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LSRC reference Section 3 page 18/19 Empirical evidence for pedagogical impact We have found no published evidence addressing Gregorc’s claims about the benefits of self-knowledge of learning styles or about the alignment of Gregorc-type learning and teaching styles. However, there are some interesting studies on instructional preference and on using style information to predict learning outcomes. Three of these come from the University of Calgary, where there has been large-scale use of the GSD. Lundstrom and Martin (1986) found no evidence to support their predictions that CS students would respond better to self-study materials and AR students to discussion. However, Seidel and England (1999) obtained results in a liberal arts college which supported some of Gregorc’s claims. Among the subsample of 64 out of 100 students showing a clear preference for a single cognitive style, a sequential processing preference (CS and AS) was significantly associated with a preference for structured learning, structured assessment activities and independent laboratory work. Random processing (CR and AR) students preferred group discussion and projects and assessments based on performance and presentation. There was a clear tendency for science majors to be sequential processors (19/22) and for humanities majors to be random processors (17/20), while social science majors were more evenly balanced (11/22). Harasym et al. (1995b) found that sequential processors (CS and AS) did not perform significantly better than random processors (CR and AR) in first-year nursing anatomy and physiology examinations at the University of Calgary. The nursing courses involved both lectures and practical work and included team teaching. It is probably unfair to attribute this negative result to the unreliability and poor validity of the instrument. It may be more reasonable to assume either that the examinations did not place great demands on sequential thinking or that the range of experiences offered provided adequately for diverse learning styles. Drysdale, Ross and Schulz (2001) reported on a 4-year study with more than 800 University of Calgary students in which the ability of the GSD to predict success in university computer courses was evaluated. As predicted (since working with computers requires sequential thinking), it was found that the dominant sequential processing groups (CS and AS) did best and the AR group did worst. The differences were substantial in an introductory computer science course, with an effect size of 0.85 between the highest- and lowest-performing groups (equivalent to a mean advantage of 29 percentile points). Similar results, though not as striking, were found in a computer applications in education course for pre-service teachers. Drysdale, Ross and Schulz (2001) presented data collected for 4546 students over the same 4-year period at the University of Calgary. The GSD was used to predict first-year student performance in 19 subject areas. Statistically significant stylistic differences in grade point average were found in 11 subject areas, with the largest effects appearing in art (the only subject where CR students did well), kinesiology, statistics, computer science, engineering and mathematics. In seven subjects (all of them scientific, technological or mathematical), the best academic scores were obtained by CS learners, with medical science and kinesiology being the only two subjects where AS learners had a clear advantage. Overall, the sequential processors had a very clear advantage over random processors in coping with the demands of certain academic courses, not only in terms of examination grades but also retention rates. Courses in which no significant differences were found were those in the liberal arts and in nursing. It seems clear from these empirical studies as well as from the factor analyses reported earlier that the sequential-random dimension stands up rather better than the concrete-abstract dimension. Seidel and England’s study (1999) suggests that some people who enjoy and are good at sequential thinking seek out courses requiring this type of thinking, whereas others avoid them or try to find courses where such thinking is valued rather less than other qualities. The results from the University of Calgary demonstrate that people who choose terms such as ‘analytical’, ‘logical’, ‘objective’, ‘ordered’, ‘persistent’, ‘product-oriented’ and ‘rational’ to describe themselves tend to do well in mathematics, science and technology (but not in art). Conclusion The construct of ‘sequential’, as contrasted with ‘random’, processing has received some research support and some substantial group differences have been reported in the literature. However, in view of the serious doubts which exist concerning the reliability and validity of the Gregorc Style Delineator and the unsubstantiated claims made about what it reveals for individuals, its use cannot be recommended.
Table 1 Gregorc’s Mind Styles Model and Style Delineator (GSD) General Strengths The GSD taps into the unconscious ‘mediation abilities’ of ‘perception’ and ‘ordering’. Weaknesses Styles are natural abilities and not amenable to change. Design of the model There are two dimensions: concrete-abstract and sequential-random. Individuals tend to be strong in one or two of the four categories: concrete sequential, concrete random, abstract sequential and abstract random. Some of the words used in the instrument are unclear or may be unfamiliar. No normative data is reported, and detailed descriptions of the style characteristics are unvalidated. Reliability The author reports high levels of internal consistency and test–retest reliability. Independent studies of reliability raise serious doubts about the GSD’s psychometric properties. Validity Moderate correlations are reported for criterion-related validity. There is no empirical evidence for construct validity other than the fact that the 40 words were chosen by 60 adults as being expressive of the four styles. The sequential/random dimension stands up rather better to empirical investigation than the concrete/abstract dimension. Implications for pedagogy Although Gregorc contends that clear-cut Mind Style dispositions are linked with preferences for certain instructional media and teaching strategies, he acknowledges that most people prefer instructional variety. Gregorc makes the unsubstantiated claim that learners who ignore or work against their style may harm themselves. Evidence of pedagogical impact Results on study preference are mixed, though there is evidence that choice of subject is aligned with Mind Style and that success in science, engineering and mathematics is correlated with sequential style. We have not found any published evidence addressing the benefits of self-knowledge of learning styles or the alignment of Gregorc-type learning and teaching styles. Overall assessment Key source Theoretically and psychometrically flawed. Not suitable for the assessment of individuals. Gregorc 1985
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Page 9 and 10: LSRC reference Acknowledgements The
Page 11 and 12: Section 1 A systematic review of le
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Page 19 and 20: Figure 3 Vermunt’s model of learn
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Page 33 and 34: The measures Over 25 years, Dunn an
Page 35 and 36: Table 4 Percentages of respondents
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Our recommendation in favour of inc
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Learning styles in practice: labell
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Table 44 13 learning-styles models
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