VARK learning style and student satisfaction with traditional and online courses.
The Sloan Consortium's Eighth Annual Survey of Online Education (Allen & Seaman, 2010) claims online enrollment growth is fueled by economic conditions, budget pressures, and competition from for-profit institutions. Seventy five percent of the colleges and universities surveyed for that study report an increased demand for online courses and programs as a result of the present economic downturn. This most recent Sloan study reports an increase of nearly one million online students in one year (2008 to 2009) with over 5.5 million students enrolled nationwide in at least one online course in the Fall term of 2009 (Allen & Seaman, 2010).
Learning styles in the traditional classroom have been widely researched; however, there is less research on the impact of learning styles in online education. Common learning outcomes include student performance and student satisfaction. Although the relationship between learning styles and student performance has been widely researched, there is less research on the impact of learning styles on student satisfaction.
This paper explores the relationship between learning preferences and student satisfaction with traditional and online learning. The topic of student satisfaction with online courses is especially relevant due to increased efforts to provide online courses at colleges and universities. According to the Sloan Consortium's Five Pillars of Quality Online Education (Sloan, n.d.), "student satisfaction is the most important key to continuing learning." As enrollment in online courses continues to increase in significant numbers, there is a need to understand factors that affect student satisfaction with online learning given its impact on continued learning, retention, and student recruitment efforts.
Learning style describes individual factors that may be relatively stable over time as well as individual preferences that may be affected by motivation or interest (Dunn, DeBello, Brennan, Kreimsky & Murrain, 1981). Dunn, Beaudry and Klavas (1989) define learning style as "a biologically and developmentally imposed set of personal characteristics that make the same teaching method effective for some and ineffective for others" (p. 50).
Various learning styles have been used to explore the relationship between course format (online or traditional) and student performance and student satisfaction. (See Table 1.) Early work in the area of learning style and course format was conducted by Diaz and Cartnal (1999) who compared the learning styles of undergraduate students who self-selected to online or oncampus content-equivalent classes in health education. Diaz and Cartnal selected the GrashaReichmann Student Learning Style Scales (GRSLSS) which focuses on the social interaction that occurs between and among the instructor and the students. The GRSLSS characterizes students as independent, dependent, competitive, collaborative, avoidant, or participant learners. The significant finding of this study showed online students had higher scores on the independent learning style scale and lower scores on the dependent learning scales. Diaz and Cartnal (1999) suggest teaching strategies for online classes, specifically that instructors should emphasize independent learning opportunities for online learners.
In similar research, Lu, Yu and Liu (2002) used the Group Embedded Figures Test (GEFT) to explore the impact of learning style, learning patterns, and selected demographic factors on student performance in an online course. Developed by Witkin, Moore, Goodenough, and Cox (1977), GEFT identifies learners as field-dependent or field independent. Fielddependent students prefer using fact-based judgment to take an analytical approach to learning, whereas field-independent students prefer using intuitive judgment to take a global approach to learning (NDE, n.d.). Using GEFT, Lu, Yu, and Liu (2002) found learning style had no impact on student achievement (i.e., test scores) in an online course.
In a study on the relationship between learning style and student satisfaction, Downing and Chim (2004) used Honey and Mumford's (2000) Learning Style Questionnaire to compare online and traditional students. Honey and Mumford (2000) describe learners as activists, reflectors, theorists, and pragmatists. Activists prefer immediate experiences; reflectors prefer to observe others. Theorists prefer to analyze and synthesize information, and pragmatists like to act quickly to try out new ideas. In this study, student satisfaction was measured with an online standard course feedback questionnaire. Downing and Chim (2004) found reflectors in online classes had higher student satisfaction while other learning style groups showed no significant relationships between learning style and student satisfaction. They conclude that "the use of online learning significantly improves satisfaction levels for reflectors" (p. 273).
Kolb's (1976) Learning Style Inventory (LSI) is popular in research relating to both traditional and online education (Kozub, 2010; Manochehri & Young, 2006; Santo, 2006; Zhang & Bonk, 2008). The LSI (Kolb, 1976) measures learner strengths and weaknesses according to four learning abilities: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Using combinations of these four learning abilities, the LSI defines four dominant learning styles: converger, diverger, assimilator, and accommodator. Convergers are strong in abstract conceptualization and active experimentation, while divergers are strong in concrete experience and reflective observation. Assimilators are strong in abstract conceptualization and reflective observation, while accommodators are strong in concrete experience and active experimentation (Kolb, 1976).
Manochehri and Young (2006) used Kolb's Learning Style Inventory to evaluate differences between web-based and traditional instructor-based course delivery on student knowledge and student satisfaction. Although they found no significant difference in the relationship between learning style and student satisfaction, they report finding a significant difference in student satisfaction based on teaching methodology such that students in the instructor-led classes reported higher satisfaction than did students in the web-based classes. Similarly, they found no significant difference in the relationship between instruction methodology and student knowledge, but report finding a significant difference in student knowledge based on learning style. In this study, student knowledge was measured by the final exam score and student satisfaction was measured by a course evaluation survey instrument.
To consider how learning style affects student performance and student enjoyment in an online environment, Kozub (2010) conducted an experiment in which traditional students were randomly assigned to a web-based treatment group for a required course component. One group used a text-based web module; the second group used a web module with multimedia and interactive components in addition to text. Using Kolb's (1976) LSI, Kozub (2010) found learning style had no impact on student satisfaction or student performance for either type web module. In this study, satisfaction was measured by survey items pertaining to the likeability of the online modules, and performance was measured by test scores on content covered previously in the online module (Kozub, 2010).
Santo (2006) notes there are mixed results on the relationship between learning style and online learning. In a comprehensive review of eight highly regarded learning styles including the GRSLSS, Kolb's LSI, and GEFT, Santo (2004) argues the learning style construct is vague, that learning style instruments tend to be self-assessment that rely on honesty and self-awareness, and that these instruments tend to be low in reliability and validity.
The VARK Questionnaire
The VARK Learning Style Questionnaire was developed by Fleming and Mills (1992) to help students understand and adapt their individual learning preferences. VARK focuses on the sensory modality dimension of learning, that is, the way that information is taken in and processed by a learner: visual, aural, read/write, or kinesthetic. Visual learners prefer graphical and symbolic information. Aural learners prefer lectures, tutorials and discussion. Read/write learners prefer printed information. And kinesthetic learners prefer experience and practice using multiple perceptual modes including sight, sound, and touch (Fleming & Mills, 1992). Leite, Svinicki and Shi (2010) examined the dimensionality of VARK, and conducted multi-trait multi-method confirmatory factory analysis (MTMM-CFA) to validate its internal structure. Their analysis produced reliability estimates of .85, .82, .84, and .77 for the visual, aural, read/write, and kinesthetic subscales of VARK and validated its use as a diagnostic tool (Leite, Svinicki, & Shi, 2010).
The VARK instrument has become a popular learning style instrument because it is based on real-life situations that users easily relate to (Rogers, 2009) and because it is easy to use (Leite, Svinicki, & Shi, 2010). Additionally, VARK has been used in various ways to explore student preferences for course delivery mode, assessment method, and course effectiveness. Rogers (2009) used the VARK instrument to survey traditional undergraduate students to increase student awareness of individual learning preferences and guide the adaptation of teaching methods to accommodate all learning styles. Zapalska and Brozik (2006) used the VARK instrument to identify the learning styles of online students and suggest instructional strategies for an online environment and expected behaviors for each type of learner. Becker, Kehoe and Tennent (2007) surveyed undergraduate students to find VARK styles do not impact student preference for course delivery methods (face-to-face or online delivery) or preferences for assessment approaches. Boatman, Courtney and Lee (2008) used VARK to classify students and evaluate performance measured as pre-test/post-test score differences. In this study, students with a strong visual preference earned higher scores in an introduction to economics course, a finding the authors claim is not surprising given that the course require students to create and interpret graphs to explain basic economic concepts.
In additional research on the relationship between VARK learning style and course effectiveness, Drago and Wagner (2004) classified students in various combinations of visual, aural, read/write and kinesthetic scores to compare learning style with "course effectiveness." In that study, course effectiveness was measured by summed responses to four survey items (reported as an effectiveness score): (1) This course contributes to preparation for my professional career. (2) I would recommend this course to friends/ colleagues. (3) I have learned a lot in this course. (4) I have enjoyed taking this course. Drago and Wagner (2004) report three significant findings: (1) Online students scored higher on three learning styles (V, A, R); (2) The effectiveness index scores for online students with a read/write (R) learning style and those with a multimodal VARK learning style were lower when compared to all other online students; and (3) The effectiveness index scores for online students with a combination Aural and Read/write learning style (AR) were higher when compared to all other online students.
Course effectiveness as measured by Drago and Wagner (2004) can be compared to student satisfaction defined by Sweeney and Ingram (2001) as the "perception of enjoyment and accomplishment in the learning environment" (p. 27). Drago and Wagner note "research on the link between learning styles and online education is underdeveloped" (p. 2). The research reported here seeks to address this gap by exploring the relationship between learning style and student satisfaction in both traditional and online courses. Specifically, this study addresses four research questions:
1. Are there differences in learning styles between students who enroll in a traditional class and those who enroll in an online class?
2. Does learning style significantly impact student satisfaction?
3. Does course delivery format significantly impact student satisfaction?
4. How do the results of this study compare to similar studies conducted at other institutions?
Sample and Data Collection
Participants in this study were undergraduate students in multiple sections of a required junior-level Management Information Systems course in the College of Business at a public state university. The participants included students in face-to-face and Web-assisted (online) sections, taught by the same instructor. All sections utilized a common syllabus; students used the same textbook, completed the same coursework regardless of content delivery mechanism, and took the same in-class or proctored exams.
At the beginning of the term, students completed the VARK learning preference questionnaire that describes an individual's preference for taking in and putting out information in a learning context. At the end of the term, students completed an online student satisfaction questionnaire. The satisfaction questionnaire was synthesized from previous research in which items from validated survey instruments were reduced by evaluating the first factors and selecting the five highest inter-item correlations of each to create a measure of satisfaction with the course with a reliability of 0.93 (Sinclaire, Simon, Campbell & Wilkes, 2010). Students rated aspects of the course (i.e., course content, instructor, group project, course management system) on a five-point scale from (1) strongly disagree to (5) strongly agree. The five satisfaction questions used in this survey were:
1. I was satisfied with the content of this course.
2. Overall, I was satisfied with the course.
3. The instructor was effective for helping me learn the material.
4. The course was effective in facilitating my learning.
5. Overall, this course was very worthwhile.
Table 2 presents demographic information that shows the student subjects were dissimilar across the traditional and online sections. The Web-assist sections had a higher percentage of students in the older age groups as well as a higher percentage of female students. Additionally, differences in employment show the Web-assist students worked more hours per week than did the traditional students in this study.
RESULTS AND DISCUSSION
Research question one: Are there differences in learning style between students who enroll in a traditional class and those who enroll in an online class?
Table 3 shows the results of a t-test for equality of means that found no significant difference in learning styles between students enrolled in the traditional sections and those enrolled in the online sections of the class.
Research question two: Does learning style significantly impact student satisfaction?
Table 4 shows the distribution of traditional students across 16 learning style categories and the mean satisfaction index scores for category and the mean score for each of the five items that comprise the satisfaction index. A t-test for equality of means was used to compare the satisfaction scores of students who evidence a particular learning style category with those that did not. Considering students in the traditional sections, there are two significant findings: Students with a multimodal aural, read/write and kinesthetic (ARK) learning style report lower satisfaction with the course, and students with a multimodal visual, aural, read write and kinesthetic (VARK) score report higher satisfaction with the course.
Table 5 shows the distribution of Web-assist (online) students across 16 learning style categories and the mean satisfaction index scores for each learning style category and the mean score for each of the five items that comprise the satisfaction index. Although findings show two significant differences on individual survey items for two learning style categories, overall there were no differences found in student satisfaction index scores relating to learning style for students in the online sections of the class.
Research question three: Does course delivery format significantly impact student satisfaction?
Table 6 presents means analysis that show a statistically significant difference in satisfaction with the course between traditional and web-assisted that reflect a main effect for learning environment and satisfaction with the course. Students in the Web-assist sections report higher satisfaction with the course than students in the traditional sections.
Research question four: How do the results of this study compare to similar studies conducted at other institutions?
Regarding research question one and the relationship between learning style and course format, using the VARK learning style questionnaire, this study found no differences in learning style when comparing students in traditional and online sections of the same course. This finding is similar to that of Becker, Kehoe, and Tennent (2007) that reports VARK learning style does not influence student preference for course delivery method (i.e., class format). Regarding research question two and the relationship between learning style and student satisfaction, the study presented here found learning style does not impact satisfaction with the course for online students; however, learning style impacts satisfaction with the course for traditional students with multimodal ARK or multimodal VARK learning styles. This finding is inconsistent with the results reported by Kozub (2010) and Manochehri and Young (2006) in which learning style had no impact on student satisfaction for traditional or online students. And it is inconsistent with findings reported by Drago and Wagner that found differences in student satisfaction for online students with R, AR and VARK learning styles.
Regarding research question three and the relationship between course delivery format and student satisfaction, the study presented here found Web-assist students report higher satisfaction with the course than traditional students. This is inconsistent with findings reported by Manochehri and Young (2006) that reports higher student satisfaction with traditional classes.
Research presented in this paper examines the relatively unexplored area of learning preference and student satisfaction with online learning. The results of the study indicate that student learning styles are not statistically significant for course delivery mode. In this study, the VARK learning preferences reported by traditional students are not significantly different from the VARK learning preferences reported by web-assist students. This conclusion is consistent with previous findings (Lu, Yu, & Liu, 2002; Becker, Kehoe, & Tennent, 2007) that student learning preferences do not influence student preference for course delivery mode.
This study also found VARK learning preferences to be statistically significant in relation to student satisfaction only for two specific learning preferences in the traditional classroom environment such that students reporting multimodal ARK and VARK learning preference reported higher satisfaction with the course. For online students, learning preference was not significantly important in relation to student satisfaction. These conclusions are inconsistent with findings reported by Manochehri and Young (2006) that found no significant difference for student satisfaction and learning preference in both online and traditional courses.
Additionally, this study found student satisfaction to be higher for students in the web-assist sections. This result is inconsistent with research reported by Manochehri and Young (2006) that found a significant difference in student satisfaction, with greater satisfaction reported by students in a traditional, instructor-led class.
The contribution of this paper is two-fold. First, it presents evidence that learning preference does not affect student satisfaction with online learning. Second, it presents evidence of higher satisfaction with online learning in courses that differ only by method of delivery. This research adds to a growing body of work on the relationship between learning preferences and student satisfaction with online learning. As online programs continue to proliferate, there is a need to understand factors that affect student satisfaction with online learning given its impact on continued learning, retention, and student recruitment efforts.
There are a number of limitations to this study, and generalizing these results to other courses or programs may be misleading. One limitation of this study was the use of a convenience sample of students who self-selected to traditional sections or online sections of a required business course. A second limitation is the use of self-report data. Additionally, using only the VARK instrument to measure student learning preference may result in monomethod bias. Finally, sample size is a concern because several VARK groups had relatively few members.
This work was supported by a grant from the College of Business at Arkansas State University, Jonesboro, AR. The VARK questionnaire was used by permission. [C] Copyright version 7.0 (2006) held by Neil D. Fleming, Christchurch, New Zealand and Charles C. Bonwell, Green Mountain Falls, Colorado 80819 U.S.A.
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About the Author:
Jollean K. Sinclaire is an Assistant Professor of Computer and Information Technology at the College of Business at Arkansas State University. Her doctorate is from the University of Memphis. Her research interests are in IT risk management, information privacy and security, distance learning, and social media.
Jollean K Sinclaire
Arkansas State University
Table 1 Summary of Sample Studies on Learning Style and Course Delivery Mode Learning Author Title Style Model Becker, Impact of personalized VARK Kehoe, & learning styles on Tennent online delivery and (2007) assessment Boatman, See how they learn: the VARK Courtney & impact of faculty and Lee (2008) student learning styles on student performance in introductory economics Diaz & Students' Learning Grasha- Cartnal Styles in Two Classes Reichmann (1999) scale (GRSLSS) Downing & Reflectors as online Honey and Chim extraverts? Mumford's (2004) LSQ Drago & VARK Preferred VARK Wagner Learning Styles and (2004) Online Education Kozub An ANOVA Analysis Kolb's LSI (2010) of the Relationships between Business Students' Learning Styles and Effectiveness of Web Based Instruction Lu, Yu & Learning style, learning Group Liu (2002) patterns, and learning Embedded performance in a Figures Test WebCT-based MIS (GEFT) to course categorize students as field- independent or field-dependent Manochehri The Impact of Student Kolb's LSI & Young Learning Styles with (2006) Web-Based Learning or Instructor-Led Learning on Student Knowledge and Satisfaction Rogers A preliminary VARK (2009) investigation and analysis of student learning style preferences in further and higher education Santo Relationship between Review of LS (2006) Learning Styles and models: Online Learning: Myth * Grasha- or Reality? Riechmann * Chellens & Valcke * Kolb's LSI * 4MAT * GEFT Zapalska & Learning styles and VARK Brozik online education (2006) Author Class Format Dependent Variable Becker, Traditional and Preference Kehoe, & online under- for course Tennent graduate. delivery (2007) N=891 Boatman, Traditional Learning Courtney & undergraduate. Performance Lee (2008) N=119 (pre-test post-test score difference) Diaz & Traditional and Learning Cartnal online under- style (1999) graduate with equivalent content. N=108 Downing & Traditional and Student Chim online under- Satisfaction (2004) graduate. N=32 matched pairs Drago & Traditional and Perceived Wagner online graduate. online (2004) N=316 course effectiveness score (comparable to student satisfaction) Kozub Traditional Student (2010) undergraduate Satisfaction with web (enjoyment) component. and N=159 performance Lu, Yu & Web-based Learning Liu (2002) graduate. Performance N=76 (Learning effectiveness based on test scores) Manochehri Traditional and Knowledge & Young online under- (final exam (2006) graduate. grade) and N=94 Student Satisfaction Rogers Traditional Learning (2009) undergraduate. style Santo Theoretical (2006) paper explores relationship between learning styles and online learning Zapalska & Online Learning Brozik undergraduate. style (2006) N=25 Author Findings Becker, Learning style does Kehoe, & not influence student Tennent preference for course (2007) delivery methods or assessment methods Boatman, Students with strong Courtney & V preference had Lee (2008) higher performance. Diaz & Online students had Cartnal higher scores on (1999) independent LS scale and lower scores on dependent LS scale. Downing & Online reflectors Chim report higher SS than (2004) traditional reflectors. Drago & Online more likely Wagner to be V and R; Online (2004) Multimodal VARK and Strong R rate effectiveness lower; AR rate effectiveness higher. Kozub Learning style has no (2010) impact on SS. Lu, Yu & Learning style had no Liu (2002) impact on learning performance. Manochehri No significant & Young difference for (2006) relationship between LS and SS. LS impacts knowledge in online class. Instructor-led class had higher SS. Rogers Identifies learning (2009) preferences to (1) promote student awareness of LS and (2) adapt teaching methods to all LS. Santo Concludes the (2006) learning style construct is vague; LS instruments tend to be self assessments and require honesty and self-awareness; and LS instruments tend to have low reliability and validity. Zapalska & Offers teaching Brozik strategies for each (2006) learning preference Table 2 Participant Demographics Traditional Web-assist N=161 N=137 Age 18-24 73% 54% 25-34 20% 30% 35 & older 7% 16% Gender Female 47% 60% Male 53% 40% Employment 1 to 20 hrs/week 32% 20% 21 to 40 hrs/week 62% 62% over 40 hrs/week 6% 18% Table 3 Group Format Statistics N Mean Visual score Web-assist 137 4.51 Traditional 161 4.48 Aural score Web-assist 137 5.52 Traditional 161 5.70 Read/write score Web-assist 137 6.99 Traditional 161 6.42 Kinesthetic score Web-assist 137 6.75 Traditional 161 6.73 Group Format Std. Deviation Visual score Web-assist 3.056 Traditional 2.777 Aural score Web-assist 2.573 Traditional 3.050 Read/write score Web-assist 3.341 Traditional 3.152 Kinesthetic score Web-assist 2.812 Traditional 3.102 Table 4 Traditional Students: Independent Samples T-test for Equality of Means Satisfaction Index Q1 Category N Index Mean Mean Score Visual 5 20.000 4.20 Remainder 156 19.013 3.88 Aural 9 17.333 3.56 Remainder 152 19.145 3.91 Read/write 17 18.412 3.76 Remainder 144 19.118 3.91 Kinesthetic 21 18.619 3.67 Remainder 140 19.107 3.93 Visual & Aural 6 19.000 4.00 Remainder 155 19.045 3.89 Visual & Read/write 4 20.000 4.25 Remainder 157 19.019 3.89 Visual & Kinesthetic 6 19.333 4.00 Remainder 155 19.032 3.89 Aural & Read/write 3 21.667 4.33 Remainder 158 18.994 3.89 Aural & Kinesthetic 6 16.833 3.67 Remainder 155 19.129 3.90 Read/write & Kinesthetic 6 18.167 3.50 Remainder 155 19.077 3.91 Visual, Aural & Read/write 13 19.846 3.85 Remainder 148 18.973 3.90 Visual, Aural & Kinesthetic 14 20.429 4.21 Remainder 147 18.912 3.86 Visual, Read/write & Kinesthetic 0 -- -- Remainder 161 19.044 3.89 Aural, Read/write & Kinesthetic 3 13.333 ** 2.33 * Remainder 158 19.152 ** 3.92 * Visual, Aural, Read/write 25 20.760 * 4.36 * & Kinesthetic Remainder 136 18.728 * 3.81 * None 23 18.174 3.78 Remainder 138 19.188 3.91 Q2 Q3 Category Mean Score Mean Score Visual 4.20 3.80 Remainder 3.83 3.88 Aural 3.22 3.44 Remainder 3.88 3.91 Read/write 3.53 3.94 Remainder 3.88 3.88 Kinesthetic 3.71 3.86 Remainder 3.86 3.89 Visual & Aural 3.83 3.67 Remainder 3.84 3.89 Visual & Read/write 4.25 3.75 Remainder 3.83 3.89 Visual & Kinesthetic 4.17 4.17 Remainder 3.83 3.87 Aural & Read/write 4.67 4.67 Remainder 3.82 3.87 Aural & Kinesthetic 3.33 3.33 Remainder 3.86 3.90 Read/write & Kinesthetic 3.50 3.67 Remainder 3.85 3.89 Visual, Aural & Read/write 4.00 3.92 Remainder 3.82 3.88 Visual, Aural & Kinesthetic 4.07 4.29 Remainder 3.82 3.84 Visual, Read/write & Kinesthetic -- -- Remainder 3.84 3.88 Aural, Read/write & Kinesthetic 2.67 ** 3.67 Remainder 3.86 ** 3.89 Visual, Aural, Read/write 4.24 * 4.12 & Kinesthetic Remainder 3.76 * 3.84 None 3.78 3.65 Remainder 3.85 3.92 Q4 Q5 Category Mean Score Mean Score Visual 4.00 * 3.80 Remainder 3.79 * 3.63 Aural 3.67 3.44 Remainder 3.80 3.64 Read/write 3.76 3.41 Remainder 3.80 3.66 Kinesthetic 3.76 3.62 Remainder 3.80 3.64 Visual & Aural 4.17 3.33 Remainder 3.78 3.65 Visual & Read/write 4.00 * 3.75 Remainder 3.79 * 3.63 Visual & Kinesthetic 3.50 3.50 Remainder 3.81 3.64 Aural & Read/write 4.33 3.67 Remainder 3.78 3.63 Aural & Kinesthetic 3.33 3.17 Remainder 3.81 3.65 Read/write & Kinesthetic 3.50 4.00 Remainder 3.81 3.62 Visual, Aural & Read/write 4.08 4.00 Remainder 3.77 3.60 Visual, Aural & Kinesthetic 4.00 3.86 Remainder 3.78 3.61 Visual, Read/write & Kinesthetic -- -- Remainder 3.80 3.63 Aural, Read/write & Kinesthetic 2.67 * 2.00 * Remainder 3.82 * 3.66 * Visual, Aural, Read/write 4.00 4.04 * & Kinesthetic Remainder 3.76 3.56 * None 3.57 3.39 Remainder 3.83 3.67 * p< 0.01 ** p< 0.05 Table 5 Web-Assist Students: Independent Samples T-test for Equality of Means Satisfaction Index Q1 Category N Index Mean Mean Score Visual 5 22.200 4.60 Remainder 132 20.205 4.21 Aural 16 19.000 4.00 Remainder 121 20.446 4.26 Read/write 13 19.462 4.23 Remainder 124 20.363 4.23 Kinesthetic 16 20.938 4.56 Remainder 121 20.190 4.18 Visual & Aural 2 18.500 4.00 Remainder 135 20.304 4.23 Visual & Read/write 7 22.571 4.71 Remainder 130 20.154 4.20 Visual & Kinesthetic 3 20.667 4.33 Remainder 134 20.269 4.22 Aural & Read/write 2 22.000 4.50 Remainder 135 20.252 4.22 Aural & Kinesthetic 3 17.000 3.67 Remainder 134 20.351 4.24 Read/write & Kinesthetic 4 19.250 3.75 Remainder 133 20.308 4.24 Visual, Aural & Read/write 4 19.500 3.75 Remainder 133 20.301 4.24 Visual, Aural & Kinesthetic 9 17.889 3.78 Remainder 128 20.445 4.26 Visual, Read/write & Kinesthetic 11 19.818 4.18 Remainder 126 20.318 4.23 Aural, Read/write & Kinesthetic 7 21.571 4.57 Remainder 130 20.208 4.21 Visual, Aural, Read/write 20 20.900 4.20 & Kinesthetic Remainder 117 20.171 4.23 None 15 21.333 4.27 Remainder 122 20.148 4.22 Q2 Q3 Category Mean Score Mean Score Visual 4.40 4.40 Remainder 4.13 3.96 Aural 3.81 3.50 ** Remainder 4.18 4.04 ** Read/write 4.00 3.85 Remainder 4.15 3.99 Kinesthetic 4.50 4.06 Remainder 4.09 3.97 Visual & Aural 3.50 4.00 Remainder 4.15 3.98 Visual & Read/write 4.71 4.43 Remainder 4.11 3.95 Visual & Kinesthetic 4.33 4.00 Remainder 4.13 3.95 Aural & Read/write 4.50 4.50 Remainder 4.13 3.97 Aural & Kinesthetic 4.00 3.00 Remainder 4.14 4.00 Read/write & Kinesthetic 3.75 4.00 Remainder 4.15 3.98 Visual, Aural & Read/write 4.25 4.00 Remainder 4.14 3.98 Visual, Aural & Kinesthetic 3.67 3.44 Remainder 4.17 4.02 Visual, Read/write & Kinesthetic 3.82 4.18 Remainder 4.17 3.96 Aural, Read/write & Kinesthetic 4.29 4.29 Remainder 4.13 3.96 Visual, Aural, Read/write 4.30 4.05 & Kinesthetic Remainder 4.11 3.97 None 4.20 4.20 Remainder 4.13 3.95 Q4 Q5 Category Mean Score Mean Score Visual 4.20 4.60 Remainder 3.94 3.96 Aural 3.94 3.75 Remainder 3.95 4.02 Read/write 3.69 3.69 Remainder 3.98 4.02 Kinesthetic 3.75 4.06 Remainder 3.98 3.98 Visual & Aural 3.50 3.50 Remainder 3.96 3.99 Visual & Read/write 4.43 4.29 Remainder 3.92 3.97 Visual & Kinesthetic 4.00 4.00 Remainder 3.95 3.99 Aural & Read/write 4.00 4.50 Remainder 3.95 3.98 Aural & Kinesthetic 3.33 3.00 Remainder 3.96 4.01 Read/write & Kinesthetic 3.75 4.00 Remainder 3.95 3.98 Visual, Aural & Read/write 3.75 3.75 Remainder 3.95 3.99 Visual, Aural & Kinesthetic 3.44 3.56 Remainder 3.98 4.02 Visual, Read/write & Kinesthetic 3.82 3.82 Remainder 3.96 4.00 Aural, Read/write & Kinesthetic 4.00 4.43 Remainder 3.95 3.96 Visual, Aural, Read/write 4.20 4.15 & Kinesthetic Remainder 3.91 3.96 None 4.40 ** 4.27 Remainder 3.89 ** 3.95 * p< 0.01 ** p< 0.05 Table 6 Group Statistics Std. Format N Mean Deviation Satisfaction Web-assist 137 4.078 ** .821 with Course Traditional 161 3.809 ** .764 ** p< 0.05
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|Author:||Sinclaire, Jollean K.|
|Publication:||International Journal of Education Research (IJER)|
|Date:||Mar 22, 2012|
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