based Learning can increase learners’ positive emotion.
H3. DGBL group have more cognitive load than traditional static e-learning group and have significant difference.
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, Huang (2011) argues that the target online GBLE might overload learners’ cognitive capacity thus lead to a fairly unsatisfactory learning experience.
H4. DGBL group have better academic achievement than traditional static e-learning
group.
Based on 32 empirical studies, Vogel et al. (2006) reported that interactive games were more effective than traditional classroom instruction on learners’ academic learning gains and cognitive skill development.
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). In this study, 1 easy problem and 1 difficult problem in post-test were selected from Mechanics Baseline Test (MBT). The test covers concepts in basic principles (Newtons' First, Second, and Third Laws, superposition principle) and special forces (gravity and friction).
Table 7.Hypotheses and reference in PHASE 2 PHASE 2
H1. High working memory capacity learning style group knew better where to look the key factors than low working memory capacity learning style group.
H4. Successful problem solvers are able to recognize and concentrate on relevant cues more than unsuccessful problem solvers
(Tsai et al., 2012)
H5. Successful problem solvers inspected the factors in a different (Tsai et al., 2012)
1.Active/Reflective
3.Percentage of total fixation (PTF)
4.Percentage of time fixated related to total fixation duration (PTFRTFD) Visual Attention (eye movement)
Figure 2.Research framework in PHASE 2
Before analyzing the problem solving strategy in each group, several LookZones were defined. For analyzing the attention distributions on different medium components (look-zones) on post-test problem, 4 eye movement measures, Hot Zone images and sequential analysis were used. Table 7 shows the hypotheses and reference. Figure 2 shows the relationship framework in PHASE 2.
H1. High working memory capacity learning style group knew better where to look the key factors than low working memory capacity learning style group.
Graf et al. (2008) investigated the relationship between learning styles, in particular, those pertaining to the Felder–Silverman learning style model and working memory capacity, one of the cognitive traits included in the cognitive trait model. The identified relationship is derived from links between learning styles, cognitive styles, and working memory capacity which are
based on studies from the literature. As a result, they demonstrated that learners with high working memory capacity tend to prefer a reflective, intuitive, and sequential learning style whereas learners with low working memory capacity tend to prefer an active, sensing, visual, and global learning style. In addition, previous research indicates that individuals with higher levels of working memory capacity perform better on learning tasks because they have more cognitive resources (Daneman & Carpenter, 1980; R.E. Mayer, 2001) 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). According these research findings, this paper presumed that high working memory capacity learning style group knew better where to look the key factors than low working memory capacity learning style group.
Table 8.The relationship between learning styles, working memory and learning performance
Working memory capacity Learning style Learning performance
High Reflective
Intuitive Sequential
High
Low Active
Sensing Visual Global
Low
H2. DGBL group knew better where to look the key factors than traditional static e-learning group.
Educators have highlighted the importance of problem solving competence. Consequently, many approaches have been proposed to enhance such competence. Liu et al. (2011) proposed an
students’ feedback and activity logs, this 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.
H3. Science major group knew better where to look the key factors than non-science major group.
F. Y. Yang et al. (2013) investigated university learners’ visual attention during a PowerPoint (PPT) presentation on the topic of “Dinosaurs” in a real classroom. Eye movement indicators, such as total time spent on the interest zone, fixation count, total fixation duration, percent time spent in zone, etc., were abstracted to indicate their visual attention. The results showed that the effect of prior knowledge was evident mostly in the text zones. Although in most cases there was no difference in the viewing time of the pictures between the different background groups, further analyses of the densities of fixations revealed that the earth-science majors’ students knew better where to look. Finally, the analysis of the saccade paths showed that the inter-zone scanning, indicating integration of different modes of presentation was evident during the PPT presentation. As expected, the earth-science majors’ students performed generally better than did the non-earth-science majors learners.
H4. 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. The unsuccessful problem solvers, with lower levels of metacognitive strategies, have difficulties in comprehending the goal of a task, distinguishing relevant factors from irrelevant (Tsai et al., 2012).
H5. Successful problem solvers inspected the factors in a different pattern from unsuccessful problem solvers.
Tsai et al. (2012) found a significant difference in students’ scan sequences among factors for solving the problems between successful and unsuccessful problem solvers. Successful problem solvers tend to shift their visual attention from irrelevant factors to relevant factors, while unsuccessful problem solvers tend to shift their visual attention from relevant to irrelevant factors and to the problem statement. It is obvious that the two groups shift attentions in opposite directions.