Critically, according to Lister (2004) DeBello (1990), Kirby (1979), Tendy and Geiser (1998-1999) and Moodley (2009), the LSI has been scrutinised and examined repeatedly by researchers and has been deemed both reliable and valid. DeBello (1990) and Tendy and Geiser (Lister, 2004) affirm that the LSI has both high reliability as well as face and construct validity. Lister (2004) confirms that among nine different instruments that measure learning styles, the LSI was rated as having good or better validity and reliability than the others. They claim that based on the LSI scores of 817 randomly selected learners in grades 5 through 12, 95% of the reliabilities were tactual to or greater than .70 for the Likert scale of the English version and similarly high reliability coefficients were indicated for Hungarian, Malay, and Swedish translations. However, as an exception reliabilities calculated for the five subgroups on Bermuda sub sample only limited number of low coefficients were found on the subscale for Late Morning (.43) and Swedish sub sample on the elements of Temperature (0.05) and Design (-.14) also received low reliabilities (Lister, 2004; Moodley, 2009).
Most compelling are studies conducted by Dunn and Griggs (2000) on how learning styles differ among learners (Moodley, 2009). These studies reveal that learning style traits significantly differentiates according to achievement levels, gender, age, culture, and global versus analytic brain processing. Their research has shown that in the case of high versus low academic achievement levels, gifted and underachieving learners show notably different learning styles and do not perform well with the same methods. Critically, gifted learners presented with similar learning styles characteristics (Dunn & Griggs, 2000; Moodley, 2009)).
103 This is also congruent with gender differences. Dunn and Griggs (2000) and Moodley (2009) contend that males and females often learn differently; with males tending to be more kinesthetic, tactual and often visual needing more mobility in informal environments than females. They are more non-conforming and peer motivated. Alternately females tend to be more auditory, conforming, authority-oriented and better able to sit passively in conventional classrooms desks and chairs needing considerably quieter while learning. Females tend to be more self and adult motivated (Moodley, 2009).
Pointedly, differences in age may also be seen (Moodley, 2009). Dunn and Griggs (2000) concur that learning styles change as individuals grow older as learners undergo transition between the different school phases through to adulthood. They confidently claim that it is possible to anticipate approximate achievement and behavioral patterns by knowing age, gender and learning styles of learners. They offer that sociological preferences especially change with age and maturity with many learners becoming peer motivated by Grade 5 or 6 and remain so to about Grade 9 when they begin becoming more self-motivated.
Importantly, they state that gifted children become more self-motivated much earlier around Grade 1 or 2 and rarely experience a peer-motivated stage. Conversely, underachievers tend to remain peer-motivated often past adolescence. Significant development similarities also present in emotional and perceptual preferences across age cohorts with younger children being more tactual and kinesthetic than auditory (Moodley, 2009).
Most significantly, according to Moodley (2009) drawing from Dunn and Griggs (2000) research on the LSI on how individuals absorb and process new and difficult information have indicated correlations between global and analytic and left-or right- preference processors. It reveals that a relationship exists among these cognitive dimensions and the other traits/strands of the LSI and that they often cluster together. They found that analytic, left-brain processors correlates with learning persistently in a quiet, well-lit, formal setting with little or no intake while learning with intermittent periods of concentration and relaxation, in soft lighting and with sound while seated informally and snacking correlates with high-global or right-processing styles (Cody, 1983; Dunn, Bruno et al, 1990; Dunn, Cavanaugh et al., 1982, cited in Dunn & Griggs, 2000).
More so, Dunn and Griggs‘ (2000) claim that many of their experimental studies on the effects of sequential versus simultaneous instructional approaches have found that those taught in their own preferred processing
104 style reported statistically higher achievement than when not (Moodley, 2009). Data in their more recent studies show that most average and well-achieving learners in seventh grade performed better in Mathematics with global than analytic teaching approaches (Dunn & Griggs, 2000). Citing Burke (1998), Dunn and Griggs (2000) assert that most middle school populations (Intermediate/Senior Phase) preferred a global to analytic learning style save extreme analytics. In addition, correlation studies among cultural groups in America according to Dunn and Griggs (2000) have revealed significant differences in learning style preferences. Analysis of studies conducted among Native, Hispanic, African, Asian and European Americans showed patterns of greater than average preferences for selected learning style elements within individual cultural groups than between groups (Dunn & Griggs, 2000).
Thus, as Dunn et al. (2008) in Moodley (2009) confirm differentiated instruction has become an inevitable part of the schooling system. Creating a model that understands curriculum implementation through the individualised pedagogy of learning styles, founded on deep learning, tested and tried empirical principles and sound cognitive values, a learning styles model may be the long-awaited cornerstone teachers need to base differentiation on. The claim that profiling learners against the 21 elements providing for a learning environment and meaningful opportunity against its 5 strands, the Dunn and Dunn (1978) learning style inventory may be the missing link to bridge the gap between how teachers teach and learners learn best for individual understanding and success makes for a worthy investigation (Moodley, 2009).
Yet given the stark recognition of debate around the definition of learning styles, research around theory and practice, according to Moodley (2009), revealing almost equal consensus in support for as there is against learning styles based approaches to teaching as a response to meeting individual learner needs, and the call in many of the reports for further rigorous work in this field to establish a firm and stable theory of matching learning and teaching, the urgent demand for a simple and user-friendly instrument of identifying learner styles and a cost, time and labour saving means of implementing learning styles are more immediately submitted. This then may account for Stahl‘s (1999) acute findings of why ‗teachers after one year of implementation discontinued matching learners to their individual learning styles.‘
Even so, learning styles theory in general and the Dunn and Dunn (1978) learning styles approach in particular given the necessary attention may become one of those long awaited solutions to understanding curriculum implementation for success in 21 century heterogeneous classrooms (Moodley, 2009). It may be
105 a critical link in understanding the ‗one-size-fits-all‘ dilemma and detriment faced by myriads of learners and teachers. Understanding curriculum implementation through learning styles for individual pedagogy cannot afford to ignore the legitimacy and value of learning styles theory and approach.