INDIVIDUAL MODELS
In this chapter, we explore the nature of intellectual styles, centering our discussion on the three controversial issues mentioned in chapter 1. To re-capitulate, the three issues are: (1) the overlap issue—whether different style labels represent different style constructs or whether they are similar constructs with different root words for styles; (2) the value issue—whether styles are value-laden or value-free; and (3) the malleability issue—whether styles represent traits (and thus are stable and unchangeable) or states (and thus are flexible and modifiable). The exploration is done through system-atically examining empirical findings obtained among students at various educational levels.
To accomplish this goal, we propose five questions to be answered. First, do styles overlap with one another? Second, how do students’ intellectual styles relate to their personality traits? Third, what are the roles of students’
intellectual styles in their academic performance? Fourth, how do students’
intellectual styles relate to socialization factors in their backgrounds and can students’ styles be trained? Finally, what are the roles of students’ intel-lectual styles in their behaviors? The overlap issue will be dealt with in the context of answering the first question; the value and malleability issues will be addressed in the process of answering the remaining four questions.
STYLE OVERLAP
In chapter 1, we stated that one of the major arguments of this book is that style constructs overlap with one another; but simultaneously, each style construct has its own unique characteristics. Would this assertion hold with Chapter
3
Styles Research: Student Oriented
27
research findings based on the study of student populations? The answer is affirmative.
The literature shows that the majority of research on the relationships among different styles was conducted in the last decade or so. Results from many studies are supportive of our claim about the style overlap is-sue. This research is characterized by the large number of style invento-ries involved and by the inclusion of a great variety of student populations from different parts of the world, ranging from primary school students to university students, and from nursing students to business students. Some examples of the inventories employed are Cantwell and Moore’s (1996) Strategic Flexibility Questionnaire; Pask and Scott’s (1972) original test-ing materials designed to suit holist and serialist learntest-ing strategies; Ent-wistle’s (1981) Approaches to Studying Inventory; Entwistle and Tait’s (1994) Revised Approaches to Studying Inventory; Riechmann and Grasha’s (1974) Learning Preferences Inventory; Honey and Mumford’s (1992) Learning Styles Questionnaire; Allinson and Hayes’s (1996) Cog-nitive Style Index; Kolb’s (1976) Learning Style Inventory; Dunn, Dunn, and Price’s (1979) Learning Styles Inventory; and Renzulli and Smith’s (1978) Learning Style Inventory.
Many of these style inventories appear to have very similar names. The apparent use of similar inventories for assessing the relationships among styles would, undoubtedly, give rise to some people’s questioning of the uniqueness, and therefore, the value of, the existence of each individual in-ventory. This kind of concern, although seemingly legitimate, is misplaced.
Even when some inventories have exactly the same names, each inventory is unique. For example, the last three inventories mentioned are all called the Learning Style Inventory (LSI). However, each of them assesses quite differ-ent styles. Kolb’s LSI measures an individual’s preference for perceiving and processing information. Kolb identified four adaptive learning modes that can be organized along two continua. One continuum (with one end representing concrete experience and the other abstract conceptualiza-tion) represents how one prefers to perceive the environment; and the other continuum (with one end representing reflective observation and the other active experience) represents how one processes the incoming infor-mation. Dunn, Dunn, and Price’s (1979) LSI, however, assesses an individ-ual’s preferred modes of concentration and for learning difficult informa-tion. The learning styles considered by these authors are multidimensional in nature—emotional (motivation, persistence, responsibility, structure), environmental (sound, light, temperature, design), physical (perceptual, intake, time, mobility), and sociological (peers, self, pair, team, adult, var-ied). Finally, Renzulli and Smith’s (1978) LSI identifies an individual’s pre-ferred ways of dealing with learning tasks, with each corresponding to a method of teaching, such as projects, discussion, and drill and recitation.
Empirically, investigations have shown the significant overlap among these styles inventories. For example, Fourqurean, Meisgeier, and Swank (1990) investigated the relationship between the Jungian psychological types as measured by the Murphy–Meisgeier Type Indicator for Children (MMTIC, Meisgeier & Murphy, 1987), which is constructed based on the Myers–Briggs Type Indicator (MBTI, Myers, 1962), and learning styles as measured by Dunn et al.’s (1979) Learning Style Inventory (Dunn LSI) as well as by Renzulli and Smith’s (1978) Learning Style Inventory (Renzulli LSI). Research participants were 492 ninth graders enrolled in a large metropolitan high school. Results indicated that styles from the MMTIC were related to the learning styles from both the Dunn LSI and the Renzulli LSI. For example, regarding the relationships between the MMTIC and the Dunn LSI scales, students identified as introverted intu-itive tended to indicate a strong preference for both auditory and visual presentations of information but did not prefer to learn by using a variety of learning methods. Students identified as sensing perceiving types indi-cated a dislike for learning by touching and manipulating concrete ob-jects. Also, with regard to the relationships between the MMTIC and the Renzulli scales, introverted students indicated a dislike for working on projects, simulations, or group learning, but they indicated a stronger preference for learning through lectures. The judging type of students re-vealed a preference for learning by repetition and drill, teaching games, and independent study.
In fact, with the exception of one study (i.e., Sadler-Smith, 1997), all studies in the literature demonstrated significant relationships between or among the styles investigated. For example, in a study of 207 Australian fi-nal-year nursing students, Cantwell and Moore (1998) identified “theoreti-cally consistent associations” (p. 100) between the scales in Biggs’s (1987) Study Process Questionnaire (SPQ) and those in Cantwell and Moore’s (1996) Strategic Flexibility Questionnaire. The adaptive control strategy was positively correlated with both the deep and the achieving approaches.
Similarly, a surface approach was positively correlated with the inflexible and irresolute control strategies.
Sadler-Smith (1999) conducted a study aimed at exploring the relation-ships between two style constructs: the intuition-analysis style as assessed by the Cognitive Style Index (Allinson & Hayes, 1996) and the three study ap-proaches (achieving, reproducing, and meaning) as assessed by a short ver-sion of Entwistle’s (1988) Approaches to Studying Inventory constructed by Gibbs, Habeshaw, and Habeshaw (1988). Furthermore, Sadler-Smith also added a collaborative scale. The research participants for this study were 130 second-year undergraduates taking a range of business-related degree programs. Results revealed significant relationships between the two style constructs. The analysts scored significantly higher on the meaning
ap-proach than did the intuitives, whereas the intuitives indicated a stronger preference for a collaborative approach than did the analysts.
As a final example, Atkinson, Murrell, and Winters (1990) investigated the relationships between Holland’s (1973) career personality types and Kolb’s (1976) learning styles among 169 first-year college students. Results indicate that scales from the two measures are significantly related. From the perspective of Holland’s codes, three major relationships were found.
First, Holland’s investigative students tended to be predominantly assim-ilators (40.5%), followed by divergers (23.8%) and convergers (21.4%).
Second, Holland’s artistic students tended to be divergers (57.1%) and assimilators (25%). Third, Holland’s social type of students tended to be divergers (41.5%) and accommodators (24.6%).
However, as mentioned earlier, we also have identified a study that in-volved an inventory (Riding’s Cognitive Styles Analysis) that did not result in any significant relationship with any of the other three style inventories administered. In a sample of 245 university undergraduates in business studies, Sadler-Smith (1997) tested the relationships among the styles measured four instruments: Riding’s (1991) Cognitive Styles Analysis, the Learning Preferences Inventory (LPI, Riechmann & Grasha, 1974), the Learning Styles Questionnaire (LSQ, Honey & Mumford, 1986, 1992), and the Revised Approaches to Studying Inventory (RASI, Entwistle &
Tait, 1994). After examining the correlation coefficients among the scales of the different instruments, the authors concluded that the data sug-gested some overlap between the dimensions measured by the LSQ and the RASI. However, no statistically significant relationships were identi-fied between cognitive styles (as measured by the CSA) and any of the other “style” constructs investigated.
Yet, the lack of relationship of the CSA to the other styles assessed in the aforementioned study does not mean that the CSA is not associated with styles measured by other style inventories. For instance, in Ford’s (1995) study, 38 university students were tested for field dependence/independ-ence using Riding’s (1991) computer-administered Cognitive Styles Analy-sis (CSA). Students were also taught the computerized version of Pask and Scott’s (1972) original testing materials designed to suit holist and serialist learning strategies. A computerized test was used for assessing learning per-formance. It was found that students’ holist and serialist competencies could be predicted by the CSA scores.
Why are the results obtained for the CSA inconsistent? The CSA assesses the two style dimensions (i.e., wholistic–analytic and verbal–imagery), whereas all the other three inventories used in Sadler-Smith’s (1997) study are from the family of learning strategies, as would have been classified by Riding and Cheema (1991). Nevertheless, the study of the relationships of the styles in CSA with other style construct is rather limited. Further
investi-gation should be conducted to determine the relationships between the CSA and other style inventories.
Given that almost all existing styles resulted in significant overlaps among intellectual styles, we would argue that the various intellectual styles as measured by different inventories are intricately entwined. They invariably overlap with one another to varying degrees. Yet, the degree of overlap is such that each style construct has unique value in explaining individual dif-ferences in performance and behaviors in academic settings.
STYLES AND PERSONALITY TRAITS
To address the controversial issue over whether intellectual styles are value-laden or value-free, we thoroughly examined the studies that focus on the relationships between students’ intellectual styles and their personality traits. This research indicated that some styles (e.g., field-independent style and reflective style) are consistently associated with personality traits that are normally perceived to be positive (e.g., higher levels of assertiveness, self-esteem, and moral maturity). Their polar opposite styles (e.g., field-dependent style and impulsive style) are consistently associated with the kinds of personality traits that are typically perceived to be negative (e.g., lower levels of assertiveness, self-esteem, and moral maturity). Thus, these styles are more value-laden. Still, the adaptive value of other styles (e.g., an-alytic and wholist) is more situation-specific, which makes these styles be more value-differentiated. Findings from this review indicate that while some intellectual styles are essentially value-laden, other styles are more value-differentiated. However, intellectual styles are not value-free.
Research evidence for significant relationships between intellectual styles and personality traits is abundant. In reading this literature, we found that several intellectual style constructs are frequently involved in this research: field dependence/independence, reflectivity–impulsivity, learning approach, and brain dominance (also called “mode of thinking”;
see Zhang, 2002b). Moreover, we began to see studies that are based on an integrative style model, such as Riding and Cheema’s (1991) model com-prising two style dimensions—verbal–imagery and wholistic–analytic. Ad-ditionally, we discovered that several personality traits have maintained the interest of scholars in the context of their examining the nature of in-tellectual styles. These are the Big Five personality traits (Costa & McCrae, 1985—neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness), the Big Three personality traits (Eysenck & Ey-senck, 1964—Extraversion, Neuroticism, and Psychoticism), personality traits that relate to the self (e.g., self-esteem, self-confidence, self-control, and sense of identity), personality traits as they relate to dealing with the
world (e.g., assertiveness and shyness), as well as a whole range of other personality traits. These traits include anxiety, response to threat of frus-tration and tolerance of ambiguity, locus of control, moral maturity, and sense of responsibility.
Findings from the studies that we identified in the literature suggest that field dependence/independence, reflectivity–impulsivity, learning approaches, and brain dominance are largely value-laden, whereas the two stylistic dimensions as assessed by the Cognitive Styles Analysis are value-differentiated in the context of the relationships between styles and personality traits.
The field-dependence/independence construct has been found to be significantly related to a number of personality traits, including assertive-ness (e.g., Deng, Li, & Zhang, 2000), locus of control (e.g., Leventhal &
Sisco, 1996), moral maturity (e.g., Schleifer & Douglas, 1973), responses to the threat of frustration (e.g., Campbell & Douglas, 1972), self-esteem (e.g., Bosacki, Innerd, & Towson, 1997), and sense of identity (e.g., Bhatnager &
Rastogi, 1986). Except in the case of the variable of self-esteem, in which an interaction effect of gender was found with the field-dependence/inde-pendence style dimension, for all the other personal traits, the relation-ships of field dependence/independence to other personality traits are straightforward. That is, field independence was associated with the kinds of personality traits that are conventionally perceived to be positive (e.g., higher level of assertiveness, internal locus of control, higher level of moral maturity, optimistic in the face of threat of frustration, and a better devel-oped sense of identity). On the contrary, field dependence was associated with the kinds of personality traits that are typically perceived to be negative (e.g., lower levels of assertiveness, external locus of control, lower levels of moral maturity, pessimism, and a poorly developed sense of identity).
Therefore, the results of this group of studies indicate that field independ-ence is likely to be more positively valued than field dependindepend-ence.
The relationship between field dependence/independence and self-esteem is not as straightforward. There is a gender effect. In a study of 63 sixth-grade students in a southwestern Ontario city, Bosacki, Innerd, and Towson (1997) found that field independence and self-esteem were corre-lated negatively for girls and positively for boys.
Reflectivity–impulsivity has been tested with children’s responses to the threat of frustration and with their moral maturity. Campbell and Douglas (1972) found that children who scored higher on impulsivity displayed higher levels of pessimism in the face of threatened frustration than did children who scored higher on reflectivity. By the same token, Schleifer and Douglas (1973) found that children who scored higher on reflectivity tended to have a higher level of moral maturity, whereas children who scored higher on impulsivity tended to show a lower level of moral
matu-rity. Collectively, these two studies suggest that the reflective style is typically superior to the impulsive style in terms of its adaptive value.
Learning approaches have been found to be significantly related to stu-dents’ self-confidence, their self-esteem, and to the Big Five personality traits. Research evidence has indicated that the deep approach to learning as well as the meaning- and application-directed learning approaches are correlated with higher levels of self-confidence (e.g., Watkins, Biggs, &
Regmi, 1991), higher levels of self-esteem (e.g., Watkins & Dahlin, 1997), and openness to experience and conscientiousness (e.g., Busato et al., 1999)—the kinds of personality traits that are traditionally perceived as be-ing positive human traits. On the contrary, the surface approach to learn-ing and a reproductively-directed learnlearn-ing approach have been proved to be significantly correlated with lower levels of self-confidence, lower levels of self-esteem, and neuroticism, all of which are widely regarded as being relatively less adaptive human traits. In other words, results from these stud-ies consistently suggest that the learning approach construct is value-laden.
The deep and meaning and application-directed approaches are generally adaptive; the surface and the reproduction-directed approaches are gener-ally not as adaptive.
Mode of thinking (i.e., brain dominance) also has been studied in rela-tion to personality traits. Although there is a tendency for the holistic mode of thinking (i.e., right-brain dominance) to show superiority over the ana-lytic mode of thinking (i.e., left-brain dominance), mode of thinking can be value-differentiated. From a conventional perspective, the holistic mode of thinking is generally adaptive, in part because it is highly related to cre-ative behaviors (e.g., Harnad, 1972; Kim & Michael, 1995; Krueger, 1976;
Okabayashi & Torrance, 1984; Tan-Willman, 1981; Torrance & Reynolds, 1978). Moreover, there is empirical evidence suggesting that the holistic mode of thinking is associated with higher levels of tolerance of ambiguity and the analytic mode of thinking with lower levels of tolerance of ambigu-ity (e.g., Saleh, 1998).
However, several studies of the relationships between mode of thinking and personality traits have resulted in findings that do not support the su-periority of the holistic mode of thinking over the analytic mode. For exam-ple, in examining the relationships between information-processing styles and personality traits, Gadzella (1999) administered the Human Informa-tion Processing Survey (Torrance, Taggart, & Taggart, 1984) and the 16 Personality Factor Questionnaire (Cattell, Cattell, & Cattell, 1978) to 55 stu-dents enrolled in undergraduate psychology classes in a Midwestern state university in the United States. The investigator found that students with an analytic mode of thinking tended to display higher levels of self-control than did students with a holistic mode of thinking, and that students com-fortable with both the analytic and the holistic modes of thinking tended to
score higher on the anxiety scale than did students with an integrative mode of thinking. When studying 70 undergraduate students in The Neth-erlands, Jong, Merckelbach, and Nijman (1995) also found that the holistic mode of thinking was related to higher levels of anxiety. Thus, in general, these studies seem to indicate that the holistic mode of thinking is related to lower levels of self-control, but to higher levels of anxiety. On average, both lower levels of self-control and higher levels of anxiety are considered less adaptive personality traits. Furthermore, the holistic mode of thinking carries negative value because it is related to undesirable personality traits such as lack of self-control and high levels of anxiety. We cannot completely agree, especially in the case of people’s anxiety levels: The adaptive value of a person’s anxiety level depends on the situation. A proximal level of anxi-ety can be very conducive to one’s creative behaviors. Therefore, the con-struct of mode of thinking is largely value-laden, with the holistic mode of thinking generally viewed as more desirable than the analytic mode of thinking. Meanwhile, these modes of thinking can be value-differentiated at times.
Riding and Cheema’s (1991) two stylistic dimensions (verbal–imagery and analytic–wholistic) have been found to be significantly related to a range of personality traits, including neuroticism, impulsiveness, psychoti-cism, shyness, assertiveness, and sense of responsibility. Although there is certain indication that the wholist and the verbal styles carry negative value, the styles in Riding and Cheema’s framework are primarily value-differentiated.
The wholist style has been found to be highly associated with psychoticism (Riding & Wigley, 1997), whereas the verbal style has been linked to lower levels of sense of responsibility (Riding, Burton, Rees, & Sharratt, 1995). Stu-dents with the wholist and verbal combined styles tended to be more suscep-tible to neuroticism. These findings suggest that the wholist and verbal styles tend to carry undesirable value because they are significantly related to per-sonality traits generally viewed as undesirable.
However, some research findings do not yield a clear indication of the nature of the two style dimensions regarding their value. For example, like the analytic style, the wholist style also was found to be associated with low scores on impulsiveness. Furthermore, like students with the wholist–verbal combined styles, students with the analytic–imagery combined styles also tend to be high on the neuroticism scale (Riding & Wigley, 1997).
In a similar vein, other findings also reveal that the two style dimensions are essentially value-differentiated. Riding and Wright (1995) found that students who were high on the analytic style tended to be perceived as more shy by their housemates, whereas students who were high on the wholist style tended to be perceived as less shy by their housemates. Given that the desirability of shyness could vary greatly from situation to situation, and
from culture to culture, the styles (in this case, analytic and wholist) that are associated with shyness can be perceived as being desirable at times and un-desirable at others. That is, these styles are value-differentiated.
Across all studies considered in this section, it is easily discernible that several intellectual styles have been consistently related to personality traits that are deemed desirable in most societies. For example, the deep ap-proach to learning as well as the meaning-directed and application-direct-ed learning styles are relatapplication-direct-ed to such personality traits as openness to expe-rience, higher levels of self-esteem, and higher levels of self-confidence, whereas the opposite has been found for the surface approach to learning and reproducing-directed learning. A second example relates to the field-dependence/independence construct. Studies in this section suggest that, compared with field-dependent students, field-independent students ex-hibit significantly higher levels of assertiveness, higher levels of moral matu-rity, lower levels of pessimism, and more positive and psychologically better developed senses of identity. Similarly, compared with students with an im-pulsive style, students with a reflective style tend to display higher levels of moral maturity and lower levels of pessimism. Moreover, it has been dem-onstrated that the modes of thinking and the wholistic–analytic and ver-bal–imagery style dimensions are mainly value-differentiated.
Therefore, together, these findings indicate that some intellectual styles are better than others. That is, intellectual styles are mainly value-laden, or at least value-differentiated. However, styles are not value-free.
STYLES AND ACADEMIC PERFORMANCE
In examining the nature of intellectual styles, and in particular the value is-sue of styles, we have looked closely into the manner in which intellectual styles relate to students’ academic performance. This research indicates that styles are not value-free because some styles are consistently associated with better academic performance, whereas their opposite styles are consis-tently associated with poor academic performance (e.g., field-independent style over field-dependent style; reflective style over impulsive style; deep approach to learning over surface approach to learning; divergent thinking over convergent thinking, and so forth).
For example, the FDI construct was investigated in relation to various kinds of academic achievement (e.g., problem solving, laboratory tasks, lan-guage learning, tasks involving disembedding skill, organizing information, etc.) and to job performance (see chap. 5 for job performance). In general, field-independent people are higher in their academic achievement than are field-dependent people (e.g., Bagley & Mallick, 1998; Mansfield, 1998;
see also Jonassen & Grabowski, 1993 for a comprehensive review). Since the
late 1970s and early 1980s, the FDI construct has become more and more widely examined in the context of learners’ achievement under computer-assisted instructional conditions. In general, field-independent learners achieve more in computer-based learning environments. For example, compared with field-dependent learners, field-independent learners do better on problem-solving performance (e.g., Williams, 2001) and pro-gramming performance (e.g., Clements, 1986; Johnson & Kane, 1992; Wil-son, Mundy-Castle, & Sibanda, 1990). Field-independent and field-depen-dent learners use different learning strategies in a computer-assisted learning environment (e.g., Ford & Chen, 2000; Liu & Reed, 1994). For ex-ample, field-dependent learners made less use of Back/Forward buttons, whereas field-independent learners made greater use of Back/Forward but-tons. Field-dependent learners spent less time exploring the “detailed tech-niques” section of the tutorial, whereas field-independent learners made greater use of the “detailed techniques” section. (See chap. 8 for findings about relations of other styles to achievement.)
Further examination of the literature shows that some of the usually value-laden intellectual styles can become value-differentiated in the con-text of students’ academic performance. This change in the nature of styles (from value-laden to value-differentiated) can be attributed to a number of learning-environment-relevant factors, including the nature of an aca-demic discipline, performance tasks, and instructional materials. We also have come to the realization that some other styles (e.g., Kolb’s learning styles, Allinson and Hayes’s cognitive styles, and French et al.’s cognitive styles) are essentially value-differentiated, as the relationships between these styles and achievement vary as a function of the aforementioned learning environment factors.
The field-dependence/independence construct and the learning ap-proach construct, which both are essentially value-laden, have been discov-ered in some studies to be value-differentiated. For example, in examining the relationships between the field-dependent/independent styles and stu-dents’ achievement in introductory psychology, Feij (1976) found contrast-ing results between art-trained and math-trained undergraduate students in The Netherlands. For art-trained students, there was a significantly posi-tive relationship between field independence and achievement in introduc-tory psychology. By contrast, for the math-trained students, there was a neg-ative relationship between the two variables. Similarly, Varma and Thakur (1992) assessed whether field-independent and field-dependent school children differ in their academic performance in different subject matters.
Results showed that field-independent students achieved at higher levels in mathematics and physical sciences and that field-dependent students had higher achievement in social sciences and literature. Thus, both studies in-dicated that field-independent and field-dependent styles have adaptive
value, with one or the other style more functional, depending on the aca-demic disciplines.
The adaptive value (or value-differentiation) of the field-dependent and field-independent styles also can manifest itself when students are in-structed with different teaching materials. For instance, Ford (1995) con-ducted a study that examined students’ achievement under matched and mismatched learning environments among 38 university students in the United Kingdom. Students were assessed for field dependence/independ-ence as determined by their response to the wholist/analytic scale on Riding’s (1991) Cognitive Styles Analysis. Computerized teaching materials were designed to suit holist and serialist learners as defined by Pask and Scott (1972). Based on the nature of the field-dependence/independence construct and that of the holist/serialist construct, it was hypothesized that the holist teaching materials would be more suitable for field-dependent students and that the serialist teaching materials would better suited to field-independent students. As expected, in the holist learning environ-ment, field-dependent students achieved significantly better test scores, demonstrated greater learning efficiency, and spent more time on learning than did students with a field-independent style. Conversely, in the serialist learning environment, field-independent students performed significantly better than did field-dependent students.
The learning approaches assessed by Biggs’s Study Process Question-naire have also been found on occasion to be value-differentiated. For ex-ample, Zhang (2000a) investigated the relationships between learning ap-proaches and academic performance among 652 university students in Hong Kong. The investigator found that the unique contribution of stu-dents’ learning approaches to their achievement beyond abilities was sub-ject-specific. In general, the surface learning approach contributed posi-tively to students’ achievement in chemistry and geography. However, the deep and achieving learning approaches contributed positively to students’
achievement in the remaining subjects examined, including mathematics, Chinese language and culture, Chinese history, history, and English.
The adaptive value of learning approaches can also be observed when students are provided with different learning materials. For example, Kirby and Pedwell (1991) investigated undergraduate students’ performance on summarizing text passages relating to the content of an introductory psy-chology course in two different summarization conditions: text present and text absent. Students were randomly assigned to the two summarization conditions and their learning approaches were assessed by Biggs’ (1987) Study Process Questionnaire. All students read two texts and were re-minded that they would be asked questions about the texts. Students were also aware whether or not they would have access to the texts when writing the summaries. Students’ scores on summary and recall were the