CHAPTER Ⅰ INTRODUCTION
1.1 Research motivation
Digital Game-Based Learning (DGBL) is thought to be an effective tool for learning (Kebritchi & Hirumi, 2008) that can promote enhanced learning experiences (T. M. Connolly, Stansfield, & Hainey, 2007) and student motivation (Papastergiou, 2009). DGBL can be defined as “the use of a computer game-based approach to deliver, support, and enhance teaching, learning, assessment, and evaluation” (T. M. Connolly et al., 2007). There is also widespread acknowledgment of the advantages that the use of games has in elementary and secondary education (Ebner & Holzinger, 2007). 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 (Marina, 2009).
The education games and commercial games are different. Many popular commercial games offer interesting pedagogical opportunities for physics education with their focus on
physics games provide students with a strong intuitive ‘feel’ for physics concepts, they don’t appear to (and were not designed to) help students make the leap from tacit understanding to more formalized knowledge (Clark et al., 2011). Game-based experiences thus appear to require scaffolding in order for students to make the connections between the game and the more formalized knowledge required in a school-based context. These findings suggest that simply having players engage with physics based games is not sufficient to help them learn physics.
Many studies have investigated the effects of Digital Game-Based Learning (DGBL) on learning and motivation (Erhel & Jamet, 2013). For example, Huang, Huang, and Tschopp (2010) based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). Clark et al. (2011) checked for how similar or different are the learning and affective experiences of students playing the game in two different countries (i.e., Taiwan and the United States). In the other research, Huang (2011) found that learners’ cognitive capacities were in high demand in the online gamed-based learning. Since researchers on cognitive load have concluded that an overloaded cognitive capacity can de-motivate learners. This study found the all research used questionnaire survey to measure the attention score, the score was not an impersonal measure. Thus, the first goal of this study is to measure the attention score by
affective computing technique and compare the
attention, affective experiences and cognitive load of learners in digital-game based and traditional learning environments.However, major reviews of digital games seeking to explore the issue of academic achievement have reported contradictory or ambiguous findings (Papastergiou, 2009). A meta-analysis of students’ learning performance (Randel, Morris, Wetzel, & Whitehill, 1992)
reported ambiguous results, with the majority of studies (38) indicating no difference between game-based and traditional teaching methods 27 studies advocating game-based learning, and 3 studies supporting traditional methods of instruction. Unfortunately, the effectiveness of DGBL on students’ academic achievement is still unproven in a robust empirical research setting. Thus, the second goal of this study is to compare the academic achievement of learners in digital-game based and traditional learning environments.
Problem-solving is a 21st century skill which is essential for learning, work, and daily life (L. A. Annetta, 2008). Problem solving can be defined as the ability to find causes, find solutions, and avoid problems (Chan & Wu, 2007). This study found the 4 factors at least can affect the problem solving strategy:
(1) Learning environments
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). Although research on problem solving in DGBL has been conducted (Dickey, 2006; Robertson & Howells, 2008), its effectiveness in fostering problem solving abilities has not received sufficient attention from empirical research.
(2) Learners’ learning style
Graf, Lin, and Kinshuk (2008) investigated the relationship between learning styles and
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 want to investigate the relationship between learning style and problem solving abilities.
(3) Prior knowledge
F. Y. Yang, Chang, Chien, Chien, and Tseng (2013) investigated university learners’ visual attention during a PowerPoint (PPT) presentation on the topic of “Dinosaurs” in a real classroom.
The results showed that the earth-science majors’ students were better at information decoding and integration than non-earth-science majors’ students. The discussion points out that the interaction between types of graphics and information processing behaviors is also mediated by prior knowledge.
(4) Academic achievement
Tsai, Hou, Lai, Liu, and Yang (2012) employed an eye-tracking technique to examine students’ visual attention when solving a multiple-choice science problem. The results showed that successful problem solvers focused more on relevant key factors, while unsuccessful problem solvers experienced difficulties in decoding the problem, in recognizing the relevant factors, and in self-regulating of concentration.
Thus, the third goal of this study is to compare the science problem solving strategy of learners in different learning environments, learning style, major and academic achievement.