Learning styles preferences of statistics students

Quality Assurance in Education Learning styles preferences of statistics students: A study in the Faculty of Business and Economics at the UAE University Darwish Abdulrahman Yousef

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Learning styles preferences of statistics students A study in the Faculty of Business and Economics at the UAE University

Learning styles preferences 227

Darwish Abdulrahman Yousef Downloaded by United Arab Emirates University At 04:47 27 March 2016 (PT)

UAE University, Al-Ain, UAE

Received 22 January 2014 Revised 9 July 2014 2 December 2014 Accepted 3 July 2015

Abstract Purpose – Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates University (UAEU). Furthermore, it investigates whether there are statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics. Design/methodology/approach – Questionnaires were distributed to the whole population which included 79 undergraduate statistics students at the UAEU, of which 69 returned the questionnaire. Descriptive statistics such as frequencies and percentages were used to present the main characteristics of respondents and the results of the study. Additionally, a chi-square test was used to find out if there were significant differences along the four dimensions of learning style preferences due to students’ demographic and academic characteristics. Findings – The results indicate that UAEU undergraduate statistics students have balanced preferences along the four dimensions of learning styles. Results also suggest that there are no statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics, except in the active-reflective and sensing-intuitive dimensions with respect to high school type (private vs public). Research limitations/implications – There are a number of limitations associated with this study. First, the findings of the study are based on data from only one university. Second, the sample was small and limited to undergraduate statistics students and, therefore, it excluded graduate students who might have had different experiences. Third, the results are based on a self-reported questionnaire and this, in turn, might have affected the reliability of the results On the other hand, it has a number of implications for educators and students. Educators will benefit from the results of this study in the sense that they will adopt teaching styles and strategies that match learning styles of the majority of their students. Students themselves will benefit from knowing their own learning style. Originality/value – The present study is the first attempt to explore learning styles preference of undergraduate students not only in the UAE setting but also in the developing country setting. Keywords Learning styles, United Arab Emirates, Higher education, Undergraduates, Business education, Index of learning styles, Statistics students, Statistics education Paper type Research paper

Introduction Improving the quality of higher education institutions’ graduates is the main goal of educators as well as curriculum developers and policy makers. Several studies have shown that every individual has a unique learning style (Felder and Silverman, 1988;

Quality Assurance in Education Vol. 24 No. 2, 2016 pp. 227-243 © Emerald Group Publishing Limited 0968-4883 DOI 10.1108/QAE-01-2014-0004

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Bargar et al., 1994; Gappi, 2013), and the academic performance of higher education students is related to their learning styles (Christou and Dinov, 2010; Abidin et al., 2011; Komarraju et al., 2011; Yeung et al., 2012). As a result, improving students’ performance requires that consideration is to be given to individuals’ learning style. Students’ learning style preferences and the factors influencing students’ learning style preferences have been the subject of numerous publications, published either in journals or in conference proceedings, over the past decades. This is due mainly to the importance of learning styles. Students’ learning styles play an important role in their understanding of the course material and in turn their performance in the course. Additionally, learning styles play an equally important role in helping instructors adopting the teaching styles that match their students’ learning styles. This in turn enhances students’ ability to digest the material and improves students’ interest and, consequently, students’ performance in the course. Furthermore, understanding students’ learning styles provides curriculum developers with the information necessary for developing schools’ curricula. The term learning style has been defined as “being characteristics of the cognitive, affective, and physiological behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to learning environment” (Keefe, 1979, p. 4). Dunn (1990, p. 353) sees learning style as “the way each learner begins to concentrate, process, and retain new and difficult information”. Loo (2002, p. 349) defines learning style as “the consistent way in which a learner responds to or interacts with stimuli in the learning context.”. Felder (1996, p. 18) describes learning style as “characteristics strengths and preferences in the way they take in and process information”. Learning style was also defined as “the way in which a learner prefers to take in and process information” (Rosati, 1999, p. 17). However, for the purpose of this study, Felder’s (1996) definition of learning style was adopted. An extensive literature search revealed lack of prior studies concerning statistics students’ learning style preferences in the UAE context in particular. Furthermore, most business students at the United Arab Emirates University (UAEU) and elsewhere perceive statistics as one of the most difficult majors due to the quantitative nature of the subjects taught in this major. Although there is a high demand for statistics graduates in the UAE, few students usually select this major compared with other business majors such as accounting, management, marketing and finance. This is due mainly to the negative attitudes students have toward statistics as a major. Hence, it seems important to investigate the learning style preferences of statistics students, so that appropriate corrective actions could be taken and, consequently, better performance could be attained. Additionally, a number of scholars argue that there is a need to investigate the learning style preferences of undergraduate students in different cultures (Jaju et al., 2002; Naik, 2009, 2013). The objective of this study was to explore learning style preferences of the UAEU undergraduate statistics students. It also investigated whether there were significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics. The rest of the paper is organized as follows. The next section presents a brief description of learning styles, followed by a literature review of previous research related to the present study. The methodology and the process of data-gathering follow.

The results are then presented and discussed. The paper concludes with implications, limitations and identification of potential lines for further research.


Learning styles While there are a number of theories and models concerning learning styles, the most widely used models of learning styles in business education are those of Felder and Soloman (2004) and Kolb (1985, 1996 and 1999). The present study is based on the model developed by Felder and Soloman. According to this model, there are four dimensions of learning styles. These are active/reflective, sensing/intuitive, visual/verbal and sequential/global. Active/reflective Active learners like hands-on activities, group discussions and problem solving. They dislike simply sitting in class and taking notes. Reflective learners, on the other hand, like to think about a concept or problem quietly first. Furthermore, they like to study and solve problems alone and take notes and summarize materials (Felder, 1993). Sensing/intuitive Sensing learners prefer external information that is perceived by the sense, they are practical, careful and good in memorizing things. They like facts and observations. On the other hand, intuitive learners like abstracts, mathematical formulations and innovative methods of problem solving (Felder, 1993). Visual/verbal Visual learners like pictures, diagrams, flowcharts, photographs and videos. They like color-coding, highlighting and drawing boxes, circles and lines. Verbal learners like written or spoken explanations and like to outline material in their own words (Felder, 1993). Sequential/global Sequential learners absorb information and acquire understanding of material in small connected parts. Global learners are divergent and good at synthesis, they cannot learn without the “big picture”. They can solve complex problems faster but may not be able to explain how they did it (Felder, 1993). Literature review Very little research has addressed the learning style preferences of statistics students. Bell (1998) tries to answer a number of questions such as does one’s learning style affect his/her grade in an introductory statistics course? Does it depend on the age or nationality of the individual? Do male students perform better in quantitative courses? Do full-time outperform part-time students? Results indicate that learning style, age and full-time or part-time have an important impact on the final grades, while gender and nationality have no significant impact on the final grades in introductory statistics courses. Naik (2003) uses the index of learning styles (ILS) developed by Felder and Soloman (2004) to determine the learning style of 156 business students enrolled in two levels of business statistics courses. The results indicated that the majority of business students preferred sensing, visual, active and sequential learning styles. Similarly, Naik


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(2009) investigated the learning styles of 297 undergraduate business students enrolled in business statistics and operations management courses in an American university. The results showed that the majority of the business students preferred sensing, visual, active and sequential learning styles. Christou and Dinov (2010) investigated the impact of students’ learning styles, in addition to other factors, on their performance in statistics and probability courses. They concluded that students’ learning styles had an important impact on their performance. Ganyaupfu (2013) examined factors influencing academic achievement in quantitative courses among business students of private higher education institutions in South Africa. Results indicated that lecturer competence, teaching methods and quality of learning materials had a positive influence on business students’ academic achievements in quantitative business courses. Stevens (2013) measured the effect of learning style on several variables for students in undergraduate business statistics. The author demonstrated that learning style, as measured using the VARK (Visual, Aural, Read-write or Kinesthetic) model, had significant effects on a number of variables such as academic success, student perceptions of course and instructor and academic major. A handful of prior studies investigated learning styles of students of other business majors. Novin et al. (2003) investigated the preferred learning styles of accounting, management, marketing and general business majors. Findings indicated that the vast majority of all four majors demonstrated clear preferences for the assimilator and converger learning styles. Giordano and Rochford (2005) investigated the learning styles of first-year business majors at an urban community college. Results showed that 94 per cent of the participants were analytic learners. Using the ILS, Pallapu (2008) examined the relationships among undergraduate students’ learning styles from the Colleges of Business, Education and Liberal Art. The author examined the impact of gender, age, ethnicity, GPA and grade level on learning style. The results indicated that undergraduate business students preferred active (69 per cent), sensing (79 per cent), visual (77 per cent) and sequential (70 per cent) learning styles. Results also revealed no statistically significant relationships among the demographic variables of gender, race/ ethnicity, age, GPA and grade level and students’ learning styles. Goorha and Mohan (2009) analyzed the learning preferences of business schools students in addition to teaching strategies and course content that would lead to these preferences. They concluded that business students had a preference for convergence and assimilative learning. Luck and Estes (2011) investigated the learning styles of business students at a US university, as they relate to the areas of concentration with the College of Business and Public Administration. Results showed a preference for active, sensory, visual and sequential learning styles. Results also indicated the largest difference is 1.58 points between accounting and marketing concentrations on the sensory/intuitive construct on an 11-point scale. O’Leary and Munro (2011) evaluated the learning styles of final-year accounting students and assessed the interaction of teaching methods and learning styles. The findings indicated that students predominantly displayed passive learning styles; students’ preferred styles varied depending on the topic; and when learning style matched the teaching method used, usefulness was assessed as high, but when learning style and teaching method differed, usefulness deteriorated significantly. Using ILS, Naik and Girish (2012) tried to determine the distribution of learning styles of 125 South Korean business students enrolled in a South Korean institution of higher education. Results showed that a greater


proportion of South Korean business students preferred sensing over intuitive, visual over verbal, reflective over active and global over sequential learning styles. In a recent study, Yousef (2014) investigated learning style preferences of the UAEU undergraduate business students. Results indicated that UAEU business students had a balanced preference along the four dimensions of the learning styles. In summary, the literature review indicated a lack of research in the area of statistics students’ learning style preferences not only in the UAE context but also in the developed and in developing countries. This confirmed the need for such a study, as statistics as a major differs from other disciplines investigated in prior research. Method Population and sample The population of this study consisted of full-time, statistics students at the College of Business and Economics (CBE) of the UAEU. As of fall 2012, the total number of statistics students was 79. Due to the small size of the population, the author, with his colleagues, distributed printed questionnaires to the whole population. Of the 79 questionnaires distributed, 69 questionnaires were returned (an 87 per cent response rate). Data collection To gather the required data, the author developed a questionnaire, which consisted of two parts. The ILS survey with 44 questions, which measured the four learning style domains, comprised the first part. The second part consisted of academic and demographic information such as high school majors (art vs science), age, nationality (Emirati vs non-Emirati), gender, GPA and type of high school (public vs private). Measure Learning style was measured using Felder and Soloman’s (2004) ILS. This index consisted of 44 questions, 11 questions for each domain. All questions are forced-choice with alternative answers “a” or “b”. ILS covered two opposite styles in each of the four domains, active/reflexive, sensing/intuition, visual/verbal and sequential/global. Questions 1, 5, 9, 13, 17, 21, 25, 29, 33, 37 and 41 measured the domain of active/reflective with “a” for active and “b” for reflective. Questions 2, 6, 10, 14, 18, 22, 26, 30, 34, 38 and 42 measured the domain of sensing/intuitive with “a” for sensing and “b” for intuitive. Questions 3, 7, 11, 15, 19, 23, 27, 31, 35, 39 and 43 measured the domain of visual/verbal with “a” for visual and “b” for verbal. Questions 4, 8, 12, 16, 20, 24, 28, 32, 36, 40 and 44 measured the domain of sequential/global with “a” for sequential and “b” for global. ILS has been widely used in prior studies to measure students’ learning styles (Kovacˇi´c, 2004; Pallapu, 2008; Naik, 2009; Naik and Girish, 2012; Naik, 2013). The reliability of this measurement has been tested in prior studies. For example, Livesay et al. (2002) tested the reliability of ILS and found alpha to be in the range of 0.54 to 0.72. They also found relatively high test–retest reliability in repeated measurements over time, and concluded that the ILS was an appropriate and statistically acceptable tool for characterizing learning preferences. Felder and Spurlin (2005) analyzed the reliability of ILS and concluded that the current version of ILS may be considered reliable, valid and suitable. Litzinger et al. (2007) found that the internal consistency reliability of the four learning style domains of the ILS ranges from 0.55 to 0.77. In the present study, the reliability of ILS was measured using Cronbach’s alpha, which was 0.76 for active/


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reflective domain, 0.65 for sensing/intuitive domain, 0.79 for visual/verbal domain and 0.64 for sequential/global domain. The overall value was 0.75. Analysis We used descriptive statistical analysis such as frequencies and percentages to present the demographic and academic information of respondents, and also to present the distribution of respondents by learning style domains and by respondents’ demographic and academic information. Additionally, a chi-square test was used to find out whether there were significant differences along the four dimensions of the learning style preferences due to students’ demographic and academic characteristics. Results and discussion Respondents’ demographic and academic characteristics Table I presents respondents’ demographic and academic characteristics. Data in Table I show that 62 per cent of the respondents were female, 51 per cent majoring in science in the high school and about 82 per cent were Emirati. Eighty-seven per cent attended public high schools, and 68 per cent had GPA less than 3.0. Ninety-seven per cent were 20 or more years of age. These results indicated that the majority of respondents were females, majoring in science in the high school, had GPA less than 3.0, were Emirati, attended public high schools and were 20 years old and above.

Table I. Respondents’ demographic and academic information (n ⫽ 69)

Characteristics

n

(%)

High school majors Art Science

34 35

49.3 50.7

Nationality Emirati Non-Emirati

56 13

81.2 18.8

Type of high school Private Public

7 62

10.1 89.9

Age Less than 20 years 20 to less than 22 years 22 years and over

1 51 17

1.4 73.9 24.6

Gender Male Female

21 48

30.4 69.6

GPA Less than 3.0 3.0 and above

48 21

69.5 30.5


Distribution of respondents by learning style domains Table II presents the distribution and percentages of UAEU undergraduate statistics students by learning style domains. It should be noted that a score of 1 to 3 in either dichotomy of a dimension indicates a learning style preference that is fairly balanced in that dimension. A score of 5 to 7 indicates a moderate preference in the associated dichotomy of the concerned dimension. A score of 9 to 11 indicates a strong preference. Data presented in Table II show that 65.2 per cent of the UAEU undergraduate statistics students had balanced preferences in the active-reflective dimension, 20.3 per cent of students had moderate reflective preference and 5.8 per cent were strong reflective learners. Hence, 91.3 per cent of the students would benefit from teaching styles preferred by reflective learners. In the sensing-intuitive dimension, 50.7 per cent of the statistics students had balanced preferences, 36.2 per cent had moderate intuitive and 7.2 per cent had strong intuitive preference. Thus, 94.1 per cent of the students would benefit from teaching techniques preferred by intuitive learners. Table II also showed that 55.1 per cent of the statistics students were balanced learners in the visual-verbal dimension, 26.1 per cent were moderate verbal learners and 17.4 per cent were strong verbal learners. Thus, 98.6 per cent of the statistics students would be comfortable with teaching techniques suitable for verbal learners. Finally, Table II shows that 72.5 per cent of the statistics students were balanced learners in the sequential-global dimension, 20.3 per cent were moderate global learners and 1.4 per cent were strong global learners. Accordingly, 94.2 per cent of the students would benefit from teaching techniques preferred by global learners. On the other hand, the minority of students were active (8.7 per cent), sensing (5.7 per cent), visual (1.4 per cent) and sequential (5.8 per cent). It was concluded that UAEU undergraduate statistics students preferred reflective over active, intuitive over sensing, verbal over visual and global over sequential learning styles. These results indicated that UAEU undergraduate statistics students liked thinking something through, think about it, in study groups working on difficult material, they were more likely to sit back and listen, and they liked to work alone and take notes and summarize material (reflective). They preferred external information according to their memory, reflection and imagination; they preferred theory, concepts and interpretation. Furthermore, they appreciated the diversity and complexity of situations and disliked too much detail and repetition (intuitive). Furthermore, they preferred oral information (verbal). They gained an overall understanding first by absorbing material at random,

Preference

(%) Frequency Preference

Strong active Moderate active Balanced ACT-REF Moderate reflective Strong reflective Total

8.7 65.2 20.3 5.8 100.00


Strong sensing

1.4

Strong visual

Moderate sensing Balanced SEN-INT Moderate intuitive Strong intuitive Total

4.3

Moderate visual Balanced VIS-VRB Moderate verbal Strong verbal Total

50.7 36.2 7.2 100.00


1.4 55.1 26.1 17.4 100.00

Strong sequential Moderate sequential Balanced SEQ-GLB Moderate global Strong global Total


(%) Frequency 0.0 5.8 72.5 20.3 1.4 100.00

Table II. Distribution of respondents by learning style domains

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then saw the significance of the parts to the whole; they could also solve complex problems faster but they may not be able to explain how they did it (global). The results of the present study are in line with the findings of Yousef’s (2014) study, which used a sample of UAEU undergraduate business students. However, these results are contrary to the results of a number of prior studies conducted in different cultures that used samples of non-statistics students, such as the study of Rosati (1999) in Canada, Kuri and Truzzi (2002) in Brazil, Naik (2003) Pallapu (2008) in the USA and Naik and Girish (2012) in Korea. Such differences might be attributed to differences in culture, as a number of prior studies suggested that students’ learning styles differ across cultures and ethnicity backgrounds (Nooriafshar and Maraseni, 2005; Zualkernan et al., 2006; Gündüz and Özcan, 2010; Naik, 2013). Distribution of respondents by learning style domains and demographic and academic characteristics Tables III-VIII exhibit the distribution of respondents by learning style domains, and demographic and academic characteristics.

Strong active Active-reflective dimension Emirati Non-Emirati Total Strong sensing Sensing-intuitive dimension Emirati Non-Emirati 7.7 Total 1.4 Strong visual Visual-verbal dimension Emirati Non-Emirati Total Strong sequential Table III. Row percentages for the four learning style dimensionsnationality

Sequential-global dimension Emirati Non-Emirati Total

Moderate active

Balanced ACT-REF

Moderate reflective

Strong reflective

Total (%)

8.9 % 7.7 8.7

64.3 69.2 65.2

23.2 7.7 20.3

3.6 15.4 5.8

100 100 100

Moderate sensing

Balanced SEN-INT

Moderate Intuitive

Strong Intuitive

Total (%)

5.4 %

50.0 53.8 50.7

39.3 23.1 36.2

5.4 15.4 7.2

100 100 100

4.3 Moderate visual

Balanced VIS-VRB

Moderate verbal

Strong verbal

Total (%)

1.8 %

51.8 69.2 55.1

28.6 15.4 26.1

17.9 15.4 17.4

100 100 100

1.4 Moderate sequential

Balanced SEQ-GLB

Moderate global

Strong global

Total (%)

7.1 %

69.6 84.6 72.5

21.4 15.4 20.3

1.8

100 100 100

5.8

1.4

Strong active Active-reflective dimension ⬍20 years 20 to ⬍22 years 22 years and over Total


Strong sensing Sensing-intuitive dimension ⬍20 years 20 to ⬍22 years 2.0 22 years and over Total 1.4 Strong visual Visual-verbal dimension ⬍20 years 20 to ⬍22 years 22 years and over Total Strong sequential Sequential-global dimension ⬍20 years 20 to ⬍22 years 22 years and over Total

Moderate active

Balanced ACT-REF

Moderate reflective

Strong reflective

Total (%)

9.8 5.9 8.7

100 % 66.7 58.8 65.2

17.6 29.4 20.3

5.9 5.9 5.8

100 100 100 100

Strong intuitive

Total (%)

Moderate sensing

Balanced SEN-INT

Moderate intuitive

2.0 11.8 4.3

43.1 76.5 50.7

100 % 43.1 11.8 36.2

Moderate visual

Balanced VIS-VRB

5.9 1.4

100 % 52.9 58.8 55.1

Moderate sequential 100 % 3.9 5.9 5.8

Balanced SEQ-GLB

70.6 82.4 72.5

7.2

100 100 100 100

Moderate verbal

Strong verbal

Total (%)

27.5 23.5 26.1

19.6 11.8 17.4

100 100 100 100

Moderate global

23.5 11.8 20.3

9.8

Strong global

2.0 1.4


Total (%) 100 100 100 100

Data in Table III display the distribution of UAEU statistics students along the four dimensions of learning styles according to students’ nationality (Emirati or non-Emirati). A visual examination of the data presented in Table III might indicate significant differences between Emirati and non-Emirati students along some of the dimensions of learning styles. A chi-square test of independence was performed for each of the four learning style dimensions across the five categories of learning style preferences and showed no statistically significant difference between Emiratis and non-Emiratis along the four dimensions of the learning styles. A majority of non-Emiratis were Arabs and belonged to a culture similar to that of the UAE; as a result, they have similar learning style preferences to those of the Emiratis. A number of researchers have pointed out that students’ learning styles differ across cultures and ethnicity backgrounds (Gündüz and Özcan, 2010; Naik et al., 2010; Naik, 2013).

Table IV. Row percentages for the four learning style dimensions-age groups

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Strong active Active-reflective dimension Male Female Total


Strong sensing Sensing-intuitive dimension Male 4.8 Female Total 1.4 Strong visual Visual-verbal dimension Male Female Total Strong sequential Table V. Row percentages for the four learning style dimensionsgender

Sequential-global dimension Male Female Total

Moderate active

Balanced ACT-REF

Moderate reflective

Strong reflective

Total (%)

9.5 % 8.3 8.7

66.7 64.6 65.2

9.5 25.0 20.3

14.3 2.1 5.8

100 100 100

Moderate sensing

Balanced SEN-INT

Moderate intuitive

Strong intuitive

Total (%)

9.5 2.1 4.3

47.6 52.1 50.7

28.6 39.6 36.2

9.5 6.2 7.2

100 100 100

Moderate visual

Balanced VIS-VRB

Moderate verbal

Strong verbal

Total (%)

4.8

61.9 52.1 55.1

23.8 27.1 26.1

9.5 20.8 17.4

100 100 100

1.4 Moderate sequential

Balanced SEQ-GLB

Moderate global

Strong global

Total (%)

9.5 % 4.2 5.8

57.1 79.2 72.5

33.3 14.6 20.3

2.1 1.4

100 100 100

Table IV shows the distribution of UAEU statistics students along the four dimensions of learning styles according to age groups. A visual examination of the data presented in Table IV might suggest significant differences among different age groups along some of the dimensions of learning styles. A chi-square test of independence was performed for each of the four learning style dimensions across the five categories of learning style preferences and revealed no statistically significant differences among different age groups. This result is consistent with those of Pallapu (2008) and Gappi (2013), who found no significant effect of age on the learning style preferences of students. Data in Table V show the distribution of UAEU statistics students along the four dimensions of learning styles according to gender (male or female). A visual examination of the data presented in Table V might indicate significant differences between male and female students along some of the dimensions of learning styles. A chi-square test of independence was performed for each of the four learning style dimensions across the five categories of learning style preferences and revealed no statistically significant differences between male and female students along the four dimensions of learning styles. This result is consistent with the results of Pallapu (2008),

Strong active Active-reflective dimension Science Art Total


Strong sensing

Moderate active

Balanced ACT-REF

Moderate reflective

Strong reflective

Total (%)

8.8 % 8.6 8.7

67.6 62.9 65.2

14.7 25.7 20.3

8.8 2.9 5.8

100 100 100

Moderate sensing

Balanced SEN-INT

Moderate intuitive

Strong intuitive

Total (%)

5.9 2.9 4.3

47.1 54.3 50.7

32.4 40.0 36.2

11.8 2.9 7.2

100 100 100

Sensing-intuitive dimension Science 2.9 Art Total 1.4 Strong visual

Moderate visual

Balanced VIS-VRB

Moderate verbal

Strong vverbal

Total (%)

2.9%

47.1 62.9 55.1

29.4 22.9 26.1

20.6 14.3 17.4

100 100 100

Visual-verbal dimension Science Art Total Strong sequential Sequential-global dimension Science Art Total

1.4 Moderate sequential 5.9 % 5.7 5.8

Balanced SEQ-GLB 73.5 71.4 72.5

Moderate global 20.6 20.0 20.3

Strong global

2.9 1.4


Total (%) 100 100 100

Gündüz and Özcan (2010), Naik and Girish (2012), Gappi (2013) and Sopian et al. (2013), who found no significant differences in learning style preferences due to gender. Table VI displays the distribution of UAEU statistics students along the four dimensions of learning styles according to high school major (science or arts). A visual examination of the data presented in Table VI might show significant differences between those with science as major in the high school and those with arts as major in the high school along some of the dimensions of learning styles. A chi-square test of independence was performed for each of the four learning style dimensions across the five categories of learning style preferences and disclosed no statistically significant difference between students with science major in the high school and students with arts major in the high school in the four dimensions of the learning styles. Data in Table VII reveal the distribution of UAEU statistics students along the four dimensions of learning styles according to GPA groups. A visual examination of the data presented in Table VII might show significant differences among GPA groups along some of the dimensions of learning styles. A chi-square test of independence was performed for each of the four learning style dimensions across the five categories of

Table VI. Row percentages for the four learning style dimensionshigh school major

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Strong active Active-reflective dimension Less than 2.0 2.0-2.9 3.0 and above Total


Strong sensing

Moderate active

Balanced ACT-REF

Moderate reflective

Strong reflective

Total (%)

10.6 4.8 8.7

100 % 61.7 71.4 65.2

25.5 9.5 20.3

2.1 14.3 5.8

100 100 100 100

Moderate sensing

Balanced SEN-INT

Moderate intuitive

Strong intuitive

Total (%)

6.4 4.3

61.7 28.6 50.7

100 % 29.8 47.6 36.2

2.1 19.0 7.2

100 100 100 100

Moderate visual

Balanced VIS-VRB

Moderate verbal

Strong verbal

Total (%)

100 % 57.4 47.6 55.1

25.5 28.8 26.1

14.9 23.8 17.4

100 100 100 100

Sensing-intuitive dimension Less than 2.0 2.0-2.9 3.0 and above 4.8 Total 1.4 Strong visual Visual-verbal dimension Less than 2.0 2.0-2.9 3.0 and above Total Strong sequential Table VII. Row percentages for the four learning style dimensionsGPA groups

Sequential-global dimension Less than 2.0 2.0-2.9 3.0 and above Total

2.1 1.4 Moderate sequential

Balanced SEQ-GLB

Moderate global

Strong global

Total (%)

8.5

100 % 66.0 85.7

23.4 14.3

2.1

100 100 100

5.8

72.5

20.3

1.4

100

learning style preferences and revealed no statistically significant differences between GPA groups along the four dimensions of learning styles. These findings are consistent with previous studies such as Pallapu (2008), Warn (2009) and Gappi (2013), which found no statistically significant correlation between the academic achievement and the learning style preferences of students. However, it is contrary to the findings of a number of prior studies which found a link between learning style preferences and GPA (Wynd and Bozman, 1996; Dwyer, 1998; Cano, 1999; Jones et al., 2003). Results in Table VIII reveal the distribution of UAEU statistics students along the four dimensions of learning styles according to high school type (public or private). A visual examination of the data presented in Table VIII shows significant differences between students who graduated from public high school and those who graduated from private high school along some of the dimensions of learning styles. A chi-square

Strong active Active-reflective dimension Private Public Total


Strong sensing Sensing-intuitive dimension Private 14.3 % Public Total 1.4 Strong visual Visual-verbal dimension Private Public Total Strong sequential Sequential-global dimension Private Public Total

Moderate active

Balanced ACT-REF

Moderate reflective

Strong Reflective

Total (%)

9.7 8.7

57.1 % 66.1 65.2

14.3 21.0 20.3

28.6 3.2 5.8

100 100 100

Moderate sensing

Balanced SEN-INT

Moderate intuitive

Strong intuitive

Total (%)

4.8 4.3

57.1 50.0 50.7

14.3 38.7 36.2

14.3 6.5 7.2

100 100 100

Moderate visual

Balanced VIS-VRB

Moderate verbal

Strong verbal

Total (%)

1.6 1.4

71.4 % 53.2 55.1

28.6 25.8 26.1

19.4 17.4

100 100 100

Moderate sequential

6.5 5.8

Balanced SEQ-GLB 57.1 % 74.2 72.5

Moderate global 42.9 17.7 20.3

Strong global

1.6 1.4


Total (%) 100 100 100

test of independence was performed for each of the four learning style dimensions across the five categories of learning style preferences and revealed statistically significant differences only for active-reflective and sensing-intuitive dimensions. Conclusion This study focused on exploring the learning style preferences of UAEU undergraduate statistics students. Furthermore, it investigated whether there were statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics. The results indicated that UAEU undergraduate statistics students had balanced preferences along the four dimensions of learning styles. It is concluded that UAEU undergraduate statistics students preferred reflective over active, intuitive over sensing, verbal over visual and global over sequential learning styles. Furthermore, it was concluded that there were no statistically significant differences along the four dimensions of learning styles due to students’ demographic and academic characteristics, except in the active-reflective and sensing-intuitive dimensions with respect to high school type (private vs public).

Table VIII. Row percentages for the four learning style dimensionstype of high school

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The present study has a number of implications for educators and students. Educators will benefit from the results of this study in the sense that they need to adopt teaching styles and strategies that match learning styles of the majority of the students in class. This in turn will result in improving students’ ability to digest the material, and accordingly, will improve their overall academic performance. Previous research has shown a positive correlation between learning styles of students and teaching styles of instructor (Hussain and Ayub, 2012). A number of previous studies have emphasized the importance of knowing the learning styles of students in class and adopting teaching methods that match the learning styles of the majority of students for effective teaching (Jaju and Kwak, 2000; Yeung et al., 2012; Al BuAli et al., 2013; Naik, 2013). For reflective learners, educators should use teaching techniques that emphasize giving students the chance to think about a concept or a problem quietly, study and solve problems alone, take notes and summarize material. For intuitive learners, educators should use teaching techniques that focus on abstract ideas, mathematical formulation and innovative methods of problems solving. They should avoid using techniques that depend on memorization and routine calculations. For verbal learners, educators should use techniques that emphasize written and spoken explanations, and give students the chance to outline material using their own words. Finally, for global learners, educators should provide the big picture or the goal of the lesson before presenting the steps. They should give students the freedom to choose their own methods of solving problems and not to force them to use the instructor’s approach. Additionally, educators will need to take care of different learning styles when preparing the contents of the courses, assignments and various activities in and out of class. On the other hand, educators should not neglect the small proportion of students who are active, sensing, visual and sequential learners and adopt teaching techniques which accommodate the needs of the minority group. Students themselves will benefit from knowing their own learning style. Cano (1999) argued that identifying the students’ learning style early in their academic career would be to alert the student to his/her potential academic weaknesses and to teach them mechanisms by which to cope and/or adapt their learning. The results of this study provide a guideline for students to make the right career decision. Students considering specialization in a statistics major can evaluate their individual learning preferences with the learning style preferences of statistics students. Further, Jones et al. (2003) argued: Increasing student awareness of their own learning styles may be quite helpful in increasing control of their learning habits and strategies, which should, in turn, influence their academic performance (p. 373).

The study has a number of limitations. First, the sample was taken from a single university in the UAE; therefore, it might not adequately represent UAE statistics students. Second, the sample is limited to undergraduate statistics students and, therefore, it excludes graduate students who might have different experiences. Third, the results are based on self-reported questionnaire and this, in turn, might affect the reliability of the results. A number of future studies are suggested. For example, a study which uses a sample which covers more than one university in the UAE would be valuable. A comparison between learning style preferences of undergraduate and graduate statistics students would be of interest. Moreover, a comparison between learning styles of students of different majors would be worthy. A study which compares the learning style


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