Many educators agree that students' optimal learning should include reflective thinking and minimize rote memorization (e.g., Bloom, 1956; Bransford, Sherwood, Vye, & Rieser, 1986; Halpern, 1998; Snow, 2002). Nevertheless, teachers in many countries encourage students to learn by rote, and several researchers have touted its effectiveness in Asian cultures (e.g., Gow, Balla, & Hau, 1996). Though learning and achievement can differ substantially across countries (e.g., Gow et al., 1996; Politzer & McGroarty, 1985; Tharp, 1989), relatively little is known about the processes contributing to these differences. For instance, how various country-level factors and their links with students' learning strategies might influence student achievement remain open questions. Thus, this study examined how country properties (country income, inequality, and cultural values) and learning strategies (rote memorization, transfer through elaboration, and metacognition) might affect the academic achievement of 15-year-olds in 34 countries. Findings can show whether country properties or learning strategies are linked to student achievement and whether these learning strategies are universal or differ systematically along countries' economic or cultural dimensions. Hence, the results can help students choose suitable strategies to learn more.
Learning strategies refer to the mental processes which help learners understand new information. Past studies have shown that students who use learning strategies that are more sophisticated typically show higher achievement (e.g., Lau & Chan, 2001; Valentine, DuBois & Cooper, 2004; Vermunt & Vermetten, 2004). For example, seventh grade students who more often used cognitive and metacognitive strategies, such as summarization and reading monitoring, scored higher in reading (Lau & Chan, 2003). Likewise, compared to regular students, gifted students more often used self-regulated learning strategies (e.g., organize information, transform information, seek peer assistance, review notes; in Zimmerman and Martinez-Pons's (1990) study of 5th, 8th and 11th graders). Thus, some strategies promote learning more effectively than others.
1. Memorization
Although researchers have devised various classification systems for learning strategies (e.g., Kang, 1997; Oxford, 1990), nearly all of them include surface cognitive strategies, deep cognitive strategies (Gall, Gall, Jacobsen, & Bullock, 1990; Pintrich, 1988), and metacognitive strategies (Yildirim, 1998). Surface cognitive strategies refer to memorization skills, of which the most common is rehearsal (which includes repeating and reciting, e.g., Craik & Watkins, 1973; Nolen, 1988; Graham & Golan, 1991). As memorizing information adds to one's knowledge base, especially during its early development, strategies that enhance memory performance are important for learning (Bloom, 1956; Kantrowitz & Wingert, 1991). For example, memorization of mathematics rules fosters the mastery of basic mathematics skills, which provide a foundation to help students to solve more complex mathematics problems (Kantrowitz & Wingert, 1991). However, some researchers have argued that rote memorization might make delayed retrieval more difficult, especially in complex tasks (Czuchry & Dansereau, 1998). Thus, relying too much on rote memorization can adversely affect academic achievement (e.g., Isaacs & Carroll, 1999). Therefore, researchers from different cultures tend to stress the importance of minimizing rote learning and emphasizing deeper level learning strategies (e.g., Morrison & Tang, 2002; Skuy, Fridjhon, & O'Carroll, 1998). One possible way of increasing learning is to transfer previously learned information to new situations through elaboration, a deep cognitive strategy.
2. Elaboration
Elaboration strategies are cognitive processes that link new and old information (e.g., paraphrase, compare and contrast concepts, use personal examples, and so on). These strategies create more ways to access or recall the linked information. Furthermore, elaboration strategies help students process information more deeply and flexibly transform it to facilitate successful problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Mayer, 1980; Stein, Morris, & Bransford, 1978). For instance, Chi, de Leeuw, Chiu, and Lavancher (1994) showed that self-explanation enhanced declarative knowledge acquisition of 8th graders. Likewise,Wong, Lawson, and Keeves (2002) demonstrated that selfexplanation facilitated 9th graders' problem solving in mathematics. Students who use elaboration strategies tend to learn more and develop more coherent understanding of concepts, compared to those who rely on rote memorization (e.g., BouJaoude, 1992). Thus, researchers often recommend teaching elaboration strategies to students to improve their problem solving and to facilitate application of their knowledge to new problems (transfer; Bransford et al., 1986; Halpern, 1998). However, facilitating transfer through elaboration is difficult to achieve and requires effort and concentration, as shown in many studies of problem-solving (e.g., Bransford et al., 1986; Halpern, 1998). When teachers do not give strong prompts or do not explicitly demonstrate how to apply previously learned knowledge and strategies to new situations, such transfer often fails to occur (Bransford et al., 1986; Garner, 1990). Indeed, even faced with problems that are conceptually similar to earlier ones, students without specific transfer training rarely use elaboration strategies to transfer previously learned knowledge (Bransford et al., 1986; Halpern, 1998). Perhaps one key to the problem of transfer is metacognition (Bransford et al., 1986; Halpern, 1998).
3. Metacognition
Metacognition is the knowledge and regulation of one's cognition (Hacker, 1998). Specifically, metacognitive
knowledge is the understanding of one's memory and learning, and regulation refers to control and manipulation of one's cognition. Knowledge of cognition is a prerequisite for regulation of cognition for many researchers, (e.g., Baker, 1989). Metacognitive strategies involve planning and self-evaluation during learning, and they facilitate mastery of complex skills, such as language (Oxford et al., 1989). Metacognitive strategies do so by helping students identify specific learning goals, filter new information, and retrieve relevant information to fill in the knowledge gaps (Pichert & Anderson, 1977). Identifying learning goals and recognizing the required information can enhance deeper processing of target information and facilitate transfer. Past intervention studies have shown that metacognitive instruction and practice in reflection, planning, and evaluation promote performance (Kincannon, Gleber, & Kim, 1999; Teong, 2003). For example, Zemira and Kramarski (2003) found that 8th graders who received metacognitive training performed better on mathematical tasks than their counterparts exposed to examples of problem solutions. Furthermore, this metacognitive training advantage persisted one year after the intervention. However, learners often fail to use their metacognitive skills, resulting in incorrect answers to problems. As metacognitive processing requires extra cognitive demands, it is effortful. Thus, if students perceive a task as
unimportant, they often do not use their metacognitive skills (Gardner, 1990). Also, many students do not use them if the task instructions do not ask them to do so (Gardner, 1990). Teaching metacognitive skills to students can also be difficult due to metacognition's covert nature and student reluctance to changing their learning routines (Conner & Gunstone, 2004). As learning of metacognition is relatively covert, objective evaluation of these learning processes is arduous. Teaching metacognition is further complicated by learners' automatization of their own strategy use and reluctance in changing their roles to become active learners who
reflect on their knowledge and actions.
Though cognitive strategies and metacognitive strategies are distinctive, they are often used together and enhance each other (Kang, 1997). Because they are relatively important to different aspects in the learning process, we examined the links between students' academic achievement in difference countries and their reported uses of these three learning strategies: rote memorization, transfer through elaboration, and metacognition.
Apart from these individual level associations of learning strategies, a country's economy and cultural values
provide a broad context in which students learn. As such, differences in economies and cultural values might affect students' academic achievement. Countries with higher real gross domestic product (GDP) per capita tend to foster higher student achievement both directly through education spending (e.g., books, teacher training, better curricula) and indirectly through higher nutritional standards or better health care (Heyneman & Loxley, 1983; Baker, Goesling, and Letendre, 2002; UNICEF, 2001). Students in countries with more equal distribution of resources (e.g., income, rich schoolmates, certified teachers) also scored higher on measures of achievement than students in less equal countries due to diminishing marginal returns or homophily (Chiu, in press; Chiu & Khoo, 2005). Consider a thirsty woman and two glasses of water. She greatly values the first glass of water and drinks it all. Her thirst quenched, she does not value the second glass of water as much and does not finish it. This relatively lower value of the additional resource is diminishing marginal returns (Mankiw, 2004, p. 273). Hence, poorer students typically benefit more from an extra book than richer students do.With greater equality, poorer students have more resources and benefit more from them, resulting in higher education outcomes overall (Chiu & Khoo, 2005). As people prefer to interact with others of similar socio-economic status (homophily; McPherson, Smith-Lovin, & Cook, 2001), greater equality within a country might encourage greater cooperation among students, which would also result in higher overall academic performance (Chiu, in press).
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