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The Relationship Between Learning Styles and Online Education Among Entry-Level Doctor of Pharmacy Degree Students

Permanent Link: http://ufdc.ufl.edu/UFE0041054/00001

Material Information

Title: The Relationship Between Learning Styles and Online Education Among Entry-Level Doctor of Pharmacy Degree Students
Physical Description: 1 online resource (85 p.)
Language: english
Creator: Bernier, Jose
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: education, learning, online, styles
Educational Administration and Policy -- Dissertations, Academic -- UF
Genre: Higher Education Administration thesis, Ed.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Education THE RELATIONSHIP BETWEEN LEARNING STYLES AND ONLINE EDUCATION AMONG ENTRY-LEVEL DOCTOR OF PHARMACY DEGREE STUDENTS By Jose Bernier December 2009 Chair: Dale F. Campbell Major: Higher Education Administration The goal of this study is to investigate the relationship between the preferred learning styles of professional pharmacy students and their performance/success in a hybrid PharmD pharmacy program making extensive use of online content. Consequently, the study will look for early detection factors which can impact the performance of the student enrolled in the program. A post hoc dataset using data collected from the 16 question ?VARK? questionnaire will be used. VARK is a questionnaire that provides users with a profile of their learning preferences. The ultimate goal of the study is to improve the online curriculum to better accommodate students with different learning styles.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jose Bernier.
Thesis: Thesis (Ed.D.)--University of Florida, 2009.
Local: Adviser: Campbell, Dale F.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041054:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041054/00001

Material Information

Title: The Relationship Between Learning Styles and Online Education Among Entry-Level Doctor of Pharmacy Degree Students
Physical Description: 1 online resource (85 p.)
Language: english
Creator: Bernier, Jose
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: education, learning, online, styles
Educational Administration and Policy -- Dissertations, Academic -- UF
Genre: Higher Education Administration thesis, Ed.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Education THE RELATIONSHIP BETWEEN LEARNING STYLES AND ONLINE EDUCATION AMONG ENTRY-LEVEL DOCTOR OF PHARMACY DEGREE STUDENTS By Jose Bernier December 2009 Chair: Dale F. Campbell Major: Higher Education Administration The goal of this study is to investigate the relationship between the preferred learning styles of professional pharmacy students and their performance/success in a hybrid PharmD pharmacy program making extensive use of online content. Consequently, the study will look for early detection factors which can impact the performance of the student enrolled in the program. A post hoc dataset using data collected from the 16 question ?VARK? questionnaire will be used. VARK is a questionnaire that provides users with a profile of their learning preferences. The ultimate goal of the study is to improve the online curriculum to better accommodate students with different learning styles.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jose Bernier.
Thesis: Thesis (Ed.D.)--University of Florida, 2009.
Local: Adviser: Campbell, Dale F.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041054:00001


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1 THE RELATIONSHIP BETWEEN LEARNING STYLES AND ONLINE EDUCATION AMONG ENTRY LEVEL DOCTOR OF PHARMACY DEGREE STUDENTS By JOSE BERNIER A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF EDUCATION UNIVERSITY OF FLORIDA 2009

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2 2009 Jose Bernier

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3 To my family

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4 ACKNOWLEDGMENTS I thank my family and committee members for their continuous support throughout the entire process. Dr. Campbell for his leadership and encouragement, Dr. Oliver for her continuous support during my time at the Division of Continuing education and afterwards, Dr. Honeyman for making himself available to answer my questions, and I also th ank Dr. Ried for making the data that he collected over the years available to me. I want to finally thank the College of Education administrative office for their help throughout the process.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ ........ 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 12 Introducti on ................................ ................................ ................................ ............. 12 Statement of the Problem ................................ ................................ ....................... 13 Purpose ................................ ................................ ................................ .................. 14 Research Questions ................................ ................................ ............................... 14 Research Hypothesis ................................ ................................ .............................. 15 Significance of the S tudy ................................ ................................ ........................ 15 Limitations ................................ ................................ ................................ ............... 15 Conceptual Framework ................................ ................................ ........................... 16 Distance Education and Learning Styles ................................ ................................ 16 Definition of Terms ................................ ................................ ................................ .. 17 2 REVIEW OF LITERATURE ................................ ................................ .................... 19 Origins of Online Professional Education Programs ................................ ............... 19 Quality of Online Professional Education Programs ................................ ............... 20 College of Pharmacy PharmD Program ................................ ................................ .. 22 Learning Styles ................................ ................................ ................................ ....... 23 VARK ................................ ................................ ................................ ................ 25 The VARK categories ................................ ................................ ....................... 25 Visual (V) ................................ ................................ ................................ ... 25 Aural / Auditory (A) ................................ ................................ ..................... 26 Read/write (R) ................................ ................................ ............................ 26 Kinesthetic (K) ................................ ................................ ............................ 26 What about m ixtures? m ultimodals (MM) ................................ ................... 26 Learning Styles Inventory (LSI) ................................ ................................ ........ 27 Gregorc Learning Style Model ................................ ................................ .......... 28 Learning and Study Strategies Inventory (LASSI) ................................ ............ 29 Benefits of Learning Styles Research to Students and I nstructors ......................... 31 3 METHODOLOGY ................................ ................................ ................................ ... 33 Participants ................................ ................................ ................................ ............. 33 Study Design ................................ ................................ ................................ .......... 33

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6 Operational Definition of Variables ................................ ................................ ......... 34 Instrumentation ................................ ................................ ................................ 34 Validity and Reliability ................................ ................................ ....................... 35 Data Collection ................................ ................................ ................................ 36 Data Analysis ................................ ................................ ................................ ... 36 Specific Aim 1 ................................ ................................ ................................ ... 36 Specific Aim 2 ................................ ................................ ................................ ... 37 4 FINDINGS ................................ ................................ ................................ ............... 38 Frequencies for the V a riables of I nterest ................................ ................................ 38 Correlations ................................ ................................ ................................ ............ 44 Oneway Analysis of Variance (ANOVA) and T Test ................................ ............... 45 Female and Male C omparison on NABP and GPA ................................ .......... 45 White and N on white C omparison on NABP and GPA ................................ ..... 47 Oneway Analysis of Variance (ANOVA) ................................ .................... 47 Visual and Non Visual Learning Styles C omparison on NABP and GPA ......... 49 Oneway Analysis of Variance (ANOVA) ................................ .................... 49 Aural and Non Aural Learning Style comparison on NABP and GPA ............... 51 Oneway Analysis of Variance (ANOVA) ................................ .................... 51 Read/Write and non Read/Write Learning Styles comparison on NABP and GPA ................................ ................................ ................................ .............. 53 Oneway Analysis of Variance (ANOVA) ................................ .................... 53 Kinesthetic and Non Kinesthetic Learning Style comparison on NABP and GPA ................................ ................................ ................................ .............. 55 Oneway Analysis of Variance (ANOVA) ................................ .................... 55 Multi Modal and Non Multi Modal comparison on NABP and GPA .................. 56 Oneway Analysis Of Variance (Anova) ................................ ...................... 56 T Test for G roups Female versus Male ................................ ............................ 58 T Test for G roups White versus Not White ................................ ....................... 59 T Test for G roups Visual versus Not Visual ................................ ...................... 60 T Test for G roups Aural versus Not Aural ................................ ........................ 61 T Test for G roups Read/Write versus Not Read/Write ................................ ..... 62 T Test for G roups Kinesthetic versus Not Kinesthetic ................................ ...... 63 T Test for G roups Multi Modal versus Not Multi Modal ................................ .... 64 5 CONCLUSION ................................ ................................ ................................ ........ 66 APPENDIX A THE VARK QUESTIONNAIRE ................................ ................................ ............... 69 B DATA DICTIONARY ................................ ................................ ............................... 73 LIST OF REFERE NCES ................................ ................................ ............................... 80 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 85

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7 LIST OF TABLES Table page 2 1 Five years of growth in Online Learning ................................ ............................. 20 4 1 Frequencies ................................ ................................ ................................ ........ 38 4 2 Race ................................ ................................ ................................ ................... 38 4 3 Age ................................ ................................ ................................ ..................... 39 4 4 GPA ................................ ................................ ................................ .................... 40 4 5 NABP Score ................................ ................................ ................................ ....... 42 4 6 Visual or Not Visual ................................ ................................ ............................ 43 4 7 Aural or Not Aural ................................ ................................ ............................... 43 4 8 Read or Not Read ................................ ................................ .............................. 43 4 9 Kinesthetic or Not Kinesthetic ................................ ................................ ............. 44 4 10 MultiModal or Not MultiModal ................................ ................................ ............. 44 4 11 Correlations ................................ ................................ ................................ ........ 45 4 12 Correlations ................................ ................................ ................................ ........ 45 4 12 Descript ives ................................ ................................ ................................ ........ 46 4 13 Descriptives ................................ ................................ ................................ ........ 46 4 14 Test of Homogeneity of Variances ................................ ................................ ...... 46 4 15 ANOVA ................................ ................................ ................................ ............... 47 4 16 Descriptives ................................ ................................ ................................ ........ 48 4 17 Descriptives ................................ ................................ ................................ ........ 48 4 18 Test of Homogeneity of Variances ................................ ................................ ...... 48 4 19 ANOVA ................................ ................................ ................................ ............... 49 4 20 Descriptives ................................ ................................ ................................ ........ 49 4 21 Descriptives ................................ ................................ ................................ ........ 50

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8 4 22 Test of Homogeneity of Variances ................................ ................................ ...... 50 4 23 ANOVA ................................ ................................ ................................ ............... 50 4 24 Descriptives ................................ ................................ ................................ ........ 51 4 25 Descriptives ................................ ................................ ................................ ........ 51 4 26 Test of Homogeneity of Variances ................................ ................................ ...... 52 4 27 ANOVA ................................ ................................ ................................ ............... 52 4 28 Descriptives ................................ ................................ ................................ ........ 53 4 29 Descriptives ................................ ................................ ................................ ........ 53 4 30 Test of Homogeneity of Variances ................................ ................................ ...... 54 4 31 ANOVA ................................ ................................ ................................ ............... 54 4 32 Descriptives ................................ ................................ ................................ ........ 55 4 33 Descript ives ................................ ................................ ................................ ........ 55 4 34 Test of Homogeneity of Variances ................................ ................................ ...... 55 4 35 ANOVA ................................ ................................ ................................ ............... 56 4 36 Descriptives ................................ ................................ ................................ ........ 57 4 37 Descript ives ................................ ................................ ................................ ........ 57 4 38 Test of Homogeneity of Variances ................................ ................................ ...... 57 4 39 ANOVA ................................ ................................ ................................ ............... 58 4 40 Group Statisti cs ................................ ................................ ................................ .. 58 4 41 Independent Samples Test ................................ ................................ ................. 59 4 42 Group Statistics ................................ ................................ ................................ .. 59 4 43 Independent Samples Test ................................ ................................ ................. 60 4 44 Group Statistics ................................ ................................ ................................ .. 61 4 45 Independent Samples Test ................................ ................................ ................. 61 4 46 Group Statistics ................................ ................................ ................................ .. 62

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9 4 47 Inde pendent Samples Test ................................ ................................ ................. 62 4 48 Group Statistics ................................ ................................ ................................ .. 63 4 49 Independent Samples Test ................................ ................................ ................. 63 4 50 Group Statistics ................................ ................................ ................................ .. 64 4 51 Inde pendent Samples Test ................................ ................................ ................. 64 4 51 Group Statistics ................................ ................................ ................................ .. 65 4 52 Independent Samples Test ................................ ................................ ................. 65

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10 LIST OF FIGURES Figure pag e 2 1 VARK Learning Model (from Hawk and Shah) ................................ ................... 27 2 2 Kolb LSI scoring ................................ ................................ ................................ 28 2 3 Gregor c Style Delineator (from Hawk and Shah) ................................ ................ 29 2 4 LASSI Scores ................................ ................................ ................................ ..... 30 3 1 Factors impacting performance in online education ................................ ........... 34

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11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Education THE RELATIONSHIP BETWEEN LEARNING STYLES AND ONLINE EDUCATION AMONG ENTRY LEVEL DOCTOR OF PHARMACY DEGREE STUDENTS By Jose Bernier December 2009 Chair: Dale F. Campbell Major: Higher Education Administration The goal of this study is to investigate the relationship between the preferred learning styles of professional pharmacy students and their performance /success in a hybrid PharmD pharmacy program making extensive use of online content Consequently, the study will look for early detection factors w hich can impact the performance of the student enrolled in the program. A post hoc dataset using data collected from questionnaire that provides users with a profile of their learning preferenc es. The ultimate goal of the study is to improve the online curriculum to better accommodate students with different learning styles. The results showed that there is not relationship between the preferred learning style and their performance in the prog ram. However, looking at the implications for Higher Education Administration, the study also shows that when designing a hybrid course, faculty will benefit from understand ing the students and their learning styles to create a learner centered course whi ch would maximize the online learning experience.

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12 CHAPTER 1 INTRODUCTION Introduction The importance and impact of online education is undeniable in higher education According to Moloney & Oakley (2006), during the 2003 04 academic year there were approximately two million enrollments in online courses and demand is growing rapidly Some schools are struggling to keep up with the demand of conver ting classroom courses to the online environment. By 2005, about 3.2 million students took at least one online course (Foster & Carnvale, 2007) and enrol l ments are expected to grown at a yearly rate of 20% (Moloney & Oakley, 2006) The differences betwee n face to face and online courses may be obvious at first glance. H owever, distance education classes can be as effective and, in some ways, even more effective than face to face courses. The tools and technologies used for distance education courses fac ilitate learning opportunities not possible in the face to face classroom (Howard,Schenk, & Discenza, 2004). Although online education has advantages, i t is not for everyone. O nline education was not designed to replace face to face courses, but instead to enhance programs. For example, o nline classes can make the university more accessible to mature students who cannot physically come to campus sites. In addition, these classes provide greater flexibility to students who benefit from being able to contro l the time during which they study the course materials. If online classes do fulfill such needs, one would expect the online section to have older and nontraditional students ( Dutton, Dutton, & Perry, 2002).

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13 The o nline hybrid Doctor of Pharmacy ( P h armD ) program at a Southeastern University has encountered students who initially expressed concerns about the online components of the hybrid program and showed more interest in an all face to face approach. However, a number of these students ended up prefer ring the hybrid mode program. One of the most cited causes for the change in perception was the increased flexibility added by the online component. Early detection of these students could allow the college to better guide and serve them by designing a c ourse plan that fit their needs and by addressing any concerns or reservations the students may have had about the hybrid model. Statement of the Problem The goal of this study is to investigate the relationship between the preferred learning styles of p rofessional pharmacy students and their performance /success in a hybrid PharmD pharmacy program making extensive use of online content Consequently, the study will look for early detection factors which can impact the performance of the student enrolled in the program. A post hoc dataset using data collected from questionnaire that provides users with a profile of their learning preferences. These preferences are about the ways that they want to take in and give out information ( http://www.vark learn.com/english/page.asp?p=faq ). The sample population for this study was one of the cohorts that enrolled and graduated from a Southea stern University Pharmacy program (PharmD) and whose academic progress was followed for the duration of the program approximately 4 years Although VARK is not the only readily available survey to measure learning styles, the PharmD program utilized it b ecause there was no cost to administ e r it

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14 The ultimate goal of the study is to improve the online curriculum to better accommodate students with different learning styles. Traditionally, online education was designed to accommodate non traditional studen ts as well as students who live in an online course even though a face to face version is available. Coffield, Moseley, Hall, and Ecclestone (2004) suggested that students will b ecome more motivated to learn by knowing more about their own strengths and weakness as learners. Purpose The purpose of this study is to determine if there is a relationship between the preferred learning style of online student s and their performance in the online P h armD program at a Southeastern University The ultimate goal of the study is to improve the online curriculum to better accommodate students with different learning styles. Research Questions This study is based on a positivism theoretical framework, using a survey research methodology and surveys administered by the College of Pharmacy at a Southeastern University to the students of their PharmD program. Research questions include the followi ng: ) of academic performance as measured by Grade Point Average (GPA) ? Specifically 1. Is there a relationship between the demographics of the student and Grade Point Average (GPA)? 2. Is th ere a relationship between the preferred learning style of the student and the Grade Point Average (GPA)? 3. GPA and academic performance measured by National Association of Bards of Ph armacy Licensure Exam (NAPBLEX) ?

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15 4. Is there a relationship between the National Association of Board s of Pharmacy Licensure Exam (NAPBLEX) learning styles and GPA ? Research Hypothesis 1. H0: There will be no relationship between the students preferred learning styles and their grade point average (GPA) 2. H0: There will be no relationship between the students demographics and their grade point average (GPA) 3. H0: There will be no relationship between the students demograp hics and their NABPLEX scores. Significance of the S tudy Faculty of t he UF College of Pharmacy has observed that certain students who are initially hesitant towards the online section of the hybrid program end up favoring it after experiencing its flexibil ity. Faculty members have also observed that some students evaluated in front of a board of professors have difficulty overcoming their fear of social interaction. Subsequent to the logic of long life learning described by Coffield et al.(2004) that stu dents will be more motivated to learn by knowing more about their own strengths and weaknesses, this study will determine the relationship between the preferred learning style of the students enrolled in the program and their success in the program as meas ured by grade point average (GPA) and success on the NAPLEX. Limitations The study is limited to students registered at a Southeastern University College of Pharmacy professional Pharm D program whose classes began in fall 2003 and fall 2004 and graduate d in Spring 2007 and Spring 2008 from the Pharm D program and have completed the VARK questionnaire.

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16 Conceptual Framework The framework for this study is based on Online Education and Learning Styles. Although these topics will be discussed in more detai l in chapter two, a brief introduction is included in this section Distance Education and Learning Styles Distance e ducation has evolved from the posting syllabi online to a sophisticated world of course management systems and online applications. Distan ce learning and corporate universities are growing at unprecedented rates. Enrollment in distance learning is growing at three times the pace of classroom based programs (Christensen, Aaron, & Clark, 2001). Although there are several different ways to def ine learning styles, this study will utilize the Learning Styles associated with the VARK questionnaire ( http://www.vark learn.com/english/index.asp ). Based on the VARK there are 4 different styles. A summary follows; V refers to visual learners. Visual learners prefer the depiction of information in maps, diagrams, charts, graphs, flow charts, labeled diagrams, symbolic arrows, circles, hierar chies and other devices that instructors use to represent what could have been presented in words. A refers to auditory or aural learners. These students prefer to learn when information is "heard or spoken." R refers to students who prefer to read the written words K refers to kinesthetic learners whose p erceptual preference is related to the use of experience and practice (simulated or real). Although such an experience may invoke other modalities, the key is that people who prefer this mode can touch or feel what they are learning,

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17 MM refers to learners who are multimodal and can learn through many modes. Definition of Terms D ISTANCE E DUCATION : A system and process that connects learners with distributed learning resources characterized by t he following: 1) separation of place and/or time between instructor and learner, among learners, and/or between learners and learning resources, and 2) interaction between the learner and the instructor, among learners, and/or between learners and learning resources conducted through one or more media; use of electronic media is not necessarily required. (Accessed January 31, 2008 at http://www.ribghe.org/distance%20learning.pdf ) L EARNING S TYLES : The way that each learner begins to concentrate on, process, absorb, and retain new and difficult information (Dunn and Dunn, 1992; 1993; 1999 accessed at: http://www.learningstyles.net/index.php?option=com_content&task=view&id=20&It emid=70&lang=en on February 4, 2008 ). C OURSE M ANAGEMENT S YSTEMS : computer software that facilitates Web based distance education (Schlosser & Simonson, 2002). VARK: The acronym VARK stands for Visual, Aural, Read/write, and Kinesthetic sensory modalities that are used for learning information. Fleming and Mills (1992) suggested four categories that seemed to reflect the experiences of the students and teachers. ( http://www.vark learn.com/english/page.asp?p=categories ) E MODERATORS : The new generation of teachers and trainers who work with learners online. A SYNCHRONOUS L EARNING N ETWORKS (ALN): A f orm of distance learning in which the teacher and student are separated in both time and space. An ALN uses computer networking technology for teaching and learning activities (Schlosser & Simonson, 2002). V ALIDITY : According the APA Standards (1985), the appropriateness, meaningfulness, and usefulness of specific inferences made (Rothenberg & Hessiling, 1990). NABP: National Association of Boards of Pharmacy NAPLEX: North American Pharmacist Licensure Examination SPSS: Statistical Package for the Social Sciences ANOVA: Analysis of Variance Statistical technique for determining the degree of difference or similarity between two or more groups of data It is based on the comparison of the average value of a common component (accessed at

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18 http: //www.businessdictionary.com/definition/analysis of variances ANOVA.html on July 9, 2009) T T EST : A statistical test involving means of normal populations with unknown standard deviations

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19 CHAPTER 2 REVIEW OF LITERATURE To fulfill the purpose of this study t his chapter contains a review of the research relevant to the origins of professional programs within distance education, as well as the different learning styles and their pedagogical relationship to online learning. Origins of Online Profe ssional Education Programs Distance e ducation is not a new concept. However, what began in the 1800s as a special form of education using nontraditional forms of delivery has evolved into a complex form of education. Advancements in technology as well as wide spread availability have triggered an increase in demand for distance learning. Through advancements like the Internet, and faster more affordable telecommunications channels distance education has been transformed into online education. Academic institutions are devoting a large amount of resources to make this transition. Online education is the fastest growing form of domestic and international education. Web based and web enhance d courses are appearing in traditional programs that are now ra where budget cuts in education funds are an everyday occurrence and physical space limitations a reality, colleges and universities look at online learning as the soluti on to overcome those barriers. Jonnassen (2004) reported an annual growth in Distance learning programs enrollment of 41 percent. By 2006 Moloney & Oakley reported a 20% annual growth in enrollment. Thirty percent of the programs are being developed to m eet the needs of adults who seek professional continuing education (Jonnassen, 2004). Today there are many institutions that run two different sections of the same

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20 program face to face and distance to provide education for on and off campus students Qua lity of Online Professional Education Programs The Sloan as well as the cost benefit ratio as key components for this increase in demand. According to the Sloan C report the enrollment growth is no ticeable at all levels of higher education and more than 59% of academic leaders report online as part of their long term strategy. Table 2 1 Five years of growth in Online Learning Online Education is Critical to the Long term Strategy of M y Institution Fall 2002 to Fall 2006 2002 2003 2004 2005 2006 Agree 48.80% 53.50% 56.00% 58.40% 59.10% Neutral 38.10% 33.70% 30.90% 27.40% 27.40% Disagree 13.10% 12.90% 13.10% 14.20% 13.50% (from Online Nation: Five years of growth in Online Learning) A s the popularity of the online education grows, concern for its quality increases. A ccountability for online professional education programs grows as institutions continue to compete for the vast online student populations. In 2004 the White House representatives announced that it would establish an e learning clearinghouse making it obvious that the lack of research in this area has political connotations ( Allen & Seaman, 2005). Since then, there have been many attempts to introduce policie s and standards to guarantee the quality of online programs. In many cases it is up to the institution to regulate and police the quality of its online programs. According to Amason (2007) there is either an absence of policies to regulate online educat ion from the state, consortia and institutional level or a lack of funding for implementation when the policy exists. This represents a problem at any institution but especially at large institutions where quality control may be decentralized. Although the use of curriculum

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21 committees is widely used in the academic world, there seems to be a large gap between face to face course approval and the approval of its equivalent virtual version. Further, a pproval for virtual courses does not seem to require th e review and endorsement of a curriculum committee. Another significant factor affecting the quality of online education is the faculty workload and its relationship to their wiliness to engage in new technology. Amason (2007) found no documentation asso ciated to faculty rewards or release time to learn about new technologies at the state and consortium level. There is some evidence of faculty rewards at the institution level, but in many cases it is up to the faculty member to acquire the requisite skil ls needed to effectively adapt his/her course to an online environment. Many times the sacrifice is not worth the reward, according to Howell, Williams, and Lindsay (2003) who reported distance education contributions are not considered in ten ure and promotion decisions However, despite the adversities, the reputation of the quality of the online education programs is on the rise. According to the Sloan Making the G published in 2006, most Chief Academic Officers believe that the quality of online instruction is equal to or superior to that of face to face learning. In 2003, 57 percent of academic leaders rated the learning outcomes in online educat ion as the same or superior to those in face to face. That number is now 62 percent, a small but noteworthy increase. According to Allen and Seaman (2006), the proportion of educators who believe that online learning outcomes are superior to those for fac e to face is still relatively small but has grown by 4 .8 percent since 2003 from 12.1 percent in 2003 to 16.9 in 2005

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22 College of Pharmacy PharmD Program In order to provide educational opportunities for geographically bound students and to increase the number of pharmacist in the state a Southeastern University College of Pharmacy created the Online PharmD program in 2002 This Southeastern University College of Pharmacy has distance education sites located in Jacksonville, Orlando, and St. Petersb urg since the academic year 2002 2003. These campuses accommodate 50 60 students at each site. S tudents receive instruction in pharmacy via distance education technology. The Doctor of Pharmacy curriculum offered by faculty on the Gainesville campus is re produced in an asynchronous format for students at the distance education campuses. Faculty at the distance education sites facilitate discussion groups, administer examinations, answer questions, and provide instruction as needed to supplement the distanc e education program. A support staff in student http://www.cop.ufl.edu/studaff/disted.htm on February 2, 2008 ). Students attending the College of Pharmacy professional program need to meet the same admission requirements as the other students in the college. According to the web site the requirements are as follows; Admission Requirements 1. Associate of Arts (AA) or higher degree (must complete gene ral education requirements) 2. Completion of pre professional course requirements, minimum pre professional GPA of 2.5 3. Pharmacy College Admission Test (PCAT), minimum 50th percentile composite score 4. Foreign language required in high school (2 yrs), or college (8 10 cr), or TOEFL IBT score

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23 5. Test of English as a Foreign Language (TOEFL) if English is your second language a. TOEFL ( http://www.ets.org/toefl/ ) IBT version with a minimum composite score of 80, but 100 or higher is preferred. For each subject area, the following minimum score is required: Reading = 20+, Listening = 20+, Speaking = 25+, Writing = 23+ Submission of ALL application materials by the application deadline including:* 1. UF online application for admission Select Entry Level Professional Phar mD 2. PharmCAS application Submit official transcripts and PCAT scores 3. Two letters of recommendation (available through PharmCAS website) 4. Personal Profile & Essay (submit online through UF College of Pharmacy website) 5. Campus Ranking form (submit online thro ugh UF College of Pharmacy website) *Only completed application files will be reviewed. Detailed application procedures are available on the website. Meeting all the requirements only guarantees that the application will be reviewed but admission into th e program is very competitive. For example, in fall 2007, only 300 students out of 2000 applicants were admitted into the program. The Admission Committee based its decisions o n the following factors : 1. Pre Professional Science/Math GPA, although the minimum GPA to apply is 2.5, in fall 2007 the average GPA for admitted students was 3.53 2. PCAT Pharmacy College Admissions Test, although the minimum score is 50% to be considered, in fall 2007 the averaged admitted student score was 82%. In addition to the requirements mentioned in their website, a high demand also contributed to the development of the online program to accommodate the non traditional students. Learning Styles Individuals have perceptual and processing strengths and weaknesses. In the same way that students as individuals differ from each other on other dimension s, such as personality they also have their own unique strengths and preferences. From these

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24 sets of preferences, a favored learning style develops. Some students prefer to work with concrete information (facts, experimental data) while others are more comfortable with abstractions (theories, symbolic information, mathematical model). Some are partial to visual presentation of information; pictures, diagrams, flowcharts, schemati cs, and others get more from verbal explanations. Some like to learn by trying things out and se eing and analyzing what happens while others would rather reflect on things they plan to do and understand as much as they can about them before actually attem pting them (Felder & Spurlin, 2005). Learning strategies are defined as, behaviors, beliefs or emotions that facilitate the acquisition, understanding or later (Weinstein, Husman, & Dierking, 2000, p. 72 7). There are different conceptual models that govern learning styles and evaluating the different models requires an understanding of their complexity. Most researchers agree with the general assumption that no one style applies to all learners. Student s hold a preference or tendency to feel more comfortable with one of the styles. Learning style models are usually divided in to four categories. Although different models do n o t follow the same naming convention when it comes to categories, they are usua lly comparable when it comes to the description. For example, according to Felder and Silverman there are four different groups of learners: are those are those who are those who are the linear thinkers. These four styles have parallel categories in other models F or example the VARK system h as four equivalent categories: v isual, aural, read/write and kinesthetic. Out of the multiple models, the

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25 College of Pharmacy at a Southeastern University selected the VARK from others, due to its availability to the public. In addition to the VARK Learning sys tem the Learning Style Inventory (LSI), Gregorc Learning Style Model, the Learning and Study Strategies Inventory ( LASSI ), and the Learning Type Measure ( LTM ) will be described in this chapter VARK VARK is a questionnaire that provides users with a pro file of their learning preferences. These preferences are about the ways that they want to take in and give out information. Accessed on February 4, 2008 from ( http://www.vark learn.com/english /page.asp?p=faq ) The VARK c ategories The acronym VARK stands for Visual, Aural, Read/write, and Kinesthetic sensory modalities that are used for learning information. Fleming and Mills (1992) suggested four categories that seemed to reflect the experienc es of the students and teachers. Although there is some overlap between categories, they are defined as follows ; Visual (V): P reference for visual media includes the depiction of information in maps, spider diagrams, charts, graphs, flow charts, labeled d iagrams, and symbolic arrows, circles, hierarchies and other devices, which instructors use to represent what could have been presented in words. It could have been called Graphic (G) as that better explains what it covers. It does NOT include movies, vide os or PowerPoint. It does include designs, whitespace, patterns, shapes and different illustrative formats that are used to highlight and convey information. ( http://www.vark learn.com/e nglish/page.asp?p=categories )

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26 Aural / a uditory (A) This perceptual mode describes a preference for information that is "heard or spoken." Students with this modality report that they learn best from lectures, tutorials, tapes, group discussion s email, us ing mobile phones, speaking, web chat and talking things through. It includes talking out loud as well as talking to themselves Often people with this preference want to sort things out by speaking, rather than sorting things out and then speak. ( http://www.vark learn.com/english/page.asp?p=categories ) Read/write (R) This preference is for information that is displayed as words. Not surprisingly, many academics have a strong preference for this modality. This preference emphasizes text based input and output reading and writing in all its forms. People who prefer this modality are often addicted to PowerPoint, the Internet, lists, filofaxes, dictionaries, thesauri, quotations and word s ( http://www.vark learn.com/english/page.asp?p=categories ) Kinesthetic (K) By definition, this modality refers to the "perceptual preference related to the use of experience and practice (simulated or real)." Although such an experience may invoke other modalities, the key is that people who prefer this mode are connected to reality, "either through concrete personal experiences, examples, practice or simulation" [See Fleming & Mi lls, 1992, pp. 140 141]. It includes demonstrations, simulations, videos and movies of "real" things, as well as case studies, practice and applications. What about m ixtures? m ultimodals (MM) Life is multimodal. There are seldom instances where one mode is used, or is sufficient, so we have a four part VARK profile. That is why the VARK questionnaire

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27 gives you four scores. Those who prefer many modes almost equally are of two types. There are those who are context specific who choose a single mode to suit t he occasion or situation. There are others who are not satisfied until they have had input (or output) in all of their preferred modes. They take longer to gather information from each mode and, as a result, they often have a deeper and broader understandi ng. ( http://www.vark learn.com/english/page.asp?p=categories )(VARK questionnaire appendix A) The scores are represented in a 2 axes Fig ure 2 1. VARK Learning Model (from Hawk and Shah) Learning Styles Inventory (LSI) The LSI method is another method used to determine the preferred learning style of College of Pharmacy students. The LSI was developed by Kolb and is based on theoretical work by Piaget and Guilford related to intelligence, thinking, and creativity. solving model, although it emphasizes how learners absorb and deal with new informati on ( Adamcik, Hurley,& Erramousque,1996). As the VARK system, the LSI di vides the learners in four different groups representing preferred learning styles, feelings (CE), watching and listening (RO), thinking (AC) and doing (AE). The preferences are score d and mapped in two axes (see fig.2)

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28 Fig ure 2 2 Kolb LSI scoring Gregorc Learning Style Model According to Hawk and Shah (2007), the Gregorc Learning/Teaching Style Model is based upon style. These qualities are : abstract and concrete perception, sequential and random ordering, deductive and inductive processing and separative and associative relati onships. A combination of the first two, perception and ordering, placed on the Gregorc Style Delineator produce four learning Styles Concrete Sequential (CS), Abstract Sequential (AS), Abstract Random (AR), and Concrete Random (CR) See fig. 2 3

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29 Fig ure 2 3 Gregorc Style Delineator (from Hawk and Shah) According Hawk and Shah (2007) the CS learner is described as a learner who prefers direct, hands on experience, wants order and a logical sequence to tasks, and follows directions well. The AS learner is one who likes working with ideas and symbols, is logical and sequential in thinking, and likes to focus on the task without distractions. The AR learner focuses attention on the people and the surroundings, prefers discussions and conversations that are wi de ranging, and wants time to reflect on experiences. Finally, the CR learner is experimental and a risk taker, likes to explore unstructured problems, makes intuitive leaps in solving them, and uses trial and error to work out solutions. Coffield, Mosole y,Hall, Eccleston (2004), after reviewing the reliability and validity of the instrument suggested that the Gregorc Learning/Teaching Style Model should not be used in education. Learning and Study Strategies Inventory (LASSI) Developed at the University o f Texas at Austin by Claire E. Weinstein, Ann C. Schulte, and David R. Palmer, according to Cano (2006), the Learning and Study Strategies Inventory (LASSI) is a self report instrument to assess learning styles which is based on a general model of learning and cognition and on a model of strategic

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30 use of learning and study strategies related to skill, will and self regulation components of strategic learning. The skill c omponent of LASSI measures information processing, selecting ideas, and testing strategies through processes related to identifying, acquiring and constructing meaning for new information and how one prepares for and demonstrates new knowledge on tests. Th e will component of LASSI measures necessary to successfully complete academic requirements. The Self regulation component measures how the students manage the whole l earning process through using their time effectively, focusing their attention and maintaining their concentration. ( http://www.hhpublishing.com/_assessments/LASSI/index.html ). LASSI Scores 01 05 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 ANX ------------------------> ATT --------------------------------> CON ------------> INP ------------------------------------> MOT ------------------------> SFT ----------------------------------------------------> SMI --------------------------------------------> STA --------------------------------> TMT ----------------> TST --------> 01 05 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 Figure.2 4. LASSI Scores The LASSI has been used to assess abilities directly related to academic performance of Pharmacy students, Lobb, Wilkin, McCaffrey, Wilson, and Bentley (2006) repo rted that the results of the LASSI, Defining Issues Test (DIT) and Watson

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31 related to academic performance in students, but it may be useful in assessing other attributes that are highly desirable for the practice of pharmacy. Melancon (2002), reported reliability and validity scores within .03 of the alpha coefficient as reported in desc ribed in the manual. These findings were in agreement with previous research results reported by Deming, Valeri Gold, and Idleman (1994). Benefits of L earning S tyles Research to S tudents and I nstructors As the student body varies in diversity, so do the s tudents learn ing styles. All the research and methods described in this study are looking for different approaches to successfully develop or modify curricula to accommodate these learning styles and maximize the learning experience. Not only can the cu rriculum accommodate learning styles but, according to Hein & Bundy (1999), any learning style can be adapted for use with any population. Performance Learning System reports a series of benefits associated with the rese arch and study of learning styles. Tailor your lesson plans to be more lively and interactive. Create an interesting and motivating classroom. Reach peak performance by matching your teaching and learning styles to those of your students. Teach students about their learning styles. Dev elop effective classroom management skills. Motivate students according to their interests. However, Coffield, Mosoley,Hall, Eccleston (2004), warned us about the dangers in commending detailed strategies to practitioners because the theories and instru ments are not equally useful and no consensus about the recommendations.

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32 During the 2003 04 academic year, approximately two million learners were engaged in higher education via Asynchronous Learning Networks (ALN), and online enrollments are expected to grow at a 20% annual rate during the next few years (Moloney & Oakley, 20 06). Despite this increase in popularity, there are still questions related to the adequacy of online education for different students. In 2003 the College of Pharmacy at the Southeastern University created their own online PharmD program It is obviou understanding and knowledge of the material. Therefore, if students are asked to give their interpretation of a page after they are finished reading it, a wide range of understanding wi ll be their answers. This wide range exists because people absorb information in different ways. In 2003, Pungente, Wasan, and Moffett used learning styles to evaluate first year based learning approach. They found a relationship between the different learning styl es and the preferences for activities associated with problem based learning approach. However, their conclusion calls for future efforts to link the individual learning and their academic success.

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33 CHAPTER 3 METHODOLOGY This chapter will describe the participants, operational definition of variables, instrumentation, data collection, and data analysis. The ultimate goal of the study is to improve the curriculum to better accommodate students with different learning styles. Participan ts The participants in the study were selected from 550 professional students whose classes began in Fall 2002, Fall 2003, Fall 2004 and graduated in Spring 2006, 2007, 2008 from the Pharm D program at a Southeastern University and have completed the VARK q uestionnaire. Out of the 550 graduate students a sample size of 121 students were selected based on data availability. They filled out the instruments as part of their "computer competency" assessment during the summer before they entered into the colleg e as part of their entrance requirements. The computer competency assessment prepares students for some of the tasks the y will perform with the computer during the program. Study Design The study is a survey study using a post hoc dataset The post hoc dataset has been designed as a non experimental survey in such way that the researcher will look at the data provided by the surveys and analyze it using the methods described in this chapter to look for relationships between the independent and dependent variables. (see fig.4)

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34 Fig ure 3 1 Factors impacting performance in online education Operational Definition of Variables Instrumentation As mentioned in the literature review there are many instruments to measure and define The Pharm D program at a Southeastern University select ed the VARK questionnaire to collect data about student s learning styles. This 16 question instrument provides information about the ways that students take in and give out information, and creates a profile of their learning preferences. The VARK questionnaire is a sensory model created as an extension of the earlier neuro linguistic model by Neil D. Fleming and Charles C. Bonwell. Neil D. Fleming has over forty years of experience in education, while Charles C. Bonwell directed for over five years Centers for Teaching and Learning at Saint Louis College of Pharmacy and is an instructional consultant and author among other professional acti vities.

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35 The acronym VARK stands for Visual, Aural, Read/write, and Kinesthetic sensory modalities that are used for learning information. Fleming and Mills (1992) suggested four categories that seemed to reflect the experiences of the students and teacher s. The VARK questionnaire was administered to students online with specific instructions following the guidelines published by their creators. Fleming and Bonwell suggest advis ing students to make a selection (a, b, c or d) for each question, but allow t hem to omit a question or choose more than one option if they want to. In order to avoid being bias ed to any particular answer, additional information about specific questions was avoided. Students were encouraged to choose more than one response if they t hink the context is not clear. Validity and Reliability From his own database, Fleming (2007) reports that with a population of over 44 000 39.1% prefer a single learning style, and 59.6% have multimodal learning style s To answer the question about val idity Fleming (2007) reports that the strength of VARK is the fact that his questions and options are drawn from real life and that 59.4% of the participants said that the results match their own perceptions. However, Dr. Marilla Svinicki at the Univers ity of Texas at Austin Dr. Leite and Dr. Shi at the University of Florida conducted a statistical research to report the statistical validity of VARK. According to Svinicki, Leite and Shi (2008) the popularity of the VARK comes from its face validity, si mplicity and practicality. But the authors also indicated that the VARK has not yet received a comprehensive validation, and it should be used with caution when using it as a predictor.

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36 Data Collection The data was collected from two sets of students wh o attended the 4 year in length program The first set of data comes from students who were enrolled in 2003 and graduated in 2007 The second set comes from students who started the program in 2004 and graduated in 2008. The data was collected in the fo rm of survey s administered to the students as part of their "computer competency" assessment during the summer before they entered into the college as part of their entrance requirements. Data Analysis Initially, I will perform descriptive statistics and utilize a frequency distribution and graph to identify outliers and/or impossible or implausible values, to summarize the data, and to check for distributional forms. Scatter plots of bivariate data will be examined for possible non linear relationships. Data will be transformed if needed to achieve normality. I will then perform the statistical analysis appropriate for addressing each specific aim. I will use a two sided alpha=0.05 as the level of significance. For all data management and statistical analyses we will use SAS version 9.1 (SAS Institute, Cary, NC). Specific Aim 1 A two step approach will be used for modeling outcome variables of interest (i.e., internal measures of performance such as time to complet ion and grade point average learning style, and instructional delivery method First, I will conduct bivariate analyses (i.e., analyses of outcome and a single explanatory variable) and then conduct a multiple regression for each outcome. Bivariate and regression analyses yield informative yet different results. Bivariate analyses measure relationships between each

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37 explanatory variable and outcome; multiple regression analy ses show significant relationships for explanatory variables after adjustment for other variables in the model. Specific Aim 2 A two step approach will be used for modeling outcome variables of interest (i.e., external measures of performance such as board exam result, and employment) as a style, instructional delivery method, and internal performance. First, I will conduct bivariate analyses (i.e., analyses of outcome and a single explanatory variable) and then conduct a multiple logistic regression for each outcome. Bivariate and regression analyses yield informative yet different results. Bivariate analyses measure relationships between each explanatory variable and outco me; multiple logistic regression analyses show significant relationships for explanatory variables after adjustment for other variables in the model.

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38 CHAPTER 4 FINDINGS Two different analyses were used to analyze the dat a ANOVA and the T test. The T test was selected for equality of means. It is a 2 tailed test of significance. By 2 tailed, it is testing if the mean of one group is higher or lower. It is testing in both directions as opposed to only one direction (one tailed). Frequencies for the V ariables of I nterest The frequencies for variables of interest were evaluated for all the available data: gender, race, age, NABP, GPA and VARK groups (visual, aural, read, kinesthetic and multimodal ), The analysis of the population shows that our sampl e is predominantly female (85%), white (90%) between the ages 21 25 (79%). Table 4 1. Frequencies Gender Frequency Percent Valid Percent Cumulative Percent Valid Male 36 29.8 29.8 29.8 Female 85 70.2 70.2 100.0 Total 121 100.0 100.0 Table 4 2 Race Frequency Percent Valid Percent Cumulative Percent Valid Non White 31 25.6 25.6 25.6 White 90 74.4 74.4 100.0 Total 121 100.0 100.0

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39 Table 4 3 Age Frequency Percent Valid Percent Cumulative Percent Valid 20 1 .8 .8 .8 21 11 9.1 9.1 9.9 22 30 24.8 24.8 34.7 23 25 20.7 20.7 55.4 24 19 15.7 15.7 71.1 25 11 9.1 9.1 80.2 26 3 2.5 2.5 82.6 27 3 2.5 2.5 85.1 28 3 2.5 2.5 87.6 29 3 2.5 2.5 90.1 30 1 .8 .8 90.9 31 1 .8 .8 91.7 33 2 1.7 1.7 93.4 34 2 1.7 1.7 95.0 35 1 .8 .8 95.9 36 1 .8 .8 96.7 37 1 .8 .8 97.5 38 1 .8 .8 98.3 44 1 .8 .8 99.2 48 1 .8 .8 100.0 Total 121 100.0 100.0

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40 Table 4 4 GPA Frequency Percent Valid Percent Cumulative Percent Valid 2.80 1 .8 .8 .8 2.90 1 .8 .8 1.7 2.91 4 3.3 3.3 5.0 2.97 1 .8 .8 5.8 2.98 1 .8 .8 6.6 2.99 3 2.5 2.5 9.1 3.00 1 .8 .8 9.9 3.01 1 .8 .8 10.7 3.02 2 1.7 1.7 12.4 3.06 1 .8 .8 13.2 3.09 2 1.7 1.7 14.9 3.10 1 .8 .8 15.7 3.12 3 2.5 2.5 18.2 3.14 2 1.7 1.7 19.8 3.15 1 .8 .8 20.7 3.17 1 .8 .8 21.5 3.18 3 2.5 2.5 24.0 3.20 3 2.5 2.5 26.4 3.25 2 1.7 1.7 28.1 3.26 2 1.7 1.7 29.8 3.27 1 .8 .8 30.6 3.28 2 1.7 1.7 32.2 3.30 2 1.7 1.7 33.9 3.33 1 .8 .8 34.7 3.34 1 .8 .8 35.5 3.36 3 2.5 2.5 38.0 3.39 1 .8 .8 38.8 3.42 1 .8 .8 39.7 3.43 1 .8 .8 40.5 3.45 2 1.7 1.7 42.1 3.46 2 1.7 1.7 43.8 3.49 4 3.3 3.3 47.1 3.52 1 .8 .8 47.9 3.53 1 .8 .8 48.8 3.54 1 .8 .8 49.6

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41 Table 4 4. continued Frequency Percent Valid Percent Cumulative Percent 3.55 3 2.5 2.5 52.1 3.56 1 .8 .8 52.9 3.57 1 .8 .8 53.7 3.58 1 .8 .8 54.5 3.60 2 1.7 1.7 56.2 3.61 3 2.5 2.5 58.7 3.62 4 3.3 3.3 62.0 3.63 4 3.3 3.3 65.3 3.64 3 2.5 2.5 67.8 3.65 2 1.7 1.7 69.4 3.66 1 .8 .8 70.2 3.67 1 .8 .8 71.1 3.69 1 .8 .8 71.9 3.70 1 .8 .8 72.7 3.71 4 3.3 3.3 76.0 3.73 1 .8 .8 76.9 3.76 1 .8 .8 77.7 3.77 1 .8 .8 78.5 3.78 3 2.5 2.5 81.0 3.82 2 1.7 1.7 82.6 3.83 2 1.7 1.7 84.3 3.86 2 1.7 1.7 86.0 3.87 1 .8 .8 86.8 3.88 2 1.7 1.7 88.4 3.89 1 .8 .8 89.3 3.90 1 .8 .8 90.1 3.91 2 1.7 1.7 91.7 3.92 1 .8 .8 92.6 3.93 2 1.7 1.7 94.2 4.00 7 5.8 5.8 100.0 Total 121 100.0 100.0

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42 Table 4 5 Table 4 5. NABP Score Frequency Percent Valid Percent Cumulative Percent Valid 71 1 .8 .8 .8 80 1 .8 .8 1.7 81 1 .8 .8 2.5 91 2 1.7 1.7 4.1 93 2 1.7 1.7 5.8 94 2 1.7 1.7 7.4 95 1 .8 .8 8.3 96 2 1.7 1.7 9.9 97 1 .8 .8 10.7 98 1 .8 .8 11.6 101 1 .8 .8 12.4 103 1 .8 .8 13.2 106 2 1.7 1.7 14.9 107 4 3.3 3.3 18.2 108 2 1.7 1.7 19.8 109 1 .8 .8 20.7 110 2 1.7 1.7 22.3 111 2 1.7 1.7 24.0 112 1 .8 .8 24.8 113 2 1.7 1.7 26.4 115 2 1.7 1.7 28.1 116 5 4.1 4.1 32.2 117 1 .8 .8 33.1 118 4 3.3 3.3 36.4 119 2 1.7 1.7 38.0 120 2 1.7 1.7 39.7 121 1 .8 .8 40.5 122 4 3.3 3.3 43.8 123 5 4.1 4.1 47.9 124 2 1.7 1.7 49.6 125 1 .8 .8 50.4 126 4 3.3 3.3 53.7 127 4 3.3 3.3 57.0 128 3 2.5 2.5 59.5 129 2 1.7 1.7 61.2

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43 Table 4 5. continued Frequency Percent Valid Percent Cumulative Percent 130 4 3.3 3.3 64.5 131 6 5.0 5.0 69.4 132 6 5.0 5.0 74.4 133 3 2.5 2.5 76.9 134 4 3.3 3.3 80.2 135 3 2.5 2.5 82.6 136 9 7.4 7.4 90.1 137 5 4.1 4.1 94.2 138 2 1.7 1.7 95.9 141 4 3.3 3.3 99.2 142 1 .8 .8 100.0 Total 121 100.0 100.0 Table 4 6 Visual or Not Visual Frequency Percent Valid Percent Cumulative Percent Valid Not Visual 38 31.4 31.4 31.4 Visual 83 68.6 68.6 100.0 Total 121 100.0 100.0 Table 4 7 Aural or Not Aural Frequency Percent Valid Percent Cumulative Percent Valid Not Aural 54 44.6 44.6 44.6 Aural 67 55.4 55.4 100.0 Total 121 100.0 100.0 Table 4 8 Read or Not Read Frequency Percent Valid Percent Cumulative Percent Valid Not Read/Write 44 36.4 36.4 36.4 Read/Write 77 63.6 63.6 100.0 Total 121 100.0 100.0

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44 Table 4 9 Kinesthetic or Not Kinesthetic Frequency Percent Valid Percent Cumulative Percent Valid Not Kinesthetic 100 82.6 82.6 82.6 Kinesthetic 21 17.4 17.4 100.0 Total 121 100.0 100.0 Table 4 1 0 MultiModal or Not MultiModal Frequency Percent Valid Percent Cumulative Percent Valid Not Multimodal 57 47.1 47.1 47.1 Multimodal 64 52.9 52.9 100.0 Total 121 100.0 100.0 H0: point average. H0: There exam results. 1. Correlations Correlations are used to find if there is a relationship between student demographics and GPA & NABP exam results The relationship between age and GPA is positively correlated. As age increases so does GPA. It is not strong at .172 but is marginally statistically significant, p = .059 There is a negative correlation (relationship) between age and NABP score. Age and NABP go in opposite directions, as age increases NABP scores decrease It is a weak correlation at .054, and is not statistically significant. Ideally, a score of .7 and higher is desired

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45 Table 4 11 Correlations age GPA age Pearson Correlation 1 .172 Sig. (2 tailed) .059 N 121 121 GPA Pearson Correlation .172 1 Sig. (2 tailed) .059 N 121 121 Table 4 12 Correlations age NABP Score Age Pearson Correlation 1 .054 Sig. (2 tailed) .560 N 121 121 NABP Score Pearson Correlation .054 1 Sig. (2 tailed) .560 N 121 121 2 Oneway Analysis of Variance (ANOVA) and T Test Female and Male comparison on NABP and GPA The descriptive breaks down the NABP & GPA by gender, NABP scores for males versus females and GPA by males versus females. In our sample, f emale students had higher NABP scores and slightly higher GPA (higher by .0058) than their counterpart male students. The test of Homogeneity of Variance analyzes the equality of variances in the 2 groups regarding NABP scores and GPAs are equal, a requirement of the ANOVA. They are not significantly different.

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46 Table 4 12 Descriptives N Mean Std. Deviation Std. Error NABP Score Male 36 119.50 15.801 2.634 Female 85 122.05 14.607 1.584 Total 121 121.29 14.951 1.359 GPA Male 36 3.4783 .33674 .05612 Female 85 3.4841 .31664 .03434 Total 121 3.4824 .32135 .02921 Table 4 13 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Male 114.15 124.85 71 137 Female 118.90 125.20 80 142 Total 118.60 123.98 71 142 GPA Male 3.3644 3.5923 2.80 4.00 Female 3.4158 3.5524 2.91 4.00 Total 3.4246 3.5402 2.80 4.00 Table 4 14 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score .321 1 119 .572 GPA .470 1 119 .494 The following table shows the results for two ANOVA test: A first test of the null hypothesis that there is not a relationship between gender and NABP and a second ANOVA test of the null hypothesis that there is no relationship between gender and GPA.

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47 There was not a statistically significant difference in the mean NABP scores between females and males. Therefore, the null hypothesis HO is accepted. G ender doe s not influence NABP scores. There was not a statistically significant difference in the mean GPAs between females and males. T he null hypothesis HO: There is no relationship between gender and GPA is accepted. G ender does not influence GPA. Table 4 15 ANOVA Sum of Squares Df Mean Square F Sig. NABP Score Between Groups 164.064 1 164.064 .732 .394 Within Groups 26660.812 119 224.040 Total 26824.876 120 GPA Between Groups .001 1 .001 .008 .928 Within Groups 12.391 119 .104 Total 12.392 120 White and Non White C omparison on NABP and GPA Oneway Analysis of Variance (ANOVA) The descriptive statistics present the NABP & GPA by race, NABP scores for whites versus non whites and GPA by whites versus non whites. The descriptive data shows that Whites (120.84) had a lower mean NABP than non whites (122.58) and Non whites (3.50) had a higher GPA than whites (3.48) The Test of Homogeneity of Variances tests for significant difference in the variances in scores between the two groups There was not a statistically significant difference in the mean NABP scores between whites and non whites. Therefore, the null hypothesis is accepted race does not influence NABP scores.

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48 Table 4 16 Descriptives N Mean Std. De viation Std. Error NABP Score Non White 31 122.58 16.413 2.948 White 90 120.84 14.485 1.527 Total 121 121.29 14.951 1.359 GPA Non White 31 3.5013 .33910 .06090 White 90 3.4759 .31671 .03338 Total 121 3.4824 .32135 .02921 Table 4 17 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Non White 116.56 128.60 80 142 White 117.81 123.88 71 141 Total 118.60 123.98 71 142 GPA Non White 3.3769 3.6257 2.91 4.00 White 3.4096 3.5422 2.80 4.00 Total 3.4246 3.5402 2.80 4.00 Table 4 18 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score .833 1 119 .363 GPA .264 1 119 .608 There was not a statistically significant difference in the mean GPAs between whites and non whites. Thus the null hypothesis there is no relationship between race and GPA, is accepted. R ace does not influence GPA.

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49 Table 4 19 ANOVA Sum of Squares df Mean Square F Sig. NABP Score Between Groups 69.505 1 69.505 .309 .579 Within Groups 26755.371 119 224.835 Total 26824.876 120 GPA Between Groups .015 1 .015 .143 .706 Within Groups 12.377 119 .104 Total 12.392 120 Visual and Non Visual Learning Styles comparison on NABP and GPA Oneway Analysis of Variance (ANOVA) The descriptive stat istic s present the NABP & GPA by preferred learning style visual learning style versus those who do not prefer the visual learning style. This tests the null hypothesis that there is not a relationship between preference or no preference for Visual learning style and NABP. There is not a statistically significant difference in the mean NABP scores between those who preferred a Visual learnin g style and those who did not prefer a Visual learning style. As a result the null hypothesis is accepted preference or not for a Visual learning style does not influence NABP scores. Table 4 20 Descriptives N Mean Std. Deviation Std. Error NABP Score Not Visual 38 122.03 15.136 2.455 Visual 83 120.95 14.946 1.641 Total 121 121.29 14.951 1.359 GPA Not Visual 38 3.4508 .31611 .05128 Visual 83 3.4969 .32458 .03563 Total 121 3.4824 .32135 .02921

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50 Table 4 21 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Not Visual 117.05 127.00 71 141 Visual 117.69 124.22 80 142 Total 118.60 123.98 71 142 GPA Not Visual 3.3469 3.5547 2.90 3.91 Visual 3.4260 3.5677 2.80 4.00 Total 3.4246 3.5402 2.80 4.00 Table 4 22 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score .663 1 119 .417 GPA .117 1 119 .733 This tests the null hypothesis that there is not a relationship between preference or no preference for Visual learning style and GPA. There was not a statistically significant difference in the mean GPA between those who preferred a Visual learning style and those who did not prefer a Visual learning style. T he null hypothesis, preference or not for a Visual learning style does not influence GPA is accepted Table 4 23 ANOVA Sum of Squares df Mean Square F Sig. NABP Score Between Groups 30.095 1 30.095 .134 .715 Within Groups 26794.781 119 225.166 Total 26824.876 120 GPA Between Groups .055 1 .055 .534 .466 Within Groups 12.336 119 .104 Total 12.392 120

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51 Aural and Non Aural Learning Style comparison on NABP and GPA Oneway Analysis of Variance (ANOVA) Students who preferred the Aural learning style had a 0.21 lower mean NABP score than those who did not prefer the Aural learning style. The reverse was true for GPA. Students who preferred the Aural learning style had a 0.09 h igher mean GPA than those who did not prefer the Aural learning style. Mean differences were very small in both areas. Table 4 24 Descriptives N Mean Std. Deviation Std. Error NABP Score Not Aural 54 121.41 15.315 2.084 Aural 67 121.19 14.767 1.804 Total 121 121.29 14.951 1.359 GPA Not Aural 54 3.4328 .32825 .04467 Aural 67 3.5224 .31239 .03816 Total 121 3.4824 .32135 .02921 Table 4 25 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Not Aural 117.23 125.59 71 142 Aural 117.59 124.80 80 141 Total 118.60 123.98 71 142 GPA Not Aural 3.3432 3.5224 2.80 4.00 Aural 3.4462 3.5986 2.91 4.00 Total 3.4246 3.5402 2.80 4.00

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52 Table 4 26 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score .201 1 119 .655 GPA .154 1 119 .696 There was no significant differences were found in NABP or GPA values between students who preferred the Aural learning style versus those who did not prefer the Aural learning style. This tests the null hypothesis that there is not a relationship between preference or no preference for Aural learning style and NABP. There was not a statistically significant difference in the mean NABP scores between those who preferred a n Aural learning style and those who did not prefer a n Aural learning style. Accept the null hypothesis, preference or not for a n Aural learning style does not influence NABP scores. This tests the null hypothesis that there is not a relationship between preferenc e or no preference for Aural learning style and GPA. There was not a statistically significant difference in the mean GPA between those who preferred a n Aural learning style and those who did not prefer a Aural learning style. Accept the null hypothesis, p reference or not for a n Aural learning style does not influence GPA. Table 4 27 ANOVA Sum of Squares df Mean Square F Sig. NABP Score Between Groups 1.361 1 1.361 .006 .938 Within Groups 26823.515 119 225.408 Total 26824.876 120 GPA Between Groups .240 1 .240 2.351 .128 Within Groups 12.152 119 .102 Total 12.392 120

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53 Read/Write and non Read/Write Learning Styles comparison on NABP and GPA Oneway Analysis of Variance (ANOVA) Students who did not prefer the Read/Write learning style had a higher mean NABP score than those who prefer the Read/Write learning style. The reverse was true for GPA. Students who preferred the Read/Write learning style had a 0.08 higher mean GPA than those who did not prefer the Read/Write learning style. Mea n differences were very small in both areas. Table 4 28 Descriptives N Mean Std. Deviation Std. Error NABP Score Not Read/Write 44 124.59 12.646 1.906 Read/Write 77 119.40 15.892 1.811 Total 121 121.29 14.951 1.359 GPA Not Read/Write 44 3.4318 .31161 .04698 Read/Write 77 3.5113 .32524 .03706 Total 121 3.4824 .32135 .02921 Table 4 29 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Not Read/Write 120.75 128.44 91 142 Read/Write 115.80 123.01 71 141 Total 118.60 123.98 71 142 GPA Not Read/Write 3.3371 3.5266 2.80 4.00 Read/Write 3.4375 3.5851 2.90 4.00 Total 3.4246 3.5402 2.80 4.00

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54 Table 4 30 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score 2.701 1 119 .103 GPA .096 1 119 .757 Students who preferred a Read/Write learning style had a lower mean NABP score than students who did not prefer a Read/Write learning style. However, the difference between mean values was not statistically significant. The difference between mean NABP va lues was marginally statistically significant at p = .066 level. T he null hypothesis HO: there is no relationship between preferring the Read/Write learning style and not preferring the Read/Write learning style is accepted Students who preferred a Read/ Write learning style had a higher mean GPA than students who did not prefer a Read/Write learning style. However, the difference between mean values was not statistically significant. The null hypothesis is accepted There is no relationship between studen ts who preferred a Red/Write learning style and their GPA. Table 4 31 ANOVA Sum of Squares df Mean Square F Sig. NABP Score Between Groups 753.720 1 753.720 3.440 .066 Within Groups 26071.156 119 219.085 Total 26824.876 120 GPA Between Groups .177 1 .177 1.723 .192 Within Groups 12.215 119 .103 Total 12.392 120

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55 Kinesthetic and Non Kinesthetic Learning Style comparison on NABP and GPA Oneway Analysis of Variance (ANOVA) Students who did not prefer the Kinesthetic learning style had a higher mean NABP score than those who prefer the Read/Write learning style. The reverse was true for GPA. Students who preferred the Read/Write learning style had a 0.11 higher mean GPA than those who did not prefer the Read/Write learning style. Table 4 32 Descriptives N Mean Std. Deviation Std. Error NABP Score Not Kinesthetic 100 122.88 14.042 1.404 Kinesthetic 21 113.71 17.097 3.731 Total 121 121.29 14.951 1.359 GPA Not Kinesthetic 100 3.4631 .32569 .03257 Kinesthetic 21 3.5743 .28947 .06317 Total 121 3.4824 .32135 .02921 Table 4 33 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Not Kinesthetic 120.09 125.67 71 142 Kinesthetic 105.93 121.50 80 135 Total 118.60 123.98 71 142 GPA Not Kinesthetic 3.3985 3.5277 2.80 4.00 Kinesthetic 3.4425 3.7061 3.10 4.00 Total 3.4246 3.5402 2.80 4.00 Table 4 34 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score 1.948 1 119 .165 GPA 1.996 1 119 .160

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56 Students who did not prefer a Kinesthetic learning style had a higher mean NABP score than students who preferred a Kinesthetic learning style. The difference between mean NABP values was statistically significant at p = .01 level. Therefore reject the null hypothesis that there is no difference in NABP scores between students who prefer a Kinesthetic learning style and those who do not prefer a Kinesthetic learning style. Students who pref er a Kinesthetic learning style had a higher mean GPA than students who did not prefer a Kinesthetic learning style. However, the difference between mean values was not statistically significant. Accept the null hypothesis that there is no difference in me an GPAs between students who prefer a Kinesthetic learning style and students who did not prefer a Kinesthetic learning style. Table 4 35 ANOVA Sum of Squares Df Mean Square F Sig. NABP Score Between Groups 1458.030 1 1458.030 6.840 .010 Within Groups 25366.846 119 213.167 Total 26824.876 120 GPA Between Groups .215 1 .215 2.097 .150 Within Groups 12.177 119 .102 Total 12.392 120 Multi Modal and Non Multi Modal comparison on NABP and GPA Oneway Analysis Of Variance (Anova) Students who preferred a Multi modal learning style had a lower mean NABP score than students who did not prefer a Multi modal learning style but a higher mean GPA.

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57 Table 4 36 Descriptives N Mean Std. Deviation Std. Error NABP Score Not Multimodal 57 122.63 14.630 1.938 Multimodal 64 120.09 15.247 1.906 Total 121 121.29 14.951 1.359 GPA Not Multimodal 57 3.4230 .30463 .04035 Multimodal 64 3.5353 .32888 .04111 Total 121 3.4824 .32135 .02921 Table 4 37 Descriptives 95% Confidence Interval for Mean Lower Bound Upper Bound Minimum Maximum NABP Score Not Multimodal 118.75 126.51 71 142 Multimodal 116.29 123.90 80 141 Total 118.60 123.98 71 142 GPA Not Multimodal 3.3422 3.5038 2.80 3.92 Multimodal 3.4532 3.6175 2.91 4.00 Total 3.4246 3.5402 2.80 4.00 Table 4 38 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. NABP Score .229 1 119 .633 GPA .106 1 119 .745 The difference in mean NABP values was not statistically significant between the groups, the students who preferred the Multi modal learning style versus those who did not prefer the Multi modal learning style. Accept the null hypothesis of no relationship between the Multi modal learning styles and NABP scores.

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58 Students who preferred a Multi modal learning style had a higher mean GPA than students who did not prefer a Multi modal learning style. However, the difference between mean values was not statistic ally significant The difference between mean GPA values was marginally statistically significant at p = .055 level. Reject the null hypothesis because there is a statistically significant difference in the mean GPAs of the two groups. Table 4 39 ANOVA Sum of Squares df Mean Square F Sig. NABP Score Between Groups 194.175 1 194.175 .868 .353 Within Groups 26630.701 119 223.787 Total 26824.876 120 GPA Between Groups .380 1 .380 3.769 .055 Within Groups 12.011 119 .101 Total 12.392 120 T Test for G roups Female versus Male Females had a higher mean NABP score and GPA than males. The female mean GPA was only .0058 points higher. However, the differences between mean values were not statistically significant. Accept the null hypothesis of no difference between females and males on mean values of NABP and GPA. Table 4 40 Group Statistics Female N Mean Std. Deviation Std. Error Mean NABP Score Female 85 122.05 14.607 1.584 Male 36 119.50 15.801 2.634 GPASM Female 85 3.4841 .31664 .03434 Male 36 3.4783 .33674 .05612

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59 Table 4 41 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means F Sig. t df Sig. (2 tailed) Mean Differe nce Std. Error Differe nce 95% Confidence Interval of the Difference Lower Upper NABP Score Equal variances assumed .321 .572 .856 119 .394 2.55 2.976 3.347 8.441 Equal variances not assumed .829 61.558 .410 2.55 3.073 3.597 8.692 GPASM Equal variances assumed .470 .494 .090 119 .928 .0058 .06417 .12127 .13284 Equal variances not assumed .088 62.472 .930 .0058 .06580 .12572 .13729 T Test for groups White versus Not White Students who were not white had a higher mean NABP score and GPA than students who were white. However, the differences between mean values were not statistically significant. Accept the null hypothesis of no differences in mean values for NABP scores and GPA. Table 4 42 Group Statistics White N Mean Std. Deviation Std. Error Mean NABP Score White 90 120.84 14.485 1.527 Non White 31 122.58 16.413 2.948 GPASM White 90 3.4759 .31671 .03338 Non White 31 3.5013 .33910 .06090

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60 Table 4 43 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means F Sig. T df Sig. (2 tailed) Mean Differe nce Std. Error Differe nce 95% Confidence Interval of the Difference Lower Upper NABP Score Equal variances assumed .833 .363 .556 119 .579 1.74 3.123 7.919 4.447 Equal variances not assumed .523 47.113 .603 1.74 3.320 8.414 4.942 GPASM Equal variances assumed .264 .608 .378 119 .706 .0254 .06716 .15839 .10758 Equal variances not assumed .366 49.237 .716 .0254 .06945 .16496 .11416 T Test for G roups Visual versus Not Visual Students who preferred a Visual learning style had a lower mean NABP score than students who did not prefer a Visual learning style. However, the difference between mean values was not statistically significant. Accept the null hypothesis of no relationshi p between this preferred learning style and NABP scores. Students who preferred a Visual learning style had a higher mean GPA than students who did not prefer a Visual learning style. However, the difference between mean values was not statistically signif icant. Accept the null hypothesis of no relationship between this preferred learning style and GPA.

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61 Table 4 44 Group Statistics Visual or Not Visual N Mean Std. Deviation Std. Error Mean NABP Score Visual 83 120.95 14.946 1.641 Not Visual 38 122.03 15.136 2.455 GPASM Visual 83 3.4969 .32458 .03563 Not Visual 38 3.4508 .31611 .05128 Table 4 45 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means 95% Confidence Interval of the Difference F Sig. t df Sig. (2 tailed) Mean Differe nce Std. Error Differe nce Lower Upper NABP Score Equal variances assumed .663 .417 .366 119 .715 1.07 2.939 6.894 4.745 Equal variances not assumed .364 71.021 .717 1.07 2.953 6.963 4.814 GPASM Equal variances assumed .117 .733 .731 119 .466 .0461 .06306 .07879 .17095 Equal variances not assumed .738 73.602 .463 .0461 .06244 .07835 .17051 T Te st for G roups Aural versus Not Aural Students who preferred an Aural learning style had a lower mean NABP score than students who did not prefer an Aural learning style. However, the difference between mean values was not statistically significant. Accept null hypothesis of no statistically significant relationship between groups. Students w ho preferred an Aural learning style had a higher mean GPA than students who did not prefer an Aural learning style. However, the difference between

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62 mean values was not statistically significant. Accept null hypothesis of no statistically significant relat ionship between groups. Table 4 46 Group Statistics Aural or Not Aural N Mean Std. Deviation Std. Error Mean NABP Score Aural 67 121.19 14.767 1.804 Not Aural 54 121.41 15.315 2.084 GPASM Aural 67 3.5224 .31239 .03816 Not Aural 54 3.4328 .32825 .04467 Table 4 47 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means F Sig. T df Sig. (2 tailed) Mean Differ ence Std. Error Differe nce 95% Confidence Interval of the Difference Lower Upper NABP Score Equal variances assumed .201 .655 .078 119 .938 .21 2.746 5.650 5.223 Equal variances not assumed .077 111.785 .938 .21 2.756 5.675 5.248 GPASM Equal variances assumed .154 .696 1.533 119 .128 .0896 .05844 .02610 .20532 Equal variances not assumed 1.525 111.085 .130 .0896 .05875 .02681 .20603 T Test for G roups Read/Write versus Not Read/Write Students who preferred a Read/Write learning style had a lower mean NABP score than students who did not prefer a Read/Write learning style. However, the difference between mean values was not statistically significant. The difference between mean NABP values was marginally statistically significant at p = .066 level.

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63 Students who preferred a Read/Write learning style had a hig her mean GPA than students who did not prefer a Read/Write learning style. However, the difference between mean values was not statistically significant. Accept the null hypothesis that there is no statistically significant relationship. Table 4 48 Group Statistics Read or Not Read N Mean Std. Deviation Std. Error Mean NABP Score Read/Write 77 119.40 15.892 1.811 Not Read/Write 44 124.59 12.646 1.906 GPASM Read/Write 77 3.5113 .32524 .03706 Not Read/Write 44 3.4318 .31161 .04698 Table 4 49 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means 95% Confidence Interval of the Difference F Sig. T Df Sig. (2 tailed) Mean Differ ence Std. Error Differe nce Lower Upper NABP Sco re Equal variances assumed 2.701 .103 1.855 119 .066 5.19 2.797 10.727 .350 Equal variances not assumed 1.973 106.538 .051 5.19 2.630 10.401 .025 GPASM Equal variances assumed .096 .757 1.313 119 .192 .0795 .06055 .04041 .19937 Equal variances not assumed 1.328 92.842 .187 .0795 .05984 .03935 .19831 T Test for G roups Kinesthetic versus Not Kinesthetic Students who did not prefer a Kinesthetic learning style had a higher mean NABP score than students who preferred a Kinesthetic learning style. The difference between mean NABP values was statistically significant at p = .01 level. Therefore, reject the nu ll hypothesis of no statistically significant relationship.

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64 Students who prefer a Kinesthetic learning style had a higher mean GPA than students who did not prefer a Kinesthetic learning style. However, the difference between mean values was not statistica lly significant. Accept the null hypothesis; there is no difference in mean GPAs between the two learning style groups. Table 4 50 Group Statistics Kinesthetic or Not Kinesthetic N Mean Std. Deviation Std. Error Mean NABP Score Kinesthetic 21 113.71 17.097 3.731 Not Kinesthetic 100 122.88 14.042 1.404 GPASM Kinesthetic 21 3.5743 .28947 .06317 Not Kinesthetic 100 3.4631 .32569 .03257 Table 4 51 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means 95% Confidence Interval of the Difference F Sig. T df Sig. (2 tailed) Mean Differe nce Std. Error Differe nce Lower Upper NABP Score Equal variances assumed 1.948 .165 2.615 119 .010 9.17 3.505 16.105 2.226 Equal variances not assumed 2.299 25.962 .030 9.17 3.986 17.360 .971 GPASM Equal variances assumed 1.996 .160 1.448 119 .150 .1112 .07679 .04086 .26323 Equal variances not assumed 1.564 31.595 .128 .1112 .07107 .03365 .25602 T Test for G roups Multi Modal versus Not Multi Modal Students who preferred a Multi modal learning style had a lower mean NABP score than students who did not prefer a Multi modal learning style. However, the difference between mean values was not statistically significant. Accept the null

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65 hypothesis there is no statistically significant relationship between the NABP scores of the two groups. Students who preferred a Multi modal learning style had a higher mean GPA than students who did not prefer a Multi modal learning style. However, the d ifference between mean values was not statistically significant. Accept the null hypothesis that there is no statistically significant relationship in mean GPAs between student Multi modal preferences. Table 4 51 Group Statistics MultiModal or Not MultiMo dal N Mean Std. Deviation Std. Error Mean NABP Score Multimodal 64 120.09 15.247 1.906 Not Multimodal 57 122.63 14.630 1.938 GPASM Multimodal 64 3.5353 .32888 .04111 Not Multimodal 57 3.4230 .30463 .04035 Table 4 52 Independent Samples Test Levene's Test for Equality of Variances t test for Equality of Means 95% Confidence Interval of the Difference F Sig. T df Sig. (2 tailed) Mean Differe nce Std. Error Differe nce Lower Upper NABP Score Equal variances assumed .229 .633 .931 119 .353 2.54 2.724 7.933 2.857 Equal variances not assumed .934 118.325 .352 2.54 2.718 7.920 2.844 GPASM Equal variances assumed .106 .745 1.941 119 .055 .1123 .05786 .00224 .22690 Equal variances not assumed 1.950 118.808 .054 .1123 .05760 .00173 .22639

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66 CHAPTER 5 CONCLUSION This study has explored the relationship between learning styles, Demographics, GPA, and NABPLEX. Although, using both, One way ANOVA and T Test, analysis may be considered redundant; the intent of the study is to demonstrate consistency in the results produced by different methods. This final chapter will include a discussion of the findings as well as suggestions for future studies. The ultimate goal is to improve curriculum and the quality of the online components of the hybrid programs. Discussion of the Results Owen (2003) reported a positive correlation between age and GPA. These findings agree with the results of the analysis in this study which also shows a positive, significa nt correlation between age and GPA. According to Tokin (2003), the majority of the online students are females. The frequencies for the variables of interest in the study show an eighty five percentage e data, it appears that this group of female students has a higher GPA and NABPLEX score. However, the statistical analysis of both methods, the One Way ANOVA and T Test, showed that the difference is not statistically significant and therefore there is n o relationship between gender, GPA and NAPLEX scores. In other words, gender does not affect GPA or NAPLEX scores. As part of the demographics this study also reviewed the possible relationships between race, GPA and NAPLEX scores. The results of the One Way ANOVA agree with the results of the T Test. In both cases the results show that there are no statistically significant differences, concluding that there is no relationship between race, GPA and NAPLEX scores.

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67 However, the 2005 NAEP (National Assessm ent of Educational Progress http://nationsreportcard.gov/hsts_2005/hs_gpa_3a_1.asp ) report conveys a difference between White, Asian, Hispanic and Black students. While Asian students ha d a higher GPA than Whites, Blacks and Hispanic, White students had a higher GPA than Black and Hispanic. The fact that this study did not differentiate among minorities and combined all minorities together, could justify the disagreement. (i.e. the Asia n students raised the GPA) A more detailed study dividing the minorities in different ethnic groups Report Card. The literature review section of the study described differ ent conceptual models to categorize learning styles and the justification for the selection of the VARK questionnaire as the preferred method. During the analysis, two different methods, One Way ANOVA and T Test were used to search for the relationship or lack of relationship between each learning style category and GPA and NAPLEX scores. After reviewing the results of both analyses, the results indicate that there are no significant relationships between learning styles, demographics, GPA and NABPLEX. Non etheless, the study also included concerns expressed by professionals, Dr. Svinicki, Dr. Leite, and Dr. Shi, trying to validate the accuracy of the VARK questionnaire. This lack of proven validity associated with the selected questionnaire reinforces the caution expressed by them when interpreting the results. The same argument can be extended to the interpretation of the result of this study. However, when designing a hybrid course, as Palloff and Pratt (2005) recommend, faculty should understand the stu dents and their learning styles to create a learner

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68 centered and learner focused course which would maximize the online learning experience. Blackmore ( 1996 ) suggested that there are as many ways to teach as there to learn. An instructor, acknowledging th e existence of different learning styles could design classes to match the preferred style or mismatch it to help the student devel op an under used learning style Diaz (2001). Additional research is needed to understand what kinds of learners are likely to succeed in a hybrid environment. Since distance learning is student centered, understanding the personal characteristics of the students is an important component to overcoming possible barriers to learning ( Nastanski, M.,Slick, T.2008). Implications f or Higher Education Although the results of the study did not show a strong relationship between the preferred learning styles and the st be nefit of understanding how students learn best. Pungente, Wasan, and Moffett (2003) found a relationship between the learning styles and the Problem Based learning approach in their study of first year pharmacy students. T he difference in the result of their stud y and this one is likel y du e to the fact that they studied a specific area within the course whereas this study looked at the course as a whole. Additional research including a break down of the course component s would be able to determine if particular sections of a hybrid cour se have a stronger relationship with the preferred learning style of the students. Those findings could lead to a more customized curriculum in order to possibly change how we view curriculum design in Higher Education

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69 A PPENDIX A THE VARK QUESTIONNAI RE How Do I Learn Best? Questionnaire version 7.0 Choose the answer which best explains your preference and circle the letter(s) next to it. Please circle more than one if a single answer does not match your perception. Leave blank any question that does not apply. 1. You have finished a competition or test and would like some feedback. You would like to have feedback: a. using examples from what you hav e done. b. using a written description of your results. c. using graphs showing what you had achieved. d. from somebody who talks it through with you. 2. You are helping someone who wants to go to your airport, town centre or railway station. You would: a. a. go with her. b. write down the directions. c. tell her the directions. d. draw, or give her a map. 3. You are not sure whether a word should be spelled `dependent' or `dependant'. You would: a. think about how each word sounds and choose one. b. write both words on paper and choose one. c. find it in a dictionary. d. see the words in your mind and choose by the way they look.

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70 4. You want to learn a new program, skill or game on a computer. You would: a. use the controls or keyboard. b. follow the diagrams in the book that came with it. c. read the written instructions that came with the program. d. talk with people who know about the program. 5. You are going to choose food at a restaurant or cafe. You would: a. listen to the waiter or ask friends to recommend choices. b. choose from the descriptions in the menu. c. choose something that you have had there before. d. look at what others are eating or look at pictures of each dish. 6. Other than price, what would most influence your decision to buy a new non fiction book? a. Quickly reading parts of it. b. A friend talks about it and recommends it. c. It has real life stories, experiences and examples. d. The way it looks is appealing. 7. You have a problem with your knee. You would prefer that the doctor: a. described what was wrong. b. used a plastic model of a knee to show what was wrong. c. gave you a web address or something to read about it. d. showed you a diagram of what was wrong. 8. You are going to cook something as a special treat for your family. You would: a. ask friends for suggestions. b. look through the cookbook for ideas from the pictures.

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71 c. cook something you know without the need for instructions. d. use a cookbook where you know there is a good recipe. 9. Remember a time when you learned how to do something new. Try to avoid choosing a physical skill, eg. riding a bike. You learned best by: a. watching a demonstration. b. written instructions e.g. a manual or textbook. c. listening to somebody explaining it and asking questions. d. diagrams and charts visual clues. 10. I like websites that have: a. things I can click on, shift or try. b. audio channels where I can hear m usic, radio programs or interviews. c. interesting design and visual features. d. interesting written descriptions, lists and explanations. 11. Do you prefer a teacher or a presenter who uses: a. demonstrations, models or practical sessions. b. handouts, bo oks, or readings. c. diagrams, charts or graphs. d. question and answer, talk, group discussion, or guest speakers. 12. You are using a book, CD or website to learn how to take photos with your new digital camera. You would like to have: a. many examples of good and poor photos and how to improve them. b. a chance to ask questions and talk about the camera and its features. c. clear written instructions with lists and bullet points about what to do. d. diagrams showing the camera and what each p art does.

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72 13. You have to make an important speech at a conference or special occasion. You would: a. gather many examples and stories to make the talk real and practical. b. write a few key words and practice saying your speech over and over. c. write out your speech and learn from reading it over several times. d. make diagrams or get graphs to help explain things. 14. You are planning a holiday for a group. You want some feedback from them about the plan. You would: a. describe some of the highlights. b. use a map or website to show them the places. c. phone, text or email them. d. give them a copy of the printed itinerary. 15. A group of tourists want to learn about the parks or wildlife reserves in your area. You would: a. take them to a park or wildlife reserve and walk with them. b. give them a book or pamphlets about the parks or wildlife reserves. c. show them internet pictures, photographs or picture books. d. talk about, or arrange a talk for them about parks or wildlife reserves. 16. You are about to purchase a digital camera or mobile phone. Other than price, what would most influence your decision? a. Trying or testing it b. Reading the details about its fe atures. c. It is a modern design and looks good. d. The salesperson telling me about its features.

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73 APPENDIX B DATA DICTIONARY File: Demographics_GPA_Fall Year Entere d Academic Year the Student Entered the College of Pharmacy Class This data should be ignored or deleted all together. It is not relevant. Delete for Privacy Reasons Out of Seque nce This data should be ignored or deleted all together. It is not relevant. Delete for Privacy Reasons Last Last name of the student String First First name of the student String Camp us Campus that the student attended String GNV=Gain esville JAX=Jacks onville ORL=Orlan do STP=St. Pete UFID University of Florida Identification Number String SSN This data should be ignored or deleted all together. I t is not relevant. Delete for Privacy Reasons Gende r Gender of the student String Race Race of the student String Birthd ay Date Age Age at the time the student entered into COP Age in Years Index Date 9/29/2003 Value to calculate age AA Did the student have an Associate Arts degree at time of entry into college Y=Yes Blank=No Bachel ors Did the student have a BS degree at time of entry into college Y=Yes Blank=No GPAS M Pre pharmacy Science and Math Grade Point Average 0.00 4.00 PCAT Pre pharmacy Pharmacy College Admission Test Composite 0 100

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74 File Name: Names_UFID_Fall Last Last name of the student String First First name of the student String UFID University of Florida Identification Number String File Name: PCRA_1PD_Enter_Fall2003 http://www.cop.ufl.edu/ned/formgen/prca.htm T1 Last Name, First Name R2 I dislike participating in group discussions. 1=SA 2=A 3=Undecid ed 4=D 5=SD R3 Generally, I am comfortable while participating in a group discussion. R4 I am tense and nervous while participating in a group discussion. R5 I like to get involved in group discussions. R6 Engaging in a group discussion with new people makes me tense and nervous. R7 I am calm and relaxed while participating in group discussions. R8 Generally, I am nervous when I have to participate at a meeting. R9 Usually I am calm and relaxed when participating at meetings. R10 I am very calm and relaxed when I am called upon to express an opinion at a meeting. R11 I am afra id to express myself at meetings. R12 Communicating at meetings usually makes me uncomfortable. R13 I am very relaxed when answering questions at at meeting. R14 While participating in a conversation with a new acquaintance, I feel very nervous. R15 I have no fear of speaking up in conversations. R16 Ordinarily, I am very tense and nervous in conversations. R17 Ordinarily, I am very calm and relaxed in conversations. R18 While conversing with a new acquaintance, I feel very relaxed. R19 I am afraid to speak up in conversations. R20 I have no fear of giving a speech. R21 Certain parts of my body feel very tense and rigid while giving a speech. R22 I feel relaxed while giving a speech. R23 My thoughts become confused and jumbled when I am giving a speech. R24 I face the prospect of giving a speech with confidence. R25 While giving a speech, I get so nervous I forget facts I really know. Delete Everything After R25 (Column Z)

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75 How Do You Learn Best (VARK) http://www.cop.ufl.edu/ned/formgen/ried6.htm T1 Last name, First Name T2 Social Security Number R3 Campus the Student Attended You are about to give directions to a person who is standing with you. She is staying in a hotel in town and wants to visit your house later. She has a rental car. I would: C4 draw a map on paper On=selecte d Blank=not selected C4A tell her the directions C4B write down the directions (without a map) C4C collect her from the hotel in my car You are not sure whether a word should be spelled 'dependent' or 'dependant'. I would: C5 look it up in the dictionary C5A see the word in my mind and choose by the way it look C5B sound it out in my mind C5C write both versions down on paper and choose one. You have just received a copy of your itinerary for a world trip. This is of interest to a friend. I would: C6 phone her immediately C6A send her a copy of the printed itinerary C6B show her on a map of the world C6C share what I plan to do at each place I visit You are going to cook something as a special treat for your family. I would: C7 cook something familiar without the need for instructions C7A thumb through the cookbook looking for ideas from the pictures C7B refer to a specific cookbook where there is a good recipe A group of tourists has been assigned to you to find out about wildlife reserves or parks. I would: C8 drive them to a wildlife reserve or park C8A show them slides and photographs C8B give them pamphlets or a book on wildlife reserves or parks C8C give them a talk on wildlife reserves or parks. You are about to purchase a new stereo. Other than price, what would most influence your decision? C9 the salesperson telling you what you want to know C9A reading the details about it C9B playing with the controls and listening to it C9C it looks really smart and fashionable

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76 Recall a time in your life when you learned how to do something like playing a new board game. Try to avoid choosing a very physical skill, e.g., riding a bike. I learned best by: C10 visual clues -pictures, diagrams, charts C10A written instructions C10B listening to somebody explaining it C10C doing it or trying it You have an eye problem. I would prefer the doctor to: C11 tell me what is wrong C11A show me a diagram of what is wrong C11B use a model to show me what is wrong You are about to learn to use a new program on a computer: I would: C12 sit down at the keyboard and begin to experiment with the program's features C12A read the manual which comes with the program C12B telephone a friend and ask questions about it. You are staying at a hotel and have a rental car. You would like to visit friends whose address/location you do not know. I would like them to: C13 draw me a map on paper C13A tell me the directions C13B write down the directions (without a map) C13C collect me from the hotel in their car Apart from the price, what would most influence your decision to buy a particular textbook? C14 I have used a copy before C14A A friend talking about it C14B Quickly reading parts of it C14C The way it looks appealing A new movie has arrived in town. What would most influence your decision to go (or not to go)? C15 I heard a radio review about it C15A I read a review about it C15B I saw a preview of it Do you prefer a lecturer or teacher who likes to use? C16 a textbook, handouts, readings C16A flow diagrams, charts, graphs C16B field trips, labs, practical sessions C16C discussion, guest speakers Delete everything after 16C (Column AZ) Prep1 http://www.cop.ufl.edu/ned/formgen/prep1.htm T1 First Name T2 Last Name

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77 T3 UFID R4 Gender R5 Academic Year T6 What Year Do You Expect to Graduate T7 Birth date R8 Communicate pertinent information from the patient's medical record to other he hea lth care professionals. 1 = poor 4 = average 7 = excellent R 9 Communicate pertinent information from the patient's medical record to the patient. R 10 Recommend appropriate drug therapy for a specific patient. R 11 Evaluation medications and/or laboratory tests. R 12 Integrate knowledge in the basic and clinical sciences to design, implement, and evaluate patient specific pharmacotherapeutic regimens to prevent or resolve medication related problems. R 13 Evaluate and determine the validity, acceptability, and legitimacy of the prescription order from a physician for a specific patient. R 14 Determine the appropriate drug delivery system or drug product for a specific patient. R 15 Recommend medication doses and dosage schedules for a specific patient based upon relevant patient factors and drug pharmacodynamic and pharmacokinetic properties. R 16 Identify and collect all information needed to prevent or resolve a medication related problem. R17 Identify and collect all information needed to respond to an information request from another health care professional using appropriate resources and technology. R 18 Respond to an information request from a patient. R19 Evaluate laboratory test results for a specific patient. R 20 Calculate and evaluate drug pharmacokinetic properties for a specific patient. R2 1 Evaluate information obtained from the patient's history and physical assessment. R2 2 Integrate and apply the basic and clinical pharmaceutical sciences and standards for the preparation and dispensing of commercially available products, compounded individual or bulk products and sterile dosage forms and enteral nutritional produ cts. R2 3 Make reasonable assumptions and/or draw reasonable conclusions when data is incomplete. R2 4 Provide counseling to patients and/or caregivers relative to the proper use and effects of medications. R2 5 Devise methods or approaches to seek optimal patient compliance. R2 6 Monitor and document the safety and efficacy of a therapeutic plan for a specific patient. R2 7 Document information related to the identification, resolution, or

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78 prevention of drug related problems in individual patients. R2 8 Provide pharmaceutical care in a professional, ethical, and compassionate manner. R2 9 Evaluate, select and purchase pharmaceuticals, durable medical equipment devices and supplies. R 30 Develop and implement a pharmacy inventory control system to distribute and administer medications and durable medical equipment devices. R3 1 Provide emergency care on a limited basis. R3 2 Promote public awareness of health and disease. R3 3 Comply with federal, state and local laws and regulations that affect the practice of pharmacy. R3 4 Manage the operation, fiscal resources and human resources of a community, institutional or other pharmacy practice site to optimally serve the needs of patients. R3 5 Participate in the development and implementation of drug use evaluations and the formulary service. R3 6 Discuss and describe the general process of research in the clinical and basic pharmaceutical sciences. R3 7 Provide a critical and thoughtful review of a publication in the basic or clinical pharmaceutical sciences. R3 8 Systematically gather information needed to resolve a problem or situation. R3 9 Synthesize information and create a solution, hypothesize, draw conclusions, conjecture alternatives, or decide a course of action for a problem or situation. R 40 Make decisions regarding complex problems that require the integration of scientific, social, cultural, and ethical issues with one's ideas and values. R4 1 Use data and computers in professional practice. R4 2 Recognize the impact of values in personal and professional interactions with other individuals. R4 3 Apply ethical theories and principles to personal and professional values and decisions. R4 4 Recognize and explain how social, cultural, historical, political, and/or scientific issues impact upon the changing health care environment. R4 5 Understand one's professional practice in relationship to changing societal expectations on the role of pharmacists in the health care system. R 46 Adapt and use appropriate interpersonal and intergroup behaviors during professional interactions with patients, other health care providers and the public. R4 7 Contribute opinions, insights, information, and leadership assertively and appropriately during the health care team decision making process. R4 8 Assume leadership positions and/or participate in community and professional matters that involve human health and civic concerns that may or may not be health related. S49 Based on your experience to date, can you think of any areas not covered in the curriculum that should be taught?

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79 S50 If you could change one area of education, what would it be? S51 How would you change it? After this point, do not need the data Residency List 2007 Name Name R16 Residency (1=yes; missing = no) 2008 Graduating Class Names A Last Name B First Name C Middle Name D Campus G=GNV J=JAX O=ORL S=STP E UFID Number Do not Need anything else NABP Individual Scores May 2007 Graduates A Last Name B First Name C SSN D Campus G=GNV J=JAX O=ORL S=STP E Pass 0=No 1=Yes F Scaled Score G Area 1 H Area 2 I Area 3

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80 LIST OF REFERENCES Jonnassen, D. (2004) Handbook of Research on Educational Communications and Technology (pp.355). Mahwah, N.J. : L. Erlbaum Associates, 2004 Howard,C.,Schenk, K., Discenza, R. (2004) Distance Learning and University Effectiveness: Changing Education Paradigms for Online Learning Hershey, (pp.1)PA : Idea Group Publishing, 2004 Weller, M., (2003) Delivering on the Net: The why, what & how of onli ne education. New York : RoutledgeFalmer. Moore, M, Anderson, W. (2003) Handbook of Distance Education. Mahwah, N.J. : L. Erlbaum Associates, 2003 Policy on Distance Learning retrieved on January 31, 2008 from http://www.ribghe.org/distance%20learning.pdf Digest of Education Statistics ( http://nces.ed.gov/programs/digest/d06 / ) VARK Retrieved February 4, 2008 from http://www.vark learn.com/english/page.asp?p=faq ANOVA Retrieved on July 9, 2009 from http://www.businessdictionary.com/definition/analysis of variances ANOVA.html Dutton, J., Dutton, M., Perry, J. (2002) How do online students differ from lecture students? Journal of Asynchronous Learning Networks Retrieve Jan 31, 2008 from http://www.sloan c.org/publications/jaln/v6n1/pdf/v6n1_dutton.pdf Fleming, N.D. & Mills, C. (1992). Not another inventory, rather a catalyst for reflection To improve the academy (11), 137 149. Christensen, C. M., Aaron, S., & Clark, W. (2001). Disruption in education. Proceedings of the Internet and the University: 2001 Forum. Cambridge, MA: Massachusetts Institute of Technology. Retrieved February 4, 2008 from http://www.educause.edu/forum/ffpiu01w.asp http://www2.cdc.gov/PHTN/lingo.asp About Learning styles Retrieved February 4, 2008 from ( http://www.learningstyles.net/index.php?option=com_content&task=view&id=20&Itemid =70&lang=en )

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81 Schlosser, L. A., & Simonson, M. (2002). Distance education: Definit ion and glossary of terms Bloomington, IN: Association for Educational Communications and Technology. Foster, A., Carnevale, D. (2007) Distance Education Goes Public: Looking to make money and reach more working students, state universities are pushing in to the U. of Phoenix's territory The Chronicle of Higher Education Retrieved Jan 31, 2008 from http://chronicle.com/weekly/v53/i34/34a04901.htm Bernard, Robert M., Abrami, Philip C., Lou, Yiping, Borokhovski, Evgueni, Wade, Anne, Wozney, Lori, Wallet, Peter Andrew, Fiset, Manon, Huang, Binru How Does Distance Education Compare With Classroom Instruction? A Meta Analysis of the Empirical Literature Review of Educational Research 2004 74: 379 439 retrieved Feb 15, 2008 form http://intl rer.sagepub.com/cgi/reprint/74/3/379 Lin, S.,Crawford, S. (2006) An Online Debate Series for First Year Pharmacy Students. American Journal of Pha rmaceutical Education Retrieved Feb 4 2008 from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1847552 Allen, E., Seaman, J. (2005) Growing by Degrees Online Educa tion in the United States. Olin College Sloan C Moore, J. (2005) Elements of Quality Online Education: Engaging Communities: Wisdom from the Sloan Consortium Olin College Sloan C. Allen, E., Seaman, J. (2006). Making the Grade: Online Education in the United States Olin College Sloan C University of Florida College of Pharmacy Distance Education Programs retrieved on Feb 2, 2008 from http://www.cop.ufl.edu/studaff/disted.htm Salmon, G. (2004) E Moderating: The Key to Teaching and Learning Online. New York, NY Routledge 2004 Felder, R., Spurlin, J (2005) Applications, Reliability and Validity of the Index Learning Styles. Tempus Publications, 2005 retrieved on Feb 16, 2008 from http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSdir/ILS_Validation(IJEE).pdf Shuck, Avis A., Phillips, Charles R. (1999) Assessing Pharmacy Students' Learning Styles and Personality Types: A Ten Year Analysis. American Journal of Pharmaceutical Education Volume 63, Issue 1, 1999, Pages 27 33 Casini, M.; Prattichizzo, D.; Vicino, A. (2003) The automatic control telelab: a us er friendly interface for distance learning, Education, IEEE Transactions on On page(s): 252 257, Volume: 46, Issue: 2, May 2003

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82 Howell, S. L., Williams, P. B., & Lindsay, N. K. (2003). Thirty two trends affecting distance education: An informed foundati on for strategic planning. Online Journal of Distance Learning Administration, 6 (3). Retrieved Feb 17, 2008 from http://www.westga.edu/~distance/ojdla/fall63/howell63.html Adamcik, B ., Hurley, S., Erramousque (1996) Critical Thinking and Problem Solving Abilities American Journal of Pharmaceutical Education, Volume 60, Fall 1996 problem J. Appl. Beh. Sci., 21,37 49(1985). Moloney, J.F., Oackley, B. (2006) Scaling Online Education: Increasing access to Higher Education. Journal of A synchronous Learning Networks 2006 Retrieved Feb 24, 2008 from http://www.sloan c.org/publications/jaln/v10n3/pdf/v10n3_2moloney.pdf Hank, T.F., Shah, A.J., (2007). Usin g Learning Styles Instruments to Enhance Student Learning. Decision Science Journal of Innovative Education Volume 5, Number 1 (Jan.2007). Creswell, J.W. (2008) Educational research: Planning, conducting, and evaluating quantitative and qualitative resea rch. Upper Saddle River, NJ: Pearson Education. Rothenberg, L., Hessiling, P. (1990) Applying the APA/AERA/NCME "Standards": Evidence for the Validity and Reliability of Three Statewide Teaching Assessment Instruments. Retrieve March 26, 2008 from http://www.eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/2 0/47/05.pdf Baker, R. K. (2003). A framework for design and evaluatio n of Internet based distance learning courses: Phase one: Framework justification, design and evaluation. Online Journal of Distance Learning Administration 6 (2). Retrieved Ma rch 26 200 8 from http://www.westga.edu/~distance/ojdla/ Coffield, F., Mosoley, D.,Hall, E., Eccleston, K.(2004). Learning styles and pedagogy in post 16 learning: A systematic and critical review. Learning and Skill Research Center London, UK. Cano, F. (2006). An In D epth Analysis of the Learning and Study Strategies Inventory (LASSI). Educational and Psychological Measurement Volume 66,Number 6. Retrieved on March 27, 2008 from http://epm.sagepub.com/cgi/rep rint/66/6/1023 Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Self regulation interventions with a focus on learning strategies. In P. R. Pintrich & M. Boekaerts (Eds.), Handbook on self regulation New York: Academic Press.

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83 Pringle RK, Lee J. The use of Learning and Study Strategies Inventory (LASSI) as a predictor for success or failure on part I of the National Board of Chiropractic Examiners Test. J Manipulative Physiol Ther. 21 :164 6. 1998 Mar Apr Retrieved on March 30, 2008 from http://www.ncbi.nlm.nih.gov/pubmed/9567235 Lobb, W.B., Wilkin, N.E., McCaffrey, D.J., Wilson, M.C, Bentley, J.P. (2006). The predictive utility of nontraditional test scores for first year pharmacy student academic performance. American Journal of Pharmaceutical Education Volume 70, Number 6, Ret rieved on March 31, 2008 form http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1803690 Melancon, J.G. (2002). Reliability, structure, and correlates of learning and study strategies inventory scores. Educational and Psychological Measurement Volume 62, Number 6. Retrieve on March 31, 2008 from http://epm.sagepub.com/cgi/reprint/62/6/1020 Deming, M. P., Valeri Gold, M., & Idleman, L. S. (1994). The reliability and validity of the Learning and Study Strategies Inventory (LASSI) with college developmental students. Research and Instr uction 33 309 318. work. Amason, R. F.(2007) Normative analysis of Internet mediated distance education po licies in selected large community colleges and their related state systems. Unpublished doctoral dissertation, University of Florida, Gainesville. Crocker, L. & Algina, J. (1986). Introduction to Classical and Modern Test Theory. New York: Holt, Rineh art, and Winston. Irizarry, R. (2002). Self efficacy & motivation effects on online psychology student retention. USDLA Journal, 16 (12). Retrieved September 12, 2008 from http://www.usdla.org/html/journal/DEC02_Issue/article07.html Kelley, K. A., Brackett, C. C., Coyle, J. and Pruchnicki, M. C. 2006 07 05 "Learning Styles, Personality Type, and Pedagogy: How Do They Relate?" Paper presented at the annual meeting of the American Association of Colleges of Pharmacy, Sheraton San Diego Hotel & Marina, San Diego, California, USA Retrieved 2008 12 12 from http://www.allacademic.com/meta/p117853_index.html Fle ming, N., and Baume, D. (2006) Learning Styles Again: VARKing up the right tree!. Educational Developments, SEDA Ltd, Issue 7.4, Nov. 2006, p4 7. Ross, C.M. and Lukow, J. E.(2004) Are Learning Styles a Good Predictor for Integrating Instructional Techno logy Into a Curriculum?. Journal of Scholarship of Teaching and

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84 Learning, Vol.4. No.1, May 2004. Retrieved January 16, 2009 from http://www.iupui.edu/~josotl/2004vol4no1/RossLukow.pdf L eite, L. W, Svinicki, M, Yuying, S (2008) Running Head: Attempted Validation of the scores of the VARK. Tonkin, S. E. ( 2003 ) Predisposing characteristics of learner success in online education. Ph.D. diss., The College of William and Mary in Virginia. Owen, T. R. (2003). Retention Implications of a Relationship between Age and GPA. College Student Journal June 2003. Retrieved July 7, 2008 from http://findarticles.com/p/art icles/mi_m0FCR/is_2_37/ai_103563740/ Nastanski, M., Slick, T. (2008) Learning s tyles and the o nline c lassroom: Implications for b usiness s tudents. Unpublished manuscript Palloff, R., Pratt, K. (2005) The Role and Responsibility of the Learner in the On line Classroom. 19 th Annual Conference on Distance Teaching and Learning Retrieved July 10, 2009 from http://www.uwex.edu/disted/conference/Resource_library/proce edings/03_24.pdf Diaz, D., Bontental, K., (2001) Learner Preferences: Developing a Learner Centered Environment in the Online or Mediated Classroom. Ed at a Distance Journal Vol15. No 8. Retrieved Nov 10, 2009 from http://www.usdla.org/html/journal/AUG01_Issue/article03.html#1 http://nationsreportcard.gov/hsts_2005/hs_gpa_3a_1.asp Retrieved on July 13, 2009 Blackmore, J. (1996). Pedagogy: Learning styles. Retrieved on Nov.5, 2009 from : http://granite.cyg.net/~j blackmo/diglib/styl a.html Kelley, K. A., Brackett, C. C., Coyle, J. and Pruchnicki, M. C. ( 2006 ) "Learning Styles, Personality Type, and Pedagogy: How Do They Relate?" Paper presented at the annual meeting of the American Association of Colleges of Phar macy, Sheraton San Diego Hotel & Marina, San Diego, California, USA. Retrieved Nov 5, 2009 from http://www.allacademic.com/meta/p117853_index.html Pungente, M., Wasan, K., and Moffett, C., First the Problem American Journal of Pharmaceutical Education Vol.66, Summer 2003.

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85 BIOGRAPHICAL S KETCH Jose Bernier is an Information Technology professional with more than 12 years of experience in the field. He has extensive experience managing IT departments both at the national and international level. Bernier holds a bachelor of science degree in computer engineering (1997) from the University of Central Florida He has a master of science in management of information systems (2001) from Florida International University and a specialist in education degree from the University of Florida awarded in 2006 He also completed his doctorate in higher education administration from the University of Florida. Other professional certifications include Microsoft certified systems engineer.