Learning styles
In our ever-evolving society, it has become increasingly apparent that ‘each student plays an integral role in his individual learning experience’ (Weinstein and Hume 1998, p. 6). While teachers prepare lessons and present information, it is ultimately the student who interprets, understands, and retains such information in a way that permits facile retrieval and recall for application. In order to perform these tasks, students employ different preferences for and habits of sense making. A learning style is defined as the way in which a person ‘begins to concentrate on, process, internalize, and remember new and difficult academic information’ (Hall 2008, p. 6). Learning styles therefore indicate how the student ‘perceives, interacts with, and responds to the learning environment’ (Hall 2008, p. 6).
Although the concept of learning styles appeared as late as the 1970s, there have been many different ways to approach this concept. Nevertheless, it is fairly reasonable to classify learning styles from two main perspectives. One pertains to individual processing of information (e.g., auditory, visual, and kinesthetic; see Pashler et al. 2009); the other pertains to individual relationship with other learners (i.e., competitive and cooperative; see Johnson and Johnson 1989). Competitive and cooperative as learning styles are the focus of the present research.
In a classroom setting, the competitive learner implements an individualistic personal learning plan and employs learning strategies that enable the learner to achieve learning goals (Johnson and Johnson 1989). Competitive learners often see all students in the class as working towards the same goal of learning. However, the competitive learner wants to not only become the first in achieving that goal but also achieve that goal in a more outstanding manner than the peers (Montgomery and Groat 1998). Consequently, competitive learners often see academic performance as a system of few winners and many losers. The chief benefit of the competitive learning style is the motivation that stimulates great learning effort (e.g., Burguillo 2010). On the other hand, some educational psychologists have argued that competitive learning may not be desirable because it produces high stress, low self-concept (in the case of failure), cheating, and aggression in the classroom (Johnson and Johnson 1989).
A composite variable was constructed in PISA that measures competitive learning style. The composition of the competitive learning variable includes the following: (a) I would like to be the best in my class in mathematics; (b) I try very hard in mathematics because I want to do better on the exams than the other students; (c) I make a real effort in mathematics because I want to be one of the best; (d) in mathematics, I always try to do better than the other students in my class; and (e) I do my best work in mathematics when I try to do better than others. This composite appears to sufficiently catch the essence of the competitive learning style (see Montgomery and Groat 1998).
A foil to the competitive learner, the cooperative learner tends to enjoy working in a group or team setting. Cooperative learners, in an effort to achieve a certain goal of learning, often break down tasks into specific roles which are then assigned to members of the group (Murphy and Alexander 2006). Individual members subsequently accomplish their specific tasks and then share their findings with the whole group. The cooperative learner is far less concerned with ‘being number one’ than the competitive learner, and cooperative learning puts significant emphasis on the group dynamic and on the progress of the group as a whole. Many educational psychologists have praised the enhanced exchange of information, knowledge, and skills as well as the interdependence and individual accountability, all forged and fostered by a cooperative learning environment (e.g., Slavin 1980; Weinstein and Hume 1998). As its chief disadvantage, the cooperative learning environment is difficult to establish and many teachers struggle with the implementation in their classrooms (e.g., Gillies and Boyle 2010).
A composite variable was constructed in PISA that measures cooperative learning style. The composition of the cooperative learning variable includes the following: (a) in mathematics, I enjoy working with other students in groups; (b) when we work on a project in mathematics, I think that it is a good idea to combine the ideas of all the students in the group; (c) I do my best work in mathematics when I work with other students; (d) in mathematics, I enjoy helping others to work well in a group; and (e) in mathematics, I learn most when I work with other students in my class. Arguably, these variables are sound indicators of the cooperative learning style (see Murphy and Alexander 2006).
In general, cooperation is more effective for higher-order tasks (e.g., problem solving), whereas competition is more effective for lower-order tasks (e.g., rote learning) (e.g., Johnson et al. 1981; Johnson et al. 1980). These researchers concluded in their meta-analyses that students completing academic tasks under cooperative conditions tend to outperform students completing academic tasks under competitive conditions. Later on, another meta-analysis replicated this conclusion (Qin et al. 1995).
Johnston (1997) argued that, to promote academic success, educators need to understand how students differ in their approaches to learning tasks and use that understanding to create strategies for learning. Johnson et al. (2000) examined eight cooperative learning methods and found that all of them indicate significantly positive effects on academic achievement. Specific to mathematics education, Bell (1989) asserted that, to increase mathematics performance, how students learn in mathematics must be analyzed. Hall (2008) also asserted that learning styles are a significant determinant of mathematics performance. In general, review of educational research has indicated a positive relationship between learning styles and mathematics achievement (Middleton and Spanias 1999). Overall, it is important to investigate learning styles as a critical variable in explaining mathematics performance.
People are not born to share a genetic predisposition in terms of the learning approach; instead, they learn how to conduct learning through a socialization process that is unique to each culture (Nelson 1995). Of course, some learning styles can be common to students around the world. For example, if tests require students mainly to reproduce knowledge, then memorization dominates their learning styles (Au and Entwistle 1999). But, other learning styles can be very culturally specific. Singleton (1991) stated that every culture has unstated assumptions about people and how they learn and these assumptions invisibly guide the educational process in that culture. According to Ma et al. (2013), the research literature that attempts to explain East Asian academic success is comprehensive, but with one weakness that speaks to the lack of attention to the way that East Asian students manage their learning in relation to their academic success. They argued that how students learn may hold important clues to the superior academic performance of East Asian students. Therefore, investigating learning styles in an international context has important implications for improving mathematics education in the USA.
East Asian success
Many researchers have attempted to examine student, family, teacher, and school factors salient in contributing to academic achievement in East Asia (see Ma et al. 2013). One popular perception is the amount of emphasis on effort over ability in East Asian cultures (Stevenson and Stigler 1992). Meanwhile, other researchers have attributed ability to academic success in East Asia (Eccles and Wigfield 1995). More recent studies have primarily focused on attitudes rather than behaviors, showing that students in East Asian countries have a significantly higher level of interest and motivation in learning than students in other highly developed countries (Bybee and McCrae 2011; Liu et al. 2006). Based on results from PISA and TIMSS, Watkins (2000) cautioned about the use of affective factors to explain disparities in mathematics performance between the USA and East Asia because students in some of the best-performing East Asian countries indicate low self-esteem and negative attitudes towards mathematics. Studies of mathematics performance among East Asian students also indicate parental education and expectation as primary determinants correlating positively to mathematics performance (Leung 2010).
Researchers who studied the high academic achievement of Japanese students claimed that important teacher and school variables should be considered when explaining East Asian academic success (Woodward and Ono 2004). East Asian schools are notable for large class sizes with a typical middle school class containing an average of 50 students. Watkins (2000) found that large classes in East Asian schools contribute to their students' superior performance in mathematics, reasoning that a large class size permits a widespread influx and circulation of ideas and insights that can facilitate deep understanding of mathematics. Superior performance in mathematics of East Asian countries has also been attributed to high national standards and expectations (Valverde and Schmidt 2000) and mathematics content that emphasizes depth rather than breadth (Kaya and Rice 2010). Some researchers noticed that East Asian students are very respectful toward teachers and maintaining an excellent relationship with teachers can be critical to learning (Stevenson and Stigler 1992), whereas other researchers warned that such an elevated level of respect can interfere with learning when students do not feel comfortable questioning what teachers say (Jeynes 2008).
In addition, a high societal expectation is always placed on teachers to be highly skillful in curriculum and instruction to help students learn and succeed in East Asian countries. Ma's (1999) influential study indicated that Chinese teachers have a much deeper understanding of mathematics content than American teachers. Other studies based on classroom videos have also shown that East Asian teachers present clearer lessons and engage students more in the learning process (Jacobs and Morita 2002; Leung 2005). Professional development may have played a central role in East Asian teachers' success (Fernandez et al. 2003).
The quality of instruction is considered as another prime contributor to the superior mathematics performance of East Asian students. Mathematics curriculum established by East Asian countries mandates that teachers do not simply place emphasis on the development of lower-level cognitive skills; instead of instructing students through the medium of rote memorization, East Asian mathematics teachers are expected to promote higher level critical and analytical thinking in their classes (Liu et al. 2006). This encouragement of higher-order thinking skills in East Asian mathematics classes may enable students to gain a more in-depth understanding of mathematics and apply their knowledge in more novel ways. East Asian mathematics teachers are also much more group-oriented than mathematics educators in other countries. In East Asian countries, it is not uncommon for a mathematics teacher from one school to observe and critique the teaching practice of a mathematics teacher from a different school, and this group teaching dynamic may have enabled East Asian mathematics educators to better refine their teaching practices and further tailor their teaching to the individual needs of their students (Watkins 2000). In the research literature, there are other accounts for East Asian success as the result of high levels of pressure to perform well on exit exams, additional outside schooling, and strong family support (e.g., Bray 2010; Cave 2001; Watanabe 2000).
According to Ma et al. (2013), the research literature on East Asian academic success, even though comprehensive, lacks attention to whether academic success of East Asian students correlates with how they manage their learning process. They began this important line of inquiry by exploring the success of six top-performing East Asian countries (regions) in PISA 2009 (Hong Kong, Japan, Korea, Shanghai, Singapore, and Taipei). Their search for reasons for success concerned about student academic behaviors such as the use of learning strategies and meta-cognitive skills among students and school climatic attributes such as disciplinary climate among schools. They found striking total consistencies across all academic areas (reading, mathematics, and science) and across all countries (regions) that highly successful students were skillful users of advanced learning strategies in their learning and knew how to utilize meta-cognitive skills in the process of their learning. They reported that these positive effects are not only comprehensive but also the strongest among all statistically significant student academic behaviors. In line with Ma et al. (2013), the present research aims to examine the relationship between learning styles, another important aspect of how students manage their learning, and mathematics performance among American and East Asian students.