Prompt:
An investigation of student performance and satisfaction in online courses using Kolb's learning style inventoryAbstractThe purpose of this study was to investigate the influence that learning styles, as measured by the Kolb’s Learning Style Inventory, had on student performance, and satisfaction in undergraduate online courses. The participants included students (N=100) enrolled in online math courses at a small, private university in the Southeastern United States. Each participant was categorized into four different groups (Accommodators, Divergers, Assimilators, and Convergers) utilizing the electronic version of Kolb’s Learning Style Inventory 3.1. For satisfaction measures, the Student Satisfaction Survey was used (Strachota, 2006). The research design measured the relationship between learning styles, as measured by the Kolb Learning Style Inventory (IV), and student performance and satisfaction (DV) in an online course. Half of the participants were assigned to the experimental group, which answered the Kolb Inventory, whereas the other half remained unaware of their own learning style. This quasi-experimental, control and treatment group comparison was used to contrast means between groups on performance and satisfaction. Results of this study indicated that learning style had an effect on both performance and satisfaction.Table of ContentChapter 1: IntroductionStatement of the ProblemThe universities in the 21st century have been influenced by the advancement of technology.  Altbach, Reisberg, and Rumbley (2009) projected that in a few years, traditional universities will be replaced by innovation in technology and distance education.  Many educators and educational researchers have predicted that Internet-based distance higher education would become a central part of the delivery (Moller & Huett, 2012). With the proliferation of technology at low costs and with the ease of access to the World Wide Web, online instruction over the Internet has become a supplement and/or replacement of traditional classes (Huang, Lin & Huang, 2012).  In fact, in 2011, it was estimated that about $35.6 billion was spent on self-paced e-learning across the globe (Pappas, 2015).  In the United States, the number of students taking at least one distance course was 3.9%, up from the 3.7% rate from 2013 to 2014 (Allen & Seaman, 2016).In addition, the Docebo report (2014) on e-learning market trends stated that by the year 2019 “half of all college classes will be online-based.  However, there is still a belief that differences in individual cognitive styles and learning behaviors which are reflected in their  learning styles, have affected the performance of online students” (E-Learning Market Trends & Forecast 2014 - 2016 Report, 2014, p.7). Furthermore, Tremblay, Lalancette, and Roseveare (2012) indicated that “internet-based online instructional delivery is the fastest growing type or sector of higher education in many countries” (p.24).  According to Allen and Seaman (2013a), there are at least “7.1 million students taking at least one online course in the United States” (p.6).  In addition, the researchers discovered that “70.8% of academic leaders believed that online learning is critical to their institution’s long-term strategy” (p 4).According to Merriam, Caffarella, and Baumgartner (2007) Internet-based learning (e-learning) takes place when a teacher and student are separated by physical distance and technology connect the distance gap.  E-learning permits education access seven days a week, and twenty-four hours a day (Akkoyunlu & Soylu, 2008), which allows access to a vast number of students (Dziuban, Hartman, & Moskal, 2004; Osguthorpe & Graham, 2003). These days, students come to the learning environment with a different series of knowledge and proficiencies that are either unnoticed or unaddressed by educators (Considine, Horton & Moorman, 2009).  Most of today’s college students are “techno-savvy” (Stanford & Reeves, 2009), however, they are feeling dissatisfied with the educational system (Haynes, 2012).  According to Blashki, Nichol, Jia and Prompramote (2007), this is due to, “the failing of educational institutions to address the learning needs and perhaps equally important, the learning styles of their students (p.410).  Schunk (2012) indicated that “social, cultural, and technological changes require educational institutions to adapt to their environments and survive” (p. 16).  Since the beginning of the 21th century, the educational environment has experienced a radical change. The educational environment has placed more emphasis on the role of the learner and technology (Ezeh & Ohia, 2014).  However, instructional designers and educators should take into consideration the students’ cognitive styles and learning preferences since these variables could affect the success in this new type of course delivery (Sheard & Lynch, 2003).  In addition, Sheard and Lynch (2003) claim that, “accommodating learner difference will ultimately enhance students’ learning experiences and learning outcomes” (p. 244). Furthermore, learning style preferences are unique among individuals (Herrmann-Nehdi, 2015; Mupinga, Nora, & Yaw, 2006).  Understanding how learning takes place and the mental activities performed during the learning process are important for educators, and educational psychologists (Wu, 2010).  For this reason, studies about how the brain acquires information and learning styles continue to evoke interest both in the past and today (Bielefeldt, 2006).The developments in neuroscience have supported an array of research which has provided us with a more complete picture of how learning occurs (Willingham & Lloyd, 2007).  For instance, neuroscientists have found that different parts of the brain perform different functions, and that learning actually changes the physical structure of the brain by expanding the brain’s capacity (Sousa, 2010).  It has also been discovered that learning is an individual process and each of us process information differently.  This influences how each of us grasp visual, aural, verbal, or tactile information (Sousa, 2010). In addition, advancement in technology such as positron emission tomography (PET) and functional magnetic resonance imaging (FMRI), have aided neuroscientists to observe human learning processes directly (Kolb & Whishaw, 2003).  In fact, brain imaging is currently a most widely used neuro-scientific research tool employed when comparing people performing different tasks, for example, an “experimental” task and a “control” task (Camerer, Loewenstein & Prelec, 2005).  The images taken while a student is performing the two tasks provide a contrasting picture of regions of the brain when activated (Camerer, et al. 2005).  In order to further understand the human learning process, Kolb developed the experiential learning cycle model, also known as experiential learning theory (Zull, 2002). According to Kolb (1981), “The experiential learning model represents an integration of many of the intensive lines of research on cognitive development and cognitive style” (p.235). Ramsey (2005) stated that, “Kolb’s theory accentuates individual’s cognitive understanding and adaptation to a real world” (p.219). After many years of researching Kolb’s theory, Zull (2002) discovered a similarity between the experiential learning cycle and the structures of the brain.Experiential learning theory is one of the best known educational theories in higher education (Healey & Jenkins, 2000). It is grounded on the work of prominent scholars such as John Dewey, Kurt Lewin, Jean Piaget, William James, Carl Jung, Paulo Freire, and Carl Rogers who gave experience a perpetual theme in their human learning and development theories (Kolb, & Kolb, 2009). Additionally, Kolb’s theory gives a foundation for a compendium of learning methods, including independent learning, which has recently attracted a great deal of attention within the higher education field (Healey & Jenkins, 2000).The topic. The topic of this research study is to investigate whether learning styles influences satisfaction and performance in student enrolled in an online course. Many scholars have recognized that each individual human being possess their own learning style. However, the topic of learning styles is a controversial one. There are a great amount of researchers who claimed that there is no plausible evidence that learning styles actually exist. In addition, studies on online student retention has been another leading topic in higher education. Many higher education administrators and researchers have identified that satisfaction, not only increases performance, but is a major ingredient for students to succeed in an online course.   The research problem.   Online learning has been increasing and attracting many people in the world, however, it is difficult to create a course which addresses all the students learning styles.  Because of encountering these problems, Coffield, Moseley, Hall, and Ecclestone (2004) identified 71 models of learning styles which they categorized into five families.  This researcher decided to use the Kolb’s Experiential Learning Theory because Mestre (2006) explained that out of all those learning styles’ models "a number of studies have applied Kolb’s theory to investigate how learning styles affect e-learning” (p.29).Felder and Brent (2005), stated that the difficulty to create a one course for everyone is one of the major factors that affect learning.  The fact remains that people learn differently; therefore, they have different learning styles and process information differently (Sims & Sims, 1995; Felder, 1996).  Felder (1993) also argued that, “students whose learning styles are compatible with the teaching style of a course instructor tend to retain information longer, apply it more effectively, and have more positive post-course attitudes toward the subject than do their counterparts who experience learning/teaching style mismatches” (p.288). 	In addition, supporters of learning styles argued that learning styles influence a student’s ability to participate successfully in an online course. Wooldridge (1995), assuming that selected learning styles are better for online courses, suggests that before students in higher education sign up for online courses, they should be made aware of their learning styles. This can help them make the decision as to whether an online course will be fit for their learning. Furthermore, Moan and Dereshiwsky (2002) caution that “not all distance education experts are convinced of their validity in predicting student success in online learning”. For example, Fahy and Ally (2005) stated that, “inconsistent outcomes are common regarding whether styles affect academic performance” (p. 6). Doherty and Maddux (2002) indicated that studies examining the relationship between learning styles and online learning have been inconclusive.  After investigating numerous studies on the relationship between learning styles and achievement, Santo (2006) discovered that there was not significant difference in performance between student enrolled in an online course and learning styles. The wealth of online educational programs has produced new choices for students. It has also created competition among programs and higher education institutions. In spite of these competitions, higher education administrators have been preoccupied with the high dropout rates in online learning in higher education institutions (Park & Choi, 2009). Concerned with this fact, administrators and researchers investigated factors that influence students’ drop out or lack of completion of distance learning courses to determine strategies that can be used to improve retention rate. Their findings revealed that satisfaction was a major factor which influences student decision on dropping out or completing an online course (Pritchett, 2009).Furthermore, three decades ago, Astin (1977) stated that, “It is difficult to argue that student satisfaction can be legitimately subordinated to any other educational outcome” (p. 164). Nowadays, many researchers and higher education administrators are concentrating on identifying and satisfying the needs and expectations of today’s students (DeShields, Kara, & Kaynak, 2005). In addition, Noel-Levitz (2009) predicted that, “As the number of online students increase, they will become a priority and their satisfaction will become a ‘core element’ for colleges and universities to assess in terms of satisfaction. This is because “satisfied students are more likely to be successful students” (p. 1).In the last century, researchers such as Biner, Barone, Welsh, and Dean (1994) stated that researchers did not give the proper attention to student satisfaction in distance learning environments.  In fact, Allen, Bourhis, Burrell, and Mabry (2002) found limited studies on online education and student satisfaction. However, those studies were from 1989-1999, which do not reflect the current data. In spite of this, in the last fifteen-years, countless studies have been conducted to measure the student satisfaction in online learning at university level (Butt & Rehman, 2010).  Additionally, Davis (2014) pointed out the concern as to whether higher education students are satisfied and can perform well in courses delivered in an online environment.  Unfortunately, “There is a lack of evidence or research aimed at determining the effects of online instruction on students’ levels of satisfaction and performance in online classes” (LeShea, 2013, p.13). Background and Justification. Online learning has been a current trend which has captivated interest of a voluminous amount of students every year.  A study conducted by the Hoover Research in 2010, found that universities worldwide have enrolled from 100,000 to over 500,000 students (Hoover Report, 2011).  A survey conducted by Allen and Seaman (2013b), discovered that U.S. institutions are seeing an increase in demand for online courses and programs than for F2F programs in 2010.  This means that three-quarters (74.5 percent) of all institutions participating in the survey reported an increase in the demand for online courses and programs (Allen & Seaman, 2015).Despite the great demand for online courses, there is inconclusive evidence supporting that online courses increase student performance.  A study conducted by Aragon, Johnson, and Shaik (2002), evaluated student learning styles and compared student performance in traditional courses with e-learning courses.  They found that “student outcome can be equal regardless of learning styles in either delivery format” (p.227).Similarly, the significance of learning styles in the context of the online learning environment has been analyzed and debated by researchers such as Adams (2007); Aragon, et al. (2002); and Miller & King (2003).  The majority of these studies have indicated no significant differences in learner performance outcomes or learner satisfaction with regards to online learning modality or individual learning style (Constantine, 2012). Arp, Woodard, and Mestre (2006) believed that this is because there is no one preferred set of learning styles that accommodates all students, or even one specific learning style that seems to best fit a particular ethnic or cultural group. Moreover, Ak (2008) supported the idea that learning styles and learning environment determines a student’s unique learning preference.  This action establishes a connection between the learning environment and the cognitive learning styles.  Sheard and Lynch (2003) stated that “individual’s preferred learning style is a significant factor in how learning is experienced and it also has an impact on learning outcomes when learners encounter new or complex problems or are presented with new information” (p. 250). Furthermore, the components that influence the academic success of students in an online program have always been of interest to researchers and educators (Bielefeldt, 2006).  One such element is cognitive preferences, which influences the learning experience for the student.  Naimie, Siraj, Piaw, Shagholi, and Abuzaid, (2010) indicated that “most of the research on online learning and learning styles has been cross-sectional in nature” (p.349).  Many of these studies focus on student learning styles and demographical variables such as gender, age, university major, ethnicity, and personality (Ahad, 2006; Naimie, et al., 2010; Marcy, 2010).  There is a plethora of studies which compare traditional and online methods of learning (Ferrara, 2009; Salcedo, 2010; Stack, 2015).  Unfortunately, there is a limited number of studies which look at the area of learning styles and student performance enrolled in an online course (Bawaneh, Abdullah, Saleh & Yin, 2011; Özgen, Tataroğlu, & Alkan, 2011).  Additionally, Astin (1993) indicated that, “Satisfaction is an important variable that influences the students’ level of motivation” (p.278) and this motivation is an important psychological factor in academic performance (Chute, Thompson, & Hancock, 1999; Donahue & Wong, 1997).  Garrath-Reed, Roberts, and Heritage (2016), reported mixed results when comparing student satisfaction in face-to-face and online courses. For instance, Sitzmann Kraiger, Stewart and Wisher (2006) and Taylor (2001) reported that levels of student satisfaction were similar for online and classroom-based courses. On the other hand, Poirier and Feldman (2004) reported that there are no differences in overall course satisfaction when comparing online psychology course and classroom- based psychology course. Deficiencies in the evidence.  Even though, it is widely accepted that matching instructional modes and learners’ cognitive style improve learning, researchers such as, Reiner and Willingham (2010) had a difficult time connecting the importance or the existence of learning styles to learning.  Coffield, et al. (2004) stated that “the theory and practice of learning styles has generated great interest and controversy over the past 20 years and more” (p. 1).Likewise, while reviewing learning style models, Santo (2006) attempted to discover evidence for the frequent claim that learning style affects the ability to do well in an online class.  The researcher’s review showed that learning style has almost no impact on online learning outcomes (Speece, 2012). Conversely, Bostrom, Olfman and Sein (1993), believed that learning style is a good predictor of an individual’s preferred learning behavior.  For instance, Graham, Garton, and Gowdy (2001) reported that “learning style has been found to be an important variable in students’ academic achievement” (p.31).  A study conducted by Cowley et al.  (2002) inferred that students may benefit from understanding their own learning style by taking measures to adjust the way they acquire knowledge.In addition, an important topic which is often addressed in discussions related to learning styles is whether styles are different from abilities (Rassaei, 2012).  Pashler, McDaniel, Rohrer and Bjork (2009) argued that “the notion of learning style as a set of preferences and the notion of learning style as a specific aptitude are very closely intertwined in many discussions of learning styles. Moreover, it is believed that among the general public, the notion of learning styles and the notion of differential abilities are scarcely distinguished at all” (p. 109). Definitions are a problematic area in the literature and not every researcher agrees on one definition, even though there is evidence suggesting that learning styles are connected with aspects of cognitive ability (Bialystok & Frӧhlich, 1978; Messick, 1987).  Most researchers agree that learning preferences and cognitive styles are distinct from cognitive abilities. In addition, these researchers discovered that many investigators such as, Garratt-Reed, et al. (2016), O’Leary and Quinlan, (2007), and Sinclaire, (2011) observed that many studies on student satisfaction and satisfaction with online learning fail to define satisfaction. For the purpose of this study satisfaction is defined as “The perception of enjoyment and accomplishment in the learning environment” (Sweeney & Ingram, 2001). Furthermore, Massa and Mayer (2006), referred to cognitive style based on how individuals represent information, and learning preference on how individuals prefer the presentation of information. DeTure (2004) utilized the term cognitive styles to explain how learners perceive, interact with, and respond to the learning environment. Santo (2006) indicated that, “Other researchers have used the term “learning styles” to describe what others called cognitive styles or even personality (p. 71).Audience. The target audience for this research is composed of higher education students, parents, counsellors, and all higher education personnel. The belief is that students engaged in online courses can excel if their learning styles and level of satisfaction is taken into consideration. Student satisfaction has been identified as significant cause of the quality of distance education (Belfer, 2000; Chang & Smith, 2008).  Therefore, designing online programs based on student learning style is vital. Beaudoin (2002) suggested that the degree of interaction and the participation of the students could influence the desirable outcomes in distance education courses.  Furthermore, the results of this study will add to the body of instructional design knowledge and the importance of adapting to the thinking styles competencies into online curriculums.  By doing this, the student will be more successful in this type of learning environment. Setting of the StudyThe study will be conducted at Embry-Riddle Aeronautical University (ERAU)-Worldwide Campus. ERAU is a well-known, private, nonprofit university which concentrate education and research connected to aviation and aerospace. Currently, the campus has more than 125 locations and serve more than 23,000 students around the world (Embry-Riddle Aeronautical University, 2018a). It offers educational opportunities for working civilian and military professionals. This campus started offering online bachelor's degree programs since 1993-1994. In 2017, the US News and World Report ranked it number 8 in Best Colleges for Veterans and the best in Best Online Bachelor's Programs. In addition, the worldwide campus also ranked in the top 50 for its online, non-MBA graduate business programs (Hanns, 2018).The curriculum was created to fit the student’s busy lifestyles. At Embry-Riddle Worldwide and Online, students enjoy flexibility that allows them to pursue their career goals while developing new skills and expertise in an area of interest. Each section last nine-week, except the Master of Science in Aerospace Engineering degree, which last 12 weeks.  All online courses are delivered asynchronously, through EagleVision, their virtual classroom, which combines web video conferencing and learning management system software (Embry-Riddle Aeronautical University, 2018b)The Researcher’s RoleWhen researchers conduct a study, they are responsible to follow a set of steps described in the scientific method of inquiry (Kerlinger & Lee, 2008; Leedy & Ormrod, 2001). This refers to the principles, procedures, and practices that govern empirical research (Kazdin, 2016). The following paragraphs addresses the roles of the researcher.It is the responsibility of this former student, to review and follow the ERAU and Nova Southeastern University’s IRB approved protocols.  This document was designed as a student guide to policies and procedures related to the conduct of research that are specific human subject’s research at the university.  The IRB requires that all student conducting research to review this guide before submitting to the IRB.  After studying this guide, the researcher must seek approval from IRB before conducting research involving human subjects.Even though it is impossible for any human being to be free of errors, it is important that the researcher promote accountability, trust, mutual respect and fairness among all the parties involved in a study (Sudeshna & Datt, 2016). Cozby and Bates (2015) stated, “Ethical practice is fundamental to the conceptualization, planning, execution, and evaluation of research” (p.44).  In addition, the researcher should be always objective. As Kumar (1996) explained, “objectivity is a moral concept before it is a methodological concept” (p.244). One of the roles of the researcher in qualitative research is to protect participants and the data. Before collecting the data, the researcher has an ethical duty to inform the participant of the study and seek their approval.  According to Bailey (2007) “an informed concern is the most common method to inform and seek approval from the participants” (p. 384).Purpose of the StudyThe purpose of this study is to investigate how knowledge of the student’s learning style measured by Kolb’s Learning Style Inventory 3rd edition (KLSI) influences the performance and satisfaction of students who are enrolled in an online course.   This dissertation can possibly guide educators when devising online courses to address the needs of students learning styles. The knowledge can empower students learning in making their educational choices and experiences more meaningful and fruitful.Definition of TermsAccommodator. - Combines active experimentation and concrete experience. Accommodators tend to be people-oriented and learn through trial-and-error problem solving (Raschick, Maypole, & Day 1998).Assimilator. - Combines reflective observation and abstract conceptualization. Assimilators are characterized by abstract thinking and a theoretical orientation (Raschick, Maypole, & Day, 1998).Converger. -Combines abstract conceptualization and active experimentation. Convergers tend to have a good understanding of practical ideas and their application (Raschick, Maypole, & Day, 1998).Distance learning/distance education. - Institution-based, formal education where the learning group is separated, and where interactive telecommunications systems are used to connect learners, resources, and instructors (Schlosser and Simonson 2009).Diverger. - Combines concrete experience and reflective observation. Divergers tend to use information from their senses and feelings (Raschick, Maypole, & Day, 1998).Experiential Learning Theory. - Model of the process of learning from experience. (Kolb, 1984)Grade Performance. - Grade performance is a measurement of academic success based upon students’ test and assignment scores. In this study, the accumulations of the test and assignment score results were used to calculate the final course grades. Thus, the final course grades were determined by the course instructor (KunhiMohamed, 2012).Learning. – Learning is the process whereby knowledge is created through the transformation of experience” (Kolb, 1984).Learning styles. - Individual method of students frequently used for receiving, collecting, processing, and interpreting information to become knowledgeable (Kolb, 1984; Damrongpanit, & Reungtragul, 2013). Learning Style Inventory. - Framework for examining one’s approach to learning situations, and an instrument for determining one’s learning style (Kolb, 1984).Online distance education. – For the purpose of this study, it is a course that uses the Internet to deliver all of the instructional content, with students not required to attend any face-to-face (F2F) meetings (Welsh, 2007).Online learning. - Learning structured to occur over the Internet, intranet, extranet, groupware, or other networks where the majority of instruction and learning occurs (Tompkins, 2007).Student satisfaction.  - The perception of enjoyment and accomplishment in the learning environment (Sweeney & Ingram, 2001). Chapter 2 - Literature ReviewTheoretical Framework This research embodies elements pertaining to the learning theory, and cognitive theory. The human brain contributes in every human emotion, thought and decision.  Understanding the human brain has been one of the greatest challenges for centuries.  The human brain is an enormously complex information processing system with a group of different yet firmly unified levels of organization: from genes, proteins, synapses and cells to microcircuits, brain regions, and the whole brain (Walker, 2012).  One of the most extraordinary attributes of the human brain is the ability to acquire knowledge.  Through the years, learning has been defined in various ways by different theorists, researchers, and educational practitioners; however, all of them concurred that learning processes play a central role in human development (Del Río, 2000).  Learning includes the acquisition and modification of knowledge, proficiencies, approaches, beliefs, attitudes, and behaviors (Ertmer & Newby, 2008).  Learning happens every day, which imply that a new skill or a new concept is learned in a random and unsystematically manner.  Nevertheless, Castañeda (2004) argued, “if a learning situation is carefully observed, the observer will notice that learning is not as accidental as it seems” (p.16).While a universal agreement on definition is absent, many definitions employ common elements.  Shuell (1986) acknowledged that the study of learning “emphasizes on the manner in which people acquire new knowledge and skills and the way in which existing knowledge and skills are modified” (p. 412).  Based on this belief, Shuell (1986) defined learning as, “an enduring change in behavior, or in the capacity to behave in a given fashion, which results from practice or other forms of experience” (p. 2).  Ormond (2011) concurred that, “learning is a process that brings together cognitive, emotional, and environmental influences and experiences for acquiring, enhancing, or making changes in one's knowledge, skills, values, and worldviews” (p. 5).  For purposes of this study, the researcher used Ormond (2011) definition of learning. Learning Theory. Learning theories focus on the explanations of what happens when someone learns.  Langley and Simon (1981) defined learning as, “any process that modifies a system so as to improve more or less irreversible, its subsequent performance of the same task, or of tasks drawn from the same population"(p. 367). The roots of learning theory can be traced to the early efforts of several theorists such as John Dewey, who predicted a special connection between technology and learning theory (Tennyson, 2010). Edward Thorndike has been considered the first modern educational psychologist who wanted to bring a scientific approach to the study of learning.  He alleged that learning was incremental and that people learned through a trial-and- error approach (Austin, Orcutt, & Rosso, 2001).  Robert Gagné hypothesized that the attainment of knowledge is facilitated by the hierarchical sequencing of content from elemental subordinate information to more complex skills (Tennyson, 2010).There are many labels used to describe a diversity of learning theories. However, the classification of learning theories can be organized into three main areas: behaviorism, cognitivism, and constructivism. This study will concentrate on cognitive theories.Cognitive theory of learning. There was a shift from behaviorism to cognitivism during the mid-twentieth century due to the inability of behaviorists to explain why and how individuals make sense of and process information (Yilmaz, 2011).  The works of Piaget, Vygotsky, Bruner, and the Gestalt psychologists were instrumental in producing this dramatic shift (Yilmaz, 2011).Furthermore, the mid-1950s experienced the impact of cognitive theories in education.  Many cognitive psychologists witnessed an outbreak of theoretical and empirical works on cognitive processes such as memory, attention, and information processing (Shuell, 1986).  The primary goal of this new field was to investigate how knowledge is acquired, processed, stored, retrieved, and activated by the learner during the different phases of the learning process (Anderson, Reder, & Simon, 1997).  In addition, this cognitive school explains the acquisition of knowledge as a mental activity which includes internal coding and structuring by the learner (Derry, 1996; Spiro, Feltovich, Jacobson, & Coulson, 1993), and proposes that learning only happens under conditions that are aligned with the human cognitive architecture (Sobel, 2001).  Furthermore, cognitive psychologists believe that, learning is an active, constructive, and goal-oriented process (Yilmaz, 2011).Cognitive psychologists also proposed that learning is cumulative in nature (Good & Brophy 1990; Merriam & Caffarella 1999; Simon, 2001).  Cognitive notions of learning put significant importance on the role prior knowledge plays in the acquisition of new knowledge.  Cognitive psychologists are concerned for what the learner had already acquired.  Actually, studies conducted by Bransford and Johnson (1972), and Dooling and Lachman (1971) demonstrated what the learner already knows is activated at the time of learning and the new material will make sense to the learner.Learning Styles. Learning is one of the most important activities in which humans engage.  It is at the very core of the educational process.  Learning involves acquiring and modifying knowledge, skills, strategies, beliefs, attitudes, and behaviors.  In fact, people learn cognitively, behaviorally, linguistically, and socially. Studies in learning styles grew as a result of interest in individual differences (Curry, 1983).  Researchers shared that the first learning style research was conducted in the 1970s (Williamson & Watson, 2006; Roberts, 2013).  Hunt (1985) reported that by mid 1980s, there were at least 30 learning style models and accompanying questionnaires.  Even though the popularity of learning style models and measures, an agreeable definition and suitable theory for learning style should be expected.  Unfortunately, they do not exist (De Bello, 1990; Merriam et al., 2007; Roberts, 2013).  In addition, learning styles is defined as the “individual method of students frequently used for receiving, collecting, processing, and interpreting information to become knowledgeable” (Kolb, 1984; McCarthy, 1987; Davis, 1993; Damrongpanit & Reungtragul, 2013).   Sarasin (1998) addressed the importance of considering learning style differences in higher education and e-learning when he said, “As higher education becomes more accessible, our students are more representative of the general population, which means greater diversity in learning styles” (p. 2). Griggs (1991), Milgram and Dunn (1993), Karpova (1994), and Tatarinceva (2007) concurred that no less than three-fifths of learning style is genetic, and the biological factor of individual learning style operates for an individual’s whole life.  Anastasi (1988), Dunn and Dunn (1998), Restak (1984), and Thies (1999) agreed that anything connected with the process of information processing and perception is also biologically predetermined.  However, variables such age, gender, and culture could affect the individual’s learning style (Dunn, Giannitti, Murray, Rossi, Geisert, & Quinn, 1990).  Rischin (2002) also argued that every person is born with a very distinctive and personal learning style.Educational researchers and theorists, such as Kinshuk, Liu, and Graf (2009), have agreed to the fact that “students learn in different ways and learning styles can help students in learning easily and more effectively” (p. 740).  Bloom (1976) hypothesized that fitting instruction to individual learning styles would result in increased learner performance outcomes.  Dunn and Dunn (1993) demonstrated that, “when students are taught using a preferred learning style approach, there is an increase in academic achievement and a demonstrated positive attitude toward instruction as opposed to being taught with a non-preferred approach” (p.20).It is beneficial for students to know their learning styles or preferences.  This created awareness in the student of the best way to learn.  Learning styles can also be used to inform the learner about their strengths and weaknesses.  Diaz and Cartnal (1999) stated that, “Knowledge of student learning preferences can provide a bridge to course success in a distance education mode.  The first step in using learning style information to aid instruction in a distance education setting is to first determine student learning styles” (p.130).  In addition, Kolb and Kolb (2005a) explained that students who understood their learning styles were able to identify the learning strategies they used and those that they needed to improve.Furthermore, learning style was important in laying the groundwork for understanding ‘students learning performance (Furnham, Monsen, & Ahmetoglu, 2009; Gadzella, Ginther, & Bryant, 1997; Jackson, Baguma, & Furnham, 2009; Liu, Magjuka, & Lee, 2008). Lack of attention to learning styles, particularly in distance education, was a concern to many educators and educational researchers (Little, 2008).  In fact, there was evidence that most distance learners do not have effective learning strategies (Little, 2008).  For example, Solvie and Kloek (2007) discovered that only a few students used their identified learning style as a tool in their studies.  Researchers found that, for the doctoral students studied, learning styles did not appear to have an effect on student learning (Malcom, 2009).  However, researchers noted that students in graduate programs typically have higher levels of intrinsic motivation which limits how the results might be generalized to other populations (Akkoyunlu, & Soylu, 2008). Terrel (2005) stated that attrition rates in online learning for "graduate students were not affected by learning style variables under consideration; this may not hold true for learners at other levels" (p. 7).Most of today’s literature on learning styles focus on improving student’s outcomes (Biggs, 1979) and assessing students learning processes (Papilia,1978). Actually, the findings of studies conducted by Visser, McChlery, and Vreken, (2006), Ghada, Rima, Nahla, Nola, and Mona (2011) and Zhou (2011) demonstrated that a learner’s achievement in any class was influenced by factors such as the native ability, and the level of similarity between the students’ learning style and the teachers’ teaching style.  Similar learning styles and teaching styles had a positive impact on the students’ outcome.  On the other hand, different styles had a negative impact on the students’ outcome and this resulted in many problems in learning (Ford & Chen, 2001; Naimie et al., 2010).Critiques of Learning Styles. Learning styles has become an attractive topic for the last 30 years.  Throughout the 1970's and 80's extensive work was done on student learning styles in higher education by a large number of researchers (Markham, 2004).  In their empirical work, Curry (1983) and Riding and Cheema (1991) discovered that learning style, and cognitive style are utilized in the same context with similar and different measurement tools. In addition, Cassidy (2004) observed that; “there exist almost as many definitions as there are theorists in this area” (p. 440). Concurring with Cassidy (2004), Scott (2010) pointed out that there is a “considerable conceptual confusion” in the definition of learning style between researchers (p. 6). Appendix A shows the difference between the definitions. There are numerous websites on the World Wide Web that advertise learning styles inventories. In fact, Scott (2010) conducted an internet search and it produced 2,160,000 hits.  Examining several of the sites showed that some sites contained positive discussion of learning styles which belong to schools, technical and training institutes and universities. Additionally, Lilienfeld, Lynn, Ruscio, and Beyerstein, B. (2010) searching the ERIC database, discovered 1,984 journal articles (3,604 entries), 919 conference presentations, and 701 books or book chapters. This researcher conducted an internet search using Google search engine. Table 1 shows the results. Table 1. Results of learning styles search.	Learning styles and educational outcomes. Another area of controversy among researchers was the belief that learning styles improve students’ outcomes. The thought of learning styles in the educational institutions has gained credibility as an idea that affects not only individual student's learning satisfaction, but their overall success in school (Terry, 2002). The numerous articles written by scholars such as Ballinger and Ballinger (1982), Cavanaugh (1981), Dunn (1981), Fiske (1981), Hodges (1983), Jenkins (1982), and Pizzo (1982) reported the extreme success with learning styles and outcomes. Sheard, and Lynch, (2003) claimed that, “accommodating learner difference will ultimately enhance students’ learning experiences and learning outcomes” (p. 244). They also maintained that; “individual’s preferred learning style is a significant factor in how learning is experienced and it also has an impact on learning outcomes when learners encounter new or complex problems or are presented with new information” (p. 250). Smits et al., (2004) stated that, “Learning styles has gradually become accepted as a possible predictor of learning outcomes” (p.38).  Research done by Canfield (1992), DeBello, (1990), and Dunn and Dunn (1987) indicated that the students are more successful when they are instructed through approaches that match their learning style. By contrast, not all research on learning styles validated the findings that relate learning styles and outcome. There are numerous research studies that revealed no significant differences in success between learning styles and type of instruction (Ehrhard, 2000; McNeal & Dwyer, 1999; Wang, Hinn & Kanfer, 2001). Riener and Willingham (2010) insisted that there is no credible evidence that learning styles exist.  Norman (2009) concluded that, “While learning styles instruction has very broad appeal, it has very little to do with learning” (p. 3). Mayer (2011) claimed that learning styles research has continually lacked rigor. Others, like Allcock and Hulme (2010), and Pham (2012) have voiced concerns that, “In addition to a lack of credible empirical evidence, learning styles research has not been grounded in credible psychological concepts (p.77).  Klein (2003) stated that; “Theoretical basis for learning styles use lacks clarity and the absence of substantial empirical research” (p. 49).Stereotype. Reynolds (1997) pointed out that one of the problems with the use of styles was that “they encouraged a positivistic and individualistic perspective on learning” (p.116). He insinuated that this action; “Could encourage labelling and stereotyping …” (p.116). Concurring with Reynolds (1997), Sadler-Smith (2001) agreed that the term “learning style” had been used to describe a diversity of individual differences. Coffield et al. (2004) and Herzberg (2006) explained that the attributes of learning styles suggest that students are one type of learner or another. Additionally, Gutiérrez and Rogoff (2003) observed that learning styles have become a way to limit students and classify them on what they can or cannot do and how they are supposed to do it. This might lead to positive and negative opinions by the teachers’, parents’ and students’ expectations (Rubie-Davies, 2008). For instance, Coffield (2012) told a story of a teacher who wrote to him saying that; “When visiting a local school, pupils had labels on their desks indicating their learning styles as in ‘I’m a kinesthetic learner’, ‘I’m an active experimenter’ and so on” (p.222).Reliability & Validity. Investigations of the reliability and validity of learning styles have been extensive. However, researchers have questioned the validity and reliability of numerous learning styles inventories. For instance, Coffield et al. (2004) investigated the reliability and validity of 13 most popular learning styles instruments and concluded that these models have issues with the reliability and validity of existing instruments. Hattie (2009) conducted a meta-analysis of 800 studies on learning styles and discovered that;” Many were characterized by conceptual confusion (e.g. frequently conflating learning styles with learning strategies) and significant measurement and methodological flaws” (p.15). Sewall (1986) evaluated several popular instruments and learned that “many instruments have limited validity. They were difficult to interpret, and their reliability estimates are unstable” (p.58).In spite of researchers’ concerns about the validity and the reliability of the instruments, there were commercial industry that were successfully promoting a particular inventory and instruments. Pashler et al. (2009) indicated that “Most commercial activity related to learning styles is largely centered on the publishing and selling of measurement devices to help teachers assess individual learning styles” (p.106). Foundations of Experiential LearningLewis and Williams (1994) explained that, “In its simplest form, experiential learning means learning from experience or learning by doing. Experiential education first immerses learners in an experience and then encourages reflection about the experience to develop new skills, new attitudes, or new ways of thinking” (p.5).  Warren, Hof, McGriff, and Morris (2012) defined experiential learning as; “A philosophy and methodology in which educators purposefully engage with learners in direct experience and focused reflection in order to increase knowledge, develop skills, and clarify value” (p.275). Kolb (1984) defined experiential learning as “the process whereby knowledge is created through the transformation of experience. Knowledge results from the combination of grasping and transforming experience” (p.41).The theory of experiential learning began in the seventeen century as an effort to change the traditional formal education of the day (Lewis & Williams, 1994). Experiential learning has its origins on the work of scholars such as, John Locke, John Dewey, Paulo Freire, William James, Carl Jung, Kurt Lewin, Jean Piaget, Carl Rogers, Lev Vygotsky, and Mary Parker Follett (see table 2). For instance, in the seventeen century, Locke, known as the "Father of Liberalism”, expressed the idea that the foundation of learning was experience, not rational thought (Emch, 1990). His views on the importance of learning through experience served as a fundamental role in the history of learning theory.Table 2. Underpinnings of Experiential LearningBy the twentieth century, James (1912) explained that “Everything begins and ends in the continuous flux and flow of experience” (p. 159). Dewey, was probably one of the most prominent American philosophers of the early twentieth century and contemporary of James who examined the relationship between experience and learning in the publication of his well-known book Experience and Education (1938) (Experiential Education, 2017).  Dewey believed that “The starting point of curriculum was the experience of the child and, indeed, the goal of education was the growth of experience of the individual” (Cook, 2007). A well-known Russian psychologist, Vygotsky (1896-1934) believed that; “Learning from experience is the process whereby human development occurs” (Kolb, 2015, p. xi). He claimed that; “Experience has a biosocial orientation; it is what lies between the person and the environment that defines the relationship of the person to the environment” (Elkind, 2004, p. 496). Jean Piaget (1896-1980) considered one of the most influential researchers and theorists in developmental psychology agreed with Vygotsky.  According to Piaget; “learning occurs through equilibration of the dialectic processes of assimilating new experiences into existing concepts and accommodating existing concepts to new experience” (Passarelli & Kolb, 2012, p. 142).  The focal point of Jung’s book, Psychological Types or the Psychology of Individuation, was the significance and importance of experience as the key premise of learning (Hickcox, 1991). Drummond (2003) contends that the meaning of, and the support for experiential learning began with Rogers. According to him, Rogers’s books, Freedom to Learn and Client-Centered Therapy, cemented the way for experiential learning. Lewin, founder of American social psychology, placed great emphasis on subjective experience and personal involvement. (Kolb, 1984). Kolb’s Experiential Learning Theory. There are numerous publications on learning styles written by many scholars, however, Coffield’s report was the most significant report in the area of learning styles (Coffield, et al., 2004).  Commissioned through the University of London by the Learning and Skills Council, this report included a comprehensive evaluation of learning style theories found in the literature.  In their study, the researchers evaluated thirteen main theories about learning styles, selected the most important studies from the literature, assessed the theoretical strength of each model, looked at the empirical constructs such as reliability and validity of each theory, and evaluated the implications of these models for learning (May & May, 2012).  Only five of these theories were identified as meeting these requirements.  Coffield et al. (2004) concluded that Kolb’s Experiential Learning Theory was one of the six recommended models in education and training.  The report indicated that the model is especially of use because it throws light on group dynamics and encourages awareness and understanding of self and others.Kolb is credited with introducing the modern learning styles movement, by developing one of the most prominent models of learning styles in the early 1970. His model, Experiential Learning Theory (ELT) and Learning Style Inventory (LSI), have produced an extensive body of research.  In fact, a study conducted by Kolb and Kolb from 1971-2016, revealed that at least 1004 research studies have utilized either the ELT or LSI since 1971 (Kolb, & Kolb, 1971-2016) (see Appendix B). In their meta-analysis, Desmedt and Valcke (2004) discovered that Kolb was the most cited author in the learning style literature since 1972. Additionally, a meta-analysis study conducted by Dağ and Geçerb (2009) revealed that Kolb’s learning style model has been preferred by researcher. Table 3 shows the distribution of the research works according to learning style models.Table 3.Distribution of the research works according to learning style models.Note. Table content adapted from “Relations between online learning and learning styles” by Dağ and Geçerb, 2009, Procedia-Social and Behavioral Sciences, 1 p. 867.Kolb’s theory consists of three main components; (1) the theory of experiential learning; (2) the learning cycle model; and (3) the learning style inventory. All these will be discussed in this section. Kolb (1984) has acknowledged the intellectual origins of his theory of experiential learning and of the Learning Style Inventory. His theory is based on studies in psychology, philosophy and physiology. Additionally, Coffield, et al. (2004) shared that Kolb is recognized for creating the first methodical and thorough explanation of the theory of experiential learning. Even though his theory is rooted in the research of other intellectuals, “his own contribution in detailing the characteristics of experiential learning, the structural foundations of the learning process, and in creating the LSI to assess individual learning styles deserves to be regarded as original and significant” (p. 65).  Kolb (2015) contended that experimental learning has the following characteristics.Learning is best conceived as a process, not in terms of outcomes (p.37).  Learning is a continuous process grounded in experience (p.38).  The process of learning requires the resolution of conflicts between dialectically opposed modes of adaptation to the world (p.40).Learning is a holistic process of adaptation to the world (p.43).Learning involves transactions between the person and the environment (p.45).Learning is the process of creating knowledge (p.48).Kolb’s Learning Cycle. Kolb and Kolb (2008) described experiential learning theory as a four-part process, where the learners were asked to engage themselves in a new experience, actively reflect on that experience, conceptualize that experience and integrate it with past experiences.  In the beginning, Kolb called these four-part model “the Lewinian Experiential Learning Model”. The model has four elements: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Lewin points out that reflection should be encouraged hoping that this will help to personal meaning making. Later, the Lewinian Learning Cycle as shown in figure 1, was reviewed by Kolb and renamed “Experiential Learning Cycle”.Figure 1. Lewinian Learning Cycle. Adapted from “The Process of Experiential Learning. In Experiential Learning: Experience as the Source of Learning and Development” by Kolb, 2015p. 32. Copyright 2005 by Pearson Education. Reprinted with permission  Piaget was another scholar who shaped the theory of experiential learning. Piaget studied the nature of intelligence and how it is influenced by experience. Piaget’s work concentrated on the four-major phases of cognitive development, the sensory-motor stage (age birth to 2 years); preoperational stage (ages 2 to 4); Concrete operations (ages 7 to 11); Formal operations (beginning at ages 11 to 15) as depicted in the following figure 2. Figure 2. Piaget Model of learning and cognitive development. Adapted from “The Process of Experiential Learning. In Experiential Learning: Experience as the Source of Learning and Development” by Kolb, 2015 p. 36. Copyright 2005 by Pearson Education. Reprinted with permissionKolb and Kolb (2008) combined the Piaget’s and Lewin’s themes (concrete experiences, abstract concepts, feedback/reflection and application) and developed his experiential learning model (see figure 3).  Healey and Jenkins (2000) explained that; “The foundation of Kolb's four-stage model is, according to him, “A simple description of the learning cycle which shows how experience is translated through reflection into concepts, which in turn are used as guides for active experimentation and the choice of new experiences” (p.186). Kolb’s (2008) model, seen in figure 3 and table 4, described these four stages as concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). A learner can enter at any point in the cycle; however, the stages should be followed in sequence (Healey & Jenkins, 2000). To explain the four learning stages, Lu et al. (2003) offered the following description:“Concrete experience centers around responsibility and individuals associated with this type of learning style tend toward peer orientation, benefitting from peer discussion; Reflective Observation style is centered around ‘watching’, and tends toward observation in learning. Abstract Conceptualization involves ‘thinking’ and individuals exhibiting this style oriented to symbols, learning optimally through more impersonal learning environments; and finally, Active Experimentation, characterized by sensing and feeling with a tendency toward active participation, learning best through experimentation and engaging in projects” (p. 192).  Kolb (1984) explained that “Experience grasped through apprehension and transformed through intention results in what is called divergent knowledge. Experience grasped through comprehension and transformed through intention results in assimilative knowledge. When experience is grasped through comprehension and transformed through extension, the result is convergent knowledge. And finally, when experience is grasped by apprehension and transformed by extension, accommodative knowledge is the result” (p. 42).Kolb’s Learning Cycle and Biology.  Zull (2002) noticed a connection between Kolb’s learning cycle and the brain (see figure 4). This implied that the process of experiential learning is related to the process of brain functioning. According to him “…concrete experiences come through the sensory cortex, reflective observation involves the integrative cortex at the back, creating new abstract concepts occurs in the frontal integrative cortex, and active testing involves the motor brain. In other words, the learning cycle arises from the structure of the brain” (p. 18).Table 4. Kolb’s learning styles and characteristics of learners.Note: Table content adapted from “Experiential learning” by Sharlanova, 2004, Trakia Journal of Sciences, 2, p. 37.  Zull (2002) declared, “Learning is about biology” (p. xiii) and everything we learn is related to the physical and biological brain structure. He added force to Kolb’s theory by combining the neurobiological evidence with the theory. The following table shows the relationship between the brain and Kolb‘s Theory.Figure 4. The Experiential Learning Cycle and Regions of the Cerebral Cortex. Adapted from “The art of changing the brain: Enriching teaching by exploring the biology of learning” by Zull, 2002. p. 18. Copyright 2002 by Stylus Publishing. Reprinted with permission.Table 5. Summary of the 4 major parts of the human brain, their associated functions, and their relation to the 4 stages of Kolb’s Learning Cycle.Note. Table content adapted from “The art of changing the brain: Enriching teaching by exploring the biology of learning” by Zull, 2002, p.18. Copyright 2002 by Stylus Publishing. Reprinted with permission.Kolb’s learning cycle critics. According to Michelson (1996), Gill and Johnson (1991), and Mifsud (1990), experiential learning was one of the focal areas for today’s research and practice in adult education. It was also increasingly one of the most problematic areas. For instance, most of the researchers had evaluated the theory itself without assessing the graphical model in modeling terms. Bergsteiner, Avery, and Neumann (2010) argued that Kolb’s model is defective. In their opinion, the model did not differentiate credibility and reliability between concrete/active/primary and abstract/passive/ secondary learning processes. It also did not differentiate correctly between the terms learning activities and learning typologies.  Some practical learning concepts were not established or were not taken into account.Additionally, Vince (1998) believed that Kolb's learning cycle was created as a “reaction against research, education, and development processes in the past that emphasized some fairly rigid and directive attempts to generate and impart knowledge” (p. 307). Mughal and Zafar (2011) coincided with Vince (1998) affirming that; “Kolb’s learning cycle lacks the inclusion of ’here and now’ the ’immediate experience’ of a learner and focuses on the past events. The perception of a learner (as a whole) incorporated the immediate experience ’living in the moment’ triggering the event and past experiences focuses on the past events” (p. 31). Looking back to characteristic two, Kolb (2005a) stated that, “Learning is a continuous process grounded in experience” (p. 38). However, Vince (1998) argued that Kolb assumed that people are always open to experience. Additionally, Kolb did not take into consideration that some individuals protected themselves against experiences such as fears, anxieties, and doubts which are very common emotional experiences in processes of learning (Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998), which could result as a barrier and cause hindrance in learning and gaining knowledge (Britzman, 1998). In other words, Kolb’s learning cycle failed to address the emotional experience of a learner from the start of the cycle. This has led to two directions; (1) the learner holds anxiety which could promote learning (Britzman, 1998; Vince 1998) or (2) get discouraged in the learning (Vince, 1998; Mughal & Zafar, 2011). Kolb’s Learning Style Inventory (LSI). Kolb created a self-descripted, self-report, and self-scoring inventory called Learning Style Inventory (LSI) to measure individuals’ learning styles.  It was designed to help learners to identify the way they learn from experience, as well as, to construct validation to Kolb’s theory. As stated by Kayes (2005), “Kolb’s Learning Style Inventory (LSI) remained one of the most influential and widely distributed instruments used to measure individual learning preference" (p.249). In addition, it has been translated into several languages such as, Arabic, Chinese, French, Japanese, Italian, Portuguese, Spanish, Swedish, and Thai, (Yamazaki, 2004).According to Zijdemans-boudreau, Moss, and Lee (2013), the instrument was first utilized as a curriculum development project in 1969 at MIT.  Since then, the instrument has been revised several times to improve its psychometric properties (Kayes, 2005).The LSI-3, which was revised in 1999, consists of 12 sentence stems followed by four possible sentence endings (see Appendix C). Kayes (2005) explained the process by saying that “The participant ranks each of the four sentence endings based on his/ her preference for using the four modes, concrete experience, reflective observation, abstract conceptualization, and active experimentation. For example, “when I learn, (1) I like to deal with my feelings; (2) I like to think about ideas; (3) I like to be doing things; and (4) I like to watch and listen”.  The result was a 48-item survey. Then, total scale score was charted for each of the four modes. Finally, a combined score for each of the two bi-polar dimensions is computed, resulting in two dimensional scores that measure relative preference for the CE–AC mode and the AE–RO dimensions” (p. 251). A more detailed description of the LSI 3.2 and an analysis of its psychometric properties would be presented in the following chapter.Research on Kolb’s instrument recognized four statistically predominant learning styles, each of which are associated with a different method of learning (Kolb & Kolb, 2009). It included (see figure 3);Learners with diverging learning style (see table 6) emphasize in concrete experience (feeling) and reflective observation (watching). They are interested in people, tend to be imaginative and sensitive (Greenberg, 2009). They are proficient at observing concrete situation from various viewpoints (Liu & Ginther, 1999). They prefer to work in groups and obtaining personal feedback (Kolb, Boyatzis, & Mainemelis, 2000). Learners with assimilative learning style (see table 6) have reflective observation (watching) and abstract conceptualization (thinking) as their dominant modes. They are exceptional at inductive reasoning and assimilating information (Kolb, Boyatzis, & Mainemelis, 2000).  They are more interested in ideas and abstract concepts than people (Liu & Ginther, 1999).  They prefer activities such as readings, lectures, exploring analytical models (Kolb, Boyatzis, & Mainemelis, 2000).  Learners with converging learning style (see table 6) have abstract conceptualization (thinking) and active experimentation (doing) dominant learning abilities. They like to solve problems and to find solutions to practical problems (Liu & Ginther, 1999). They prefer to deal with technical tasks than social issues.  They like to experiment with new ideas, to simulate, and to work with practical applications. They are moderately unemotional (Liu & Ginther, 1999).Learners in the accommodative group (see table 6) give emphasis to concrete experience (feeling) and active experimentation (doing). They prefer learning by active participation and trust more on their intuition than the logic (Liu & Ginther, 1999). They depend more on people for information than on their own technical analysis. They favor to work with others to get assignments (Kolb, Boyatzis, & Mainemelis, 2000).   They are risk-takers and react well under pressure (Liu & Ginther, 1999).Table 6. Four types of learning styles and its characteristicsNote. Table content adapted from “Kolb's Learning Styles “by Jonassen & Grabowski. 2012, Handbook of Individual Differences Learning and Instruction, p. 250. Copyrighted 1993 by Routledge. Distance EducationSeveral years ago, distance education was considered a “non-traditional” form of education (Choudhary, 2006). However, today, distance learning has become an important mode of education. In fact, Simonson, Schlosser, and Orellana (2011) shared that “it has become the most significant change to the process of teaching and learning of the last decade” (p. 124).Since its beginning, the goal of distance learning has been to offer learning opportunities to those communities that have been excluded from formal learning systems such as people who live in remote geographic regions (Burns, 2011).  Bušelić (2012) explicated that distance learning is “a field of education that focuses on teaching methods and technology with the aim of delivering teaching, often on an individual basis, to students who are not physically present in a traditional educational setting such as a classroom” (p.24). Distance education, as defined by UNESCO (2002) is “any educational process in which all or most of the teaching is conducted by someone removed in space and/or time from the learner, with the effect that all or most of the communication between teachers and learners is through an artificial medium, either electronic or print...”  (p. 22). Unfortunately, the term “distance education” has not been well defined, which has created a great confusion between researchers and scholars in the field (Holmberg, 2003; Keegan, 2000). For example, Moore (1973) defined distance education as; “A family of instructional methods in which the teaching behaviors are executed apart from the learning behaviors, including those that in a contiguous situation would be performed in the learner's presence, so that communication between the teacher and the learner must be facilitated by print, electronic, mechanical, or other devices” (p. 664).  Moore and Kearsley (1996) stated that distance education is “a planned learning that normally occurs in a different place from teaching and as a result requires special techniques of course design, special instructional techniques, special methods of communication by electronic and other technology, as well as special organizational and administrative arrangements” (p. 2).  Appendix D shows the difference between the definitions.Another term that has produced a great debate is distance learning.  A research of “distance learning” included articles on correspondence courses, blended or hybrid courses, low-residency courses, and fully online courses. However, this researcher was able to discover two articles that discussed the difference between the two terms. According to Roblyer and Edwards (2000), the United States Distance Learning Association defined distance learning in 1998 as; “The acquisition of knowledge and skills through mediated information and instruction, encompassing all technologies and other forms of learning at a distance” (p. 192). Newby, Stepich, Lehman and Russell (2000) defined distance learning as "an organized instructional program in which teacher and learners are physically separated” (p. 210). Askov, Johnston, Petty, and Young (2003) explained that; “’distance education’ takes the view of the educator and educational institution providing the learning opportunity, whereas ‘distance learning’ takes the view of the student and refers to learners studying in a non‐classroom setting” (p.2).In addition, the research revealed several terminologies (e.g. e-learning, Internet learning, distributed learning, networked learning, tele-learning, virtual learning, computer-assisted learning, web-based learning, and distance learning) referring to online learning.  Nichols (2003) defined e-learning as, “The use of various technological tools that are either Web-based, Web-distributed or Web capable for the purposes of education” (p.2). However, Nyoni (2014) defined it as “The delivery of a learning, training or education program by electronic means” (p.154) (see Appendix E). Even though, there was not a clear, common, and precise definition for those terms, Ally (2008) affirmed that in an online learning environment, the learner uses technology (e.g. a computer) to access the learning material, to communicate and interact with the instructor and other learners who are at a distance. According to Simonson et al. (2011), distance education has these four characteristics:Distance education was by definition carried out through institutions; it was not self-study or a nonacademic learning environment (p.126). Geographic separation was inherent in distance learning, and time might also separate students and teachers (p.126).Interactive telecommunications (e.g. e-mail) connected the learning group with each other and with the teacher (p.126).Distance education, like any education, established a learning group, and sometimes called a learning community, which was composed of students, a teacher, and instructional resources (p.126).Keegan (1996) suggested that the phrase distance education is a broad term, which describes several methods of education where the learner and the teacher are separated by space and time. Even though, these terms are used interchangeable (Phipps & Merisotis, 1999), for the purpose of this study, online education will be defined as “institution based, formal education where the learning group is separated, and where interactive telecommunications systems are used to connect learners, resources and instructors” (Simonson et al., 2011, p. 126).Student Satisfaction Student satisfaction has been considered one of the most important goals in colleges and universities. It influences student’s decision to enroll in online courses (Arbaugh, 2000). DeLone and McLean (1992), and Davis (2014) argued that student satisfaction is one of the most important reasons in successful online learning operation. Student satisfaction is key to supporting learner’s success. For this reason, some institutions are recognizing the importance of student satisfaction as a desired outcome.Student satisfaction in online courses is also important to colleges and universities, because it has been linked to student attainment (Battalio, 2007; Chang & Smith, 2008; Palmer & Holt, 2009) and student retention (Elliot & Shin, 2002; Goho & Blackman, 2009; Thomas & Galambos, 2004). In fact, Palmer and Holt (2009) reported that, “Learner satisfaction has been shown to be positively correlated with quality of learning outcomes” (p. 101), as well as an evaluation of the online educational programs, courses (Grimes, 2002) and the effectiveness of a program (Walker, 2005; Levy, 2004). Numerous studies have been conducted to measure the student satisfaction at university and college level (Baturay, 2011; Bradford, 2011; Bradford & Wyatt, 2010; Bequiri, Chase, & Bishka, 2009; Bishop-Clark, Dietz-Uhler, & Fisher, 2006-2007; Burnett, Bonnici, Miksa, & Kim, 2007; Mayzer & DeJong, 2003). However, studies on student satisfaction had mixed outcomes depending on the obstacles faced in the class (Banerjee, 2011).  For this reason, Richardson, (2005), Kirtley (2002), Allen and Seaman (2006), Moskal, Dziuban, Upchurch, Hartman, and Truman (2006), Ertmer, Sadaf, and Ertmer (2011), and Kuo (2010) recognized the need for further research to understand online learning and student satisfaction. Looking at satisfaction and mode of delivery, a study conducted by Ocker and Yaverbaum (1999) concluded that face-to-face groups experience a higher satisfaction level than distance education students. Trying to understand student satisfaction, Poirier and Feldman (2004) compared online and face to face student. The results indicated that online students had greater level of satisfaction than face to face students. However, Navarro and Shoemaker (2000), and Hiltz and Goldman (2005) found no difference. In another study, Faul, Frey, and Barber (2004) discovered that students enrolled in a web-based online course had a “significantly higher level of overall satisfaction” than those enrolled in a F2F course (p. 105). However, Lim, Kim, Chen, and Ryder (2008) did not find any significant difference between the online learning and traditional learning students. Researchers have identified important factors that influence online learning. These factors have been recognized as potential influence to the satisfaction level and learning outcomes of distance learners (Butt & Rehman, 2010). Endres, Chowdhury, Frye, and Hurtubis, (2009) affirmed that student satisfaction in online learning is influenced by “all aspects of the educational experience” (p. 31). Moore (1989) identified three types of interaction in distance education that affect satisfaction. They are, student-content, student-instructor, and student-student.According to Muirhead and Juwah (2004) interaction is defined as “a dialogue or discourse or event between two or more participants and objects which occurs synchronously and/or asynchronously mediated by response or feedback and interfaced by technology” (p. 13). Interaction in distance education encompasses “the learner’s engagement with the course content, other learners, the instructor, and the technological medium used in the course. Thurmond and Wombach (2004) explained that ultimately, the goal of interaction was to increase understanding of the course content or mastery of the defined goals” (p. 4).Juwah (2006) alleged that interaction and interactivity are the vital success factors supporting the pedagogy of online education. However, these two terms are often confusing. For instance, Muirhead and Juwah (2004) argued that “interactivity and interaction in online education are complex, multifaceted phenomenon and are critical in promoting effective learning (p.13).  They also clarified that “interactivity in distance and online education describes the form, function and impact of interactions in teaching and learning” (p.14).Current research on online learning has verified the assumption that interaction is important for a successful course. However, many concerns still exist regarding the type and degree of the interaction and its effects on student performance (Picciano, 2002). Roblyer and Wiencke (2003) indicated that “interaction has become to be considered an essential condition for successful distance courses” (p. 77). According to Ertmer, et al. (2011) “interaction appears to have a primary influence on satisfaction” (p. 305).  Ni and Aust (2008) concurred with the previous statement. In their study, they found that interaction in online courses can influence student satisfaction.  Arbaugh (2005) defined student-content interaction as “pedagogical tools and assignments, including PowerPoint presentations, streaming audio and video presentations, group projects, individual projects, and embedded links in Web courses” (p. 538).  Student-content interactions usually happen when students “complete course readings, engage with multimedia materials (e.g., simulations, software), or finish course assignments, participation in course-related online discussions” (Ertmer, Sadaf, & Ertmer, 2011, p. 158). It is where the learners interact (Ekwunife-Orakwue & Teng, 2014).  In addition, Moore (1997) did not expand on the interaction between the learner and the course content.  However, the researcher indicated that the interaction between student and course content is based on the “philosophy of course designer(s), subject-matter of the course, and content” (p.27). Unfortunately, Student-content interaction has not been studied enough. However, recently particular interest is growing in the area of student-content interaction. Most of these studies involved; “Self-report student surveys, many of which were satisfaction surveys” (Lee & Choi 2011, p. 609).Marks, Sibley, and Arbaugh, (2005) examined significance of student-content interaction as predictors of perceived learning and satisfaction with an online course. These researchers discovered that this type of interaction was significantly associated with perceived learning and student’s satisfaction.   According to Moore and Kearsley, (1996) learner-instructor interaction is “the human interaction that involves two-way communication between the learner and the course instructor, including the communication of expectations, feedback to students, clarifications, and minimizing the impact of distance” (p. 133).Equally, Picciano (2002) indicated that several researchers have corroborated the importance of student-instructor interactions in online courses. Actually, Kirtley (2002) considered the lack of interaction between student-instructor can have an adverse effect on student satisfaction levels. The literature review showed that the interaction between student-instructor is one of the greatest predictors of student satisfaction in online environment (Arbaugh, 2000; Croxton, 2014; Swan, 2001). Dennen, Darabi, and Smith (2007) affirmed that “instructors believe that learner performance is more likely tied to instructor actions than course content. Learner satisfaction is more likely tied to learners' feeling than their interpersonal communication needs” (p. 65). A plethora of research has revealed that student-instructor interaction can cause positive outcomes on both student achievement and satisfaction (Chen, Wang, Hung, 2009; Drouin, 2008; Hughes, McLeod, Brown, Maeda, & Choi, 2007; Jackson, Jones, & Rodriguez, 2010; Kupczynski, Brown, & Davis, 2008; LaPointe & Gunawardena, 2004; Lim, et al., 2008; Rhode, 2009; Schwartzman, 2007; Sher, 2009).Shea, Fredericksen, Pickett, Pelz, and Swan (2001) conducted a survey of 3,800 students enrolled in 264 courses through the SUNY Learning Network (SLN). The researchers concluded that the relationship of satisfaction, interaction, and performance (grades) was that “students who reported the highest levels of learning and satisfaction also reported the highest levels and quality of interaction with the instructor and with other students” (p.12). Furthermore, Dziuban and Moskal (2001) indicated a considerable high correlation between student-instructor interaction in online courses and student satisfaction. Another predictor of student satisfaction included prompt feedback from instructors. Goldsmith (2014) defined feedback as “Information that is provided to a student from a teacher based on a formative assessment performed by observing and diagnosing student activity" (p.33).  Chickering and Gamson, (1987), Hatziapostolou and Paraskakis, (2010), Palomba and Banta, (1999), and Simonson, Smaldino, Albright, and Zvacek, (2012), indicated that prompt feedback is critical for learning process improvement. By providing feedback, the course instructor permits students to feel a connection to the online community (Featro, 2012).Espasa, and Meneses (2010) studied 186 online graduate students in their last week of courses. The results of their study indicated a significant relationship between instructor feedback received and learning as measured by student satisfaction and final grades.Learner-learner interaction has been recognized as one of two elements that most influence student satisfaction (Endres et al., 2009). Learner-learner interaction involves the human interaction in which learners communicate with other learners enrolled in the same course, typically through asynchronous discussion boards, synchronous course meetings, collaboration through Wikis or blogs, and/or through email (Moore & Kearsley, 2005). Rabe-Hemp, Woollen and Humiston (2009), and Renninger and Hidi (2002) agreed that student interaction with other students is important in all learning experiences.  Swan (2002) reported “a significant positive correlation between student interaction with classmates and satisfaction in online courses (p. 27). Drouin (2008) affirmed that student-student interaction may promote student satisfaction in online environments.  In a study conducted by Hollenbeck, Mason, and Song (2011) observed that participants pointed out that they relied on student–student affinity because it reduced the perceived threat of poor performance in a course. This finding showed that student–student interaction plays an important role in online learner satisfaction. According to Wu and Hiltz (2004), “over half of the students from three online courses reported that they learned a great deal from their peers through online discussions with 78% of the students valuing online discussion as a chance to share opinions among peers and instructors” (p. 144).Discussion boards are an approach used by most online teachers to promote community and student-to-student interaction. Laurillard, (2002) maintained that discussion between students-to-students is critical to peer interaction and learning. It also increases student performance (Moore, 1989; Murphy & Ciszewska-Carr, 2007). Ertmer, et al. (2011) explained that online discussions “can replace or extend in-class dialogue, providing opportunities for students to interact with each other over course-related topics” (p. 158).Online discussions were created to engage learners in meaningful conversation and to promote critical thinking associated to course content. McLoughlin and Mynard, (2009) discovered that discussion does not conduct to higher levels of thinking. Actually, Garrison, Anderson, and Archer (2001) and Gilbert and Dabbagh (2005) observed that more than 80% of students’ discussion posts exhibited lower levels of thinking. However, Capdeferro and Romero (2012) found that countless college students experienced frustration when they were required to interact with other students. In fact, students identified student-student interaction through discussion boards and group projects “as a waste of time and not productive in their learning” (Bacino-Thiessen, 2015, p.117).Learning Styles and Student SuccessDue to the explosion of online education, researchers have looked at the effect of learning styles on online environment. Santo (2006) stated that “proponents of learning styles claim that learning styles influence a student's ability to participate successfully in an online course” (p. 74). Chen and Shaw (2006) argued that “learning style may be an important factor to consider in the online learning environment” (p. 98). Diaz and Cartnal (1999) stated that, “Student success in distance learning classes may ultimately depend on understanding the learning style preferences of the students who enroll” (p.134). Furnham, et al. (2009), and Jackson, et al. (2009) concurred with the idea that learning styles is crucial in laying the background for understanding performance.The subject of learning styles has taken on a greater significance in modern education.  Learning styles are important for a number of reasons.  One of them is that in today’s distance-education environment, each student comes to class with undeniable learning experiences, expectations, and needs that must be addressed, for which instructors, and instructional designers need to be sensitive, to maximize the students' learning experiences.  However, because of the unknown composition of online classes, the learning preferences of online students may be unclear, making it difficult to develop effective online courses (Mupinga, et al., 2006, p. 185).  Studies comparing learning styles in an online learning class and those of students in an equivalent F2F class have revealed that students in F2F class were much more dependent learners while the online students were more intrinsically motivated (Lyke & Michael, 2012).  On the other hand, students in the online courses who possessed a more independent and conceptual learning style had the highest student achievement.  Students with social and applied learning styles performed better in the F2F environment (Diaz & Cartnal, 1999). Buerk, Malmstrom, and Peppers (2003) found that traditional students were inclined to have the assimilator learning style (student are more interested in ideas and abstract concepts) while online students had the convergent learning style (preference in technical tasks, and are less concerned with people and interpersonal aspects).  In another study, Manochehri and Young (2006) discovered a significantly difference between F2F environment and online environment. The results showed that students with assimilator and converger learning styles have higher knowledge with online learning format. However, students with diverger and accommodator learning styles have higher knowledge on F2F environment. Zacharis (2010) examined learning styles in both an online and F2F course instruction for the same subject. He found differences in the types of learning styles of the students taking the F2F instructional formats. The majority of students in the online course scored as assimilator (31%) and accommodator (27%), while in the F2F course the majority of students scored as assimilator (31%) and diverger (28%) learning styles.  However, MacGregor (2000) found that online students were more serious and more accommodators than traditional classroom students. Bozionelos (1997) discovered that online students did better on learning tasks than their counterparts with different learning styles.  Garland (2003) also found significant differences in learning styles between online and traditional students.  Nevertheless, Ching (1998) and Clariana (1997) found that students’ learning styles changed when students were exposed to online courses within several weeks.  Jordanov (2001) and Muir (2001) noticed that students preferred different learning styles when enroll in an online course than when learning in F2F environment.  She concluded that “students moved toward more active learning styles when using the Internet, and active learning styles had a positive impact on students’ attitudes toward and performance on computer-based learning tasks” (p.20).Gender. For a long time, gender difference has been recognized as an important topic in education (Chang, Liu, Sung, Lin, Chen, and Cheng, 2014).  However, variables such age, gender, and culture could affect the individual’s learning style (Dunn, Giannitti, Murray, Rossi, Geisert, & Quinn, 1990).While there is a lack of significant consensus on the role gender plays in determining learning preferences, gender is among a number of factors that has been found to influence student learning styles (Honigsfeld, 2001).  For instance, Philbin, Meier, Huffman, and Boverie, (1995) were unable to come to any definitive conclusion about how gender differences affect learning styles.  However, they admitted that there is significant evidence in learning styles between men and women.  According to these researchers, “existing data suggest that usually, men and women score contrarily on the learning style inventory.  Women tend to score higher on the Concrete Experience orientation while men tend toward Abstract Conceptualization.  No consistent differences between men and women have been identified on the active/reflective dimension” (p. 485).Bruner (1990) advised that “the danger of ignoring gender in the discussion on distance learning is the likelihood that policy makers will neglect to take into consideration that (1) there are alternative points of view, and (2) the best course of action is generally based on some kind of consensus of or collaboration among people with a variety of perspectives” (p. 30).Furthermore, according to Wells, Seifert, Eifert, Padgett, Park, and Umbach (2011) females have significantly done better than males in educational performance.  Gibb, Fergusson, and Horwood (2008) have concluded that in the traditional school setting, females tend to perform better than males from grade school through college graduation. Additionally, females also perform better on standardized tests (Marcus, 2015). However, IQ tests have shown no significant differences in intelligence (Gibb et al., 2008). Burge (1998) argued that many female learners need, “Psychological safety that cannot be satisfied with online chats and required self-postings (p. 35).  According to him, gender differences, especially for distance learners, need to be recognized.  In addition, the American Association of University Women (2000) discovered that, “nontraditional students, who were women, have perceived that the virtual classroom reduced the discomfort and alienation women have often experienced on the college campus populated by students 18 to 22 years of age” (p. 25). Looking at gender and online courses, studies conducted by Coldwell, Craig, Paterson, and Mustard (2008) have demonstrated that females are more likely to participate in online courses than males.  Gunn, French, McLeod, McSporran, & Conole, (2002) examined student participation in online activities and found that male participated less than female on discussion board.  According to Coldwell, et al. (2008), female students spent on average, 24.47 hours on online courses compared with an average of 20.61 hours for males.  Alstete and Beutell (2004) claimed that gender is related to student’s performance and that women usually outperform men in online classes.  For example, a study conducted by Bishop-Clark et al. (2006-2007) inferred that female college geography students performed significantly better than males in both Web-Based Learning and the traditional classroom.   However, Logan and Thomas (2002) did not find any differences in the learning styles between male and female students.  In term of satisfaction, Yousef (2012) stated that “there is limited research concentrating in gender differences in relation to satisfaction in online courses (p.78).  However, there are few studies that found that gender is a predictor of satisfaction (Tschetter, 2014). For instance, a study conducted by Noel-Levitz (2009) revealed that female students are more satisficed than males with online learning.  On the other hand, other studies conducted by Kim and Moore, (2005), Shehab (2007), Guripnar,  Zayim, Ozenci, and Alimolu, (2009), and Aliweh (2011) arrived to the conclusion that there is not significant differences between gender and  satisfaction with online learning.Performance.  Picciano (2002) stated that student performance can be measured by a number of indicators such as successful completion of a course, course withdrawals, grades, added knowledge, and skill building.  Even though there is insufficient evidence on the factor which influences student outcomes in an online learning environment, a few researchers have discussed the issue.  For example, in their meta-analysis study Means, Toyama, Murphy, Bakia, and Jones, (2010) reported that, “Students in online conditions performed modestly better, on average, than those learning the same material through traditional face-to-face instruction” (p. xiv). Poirier and Feldman (2004) concurred with Means et al. (2010). In their study they found similar results of performance between face-to-face and online learning. Similarly, studies have demonstrated that students enrolled in online classes have performed equivalent to or better than students in the face-to-face class (Braun, 2008; Hrastinski, 2008; Hughes, 2007; Lim, Kim, Chen, and Ryder, 2008; Wang & Morgan, 2008)Likewise, Beaudoin (2002) discovered that highly participatory students achieved higher outcomes. Studies conducted by Lu, and Sperling (2003), and Shih, Ingebritsen, Pleasants, Flickinger, and Brown (1998) concluded that neither online participation nor learning style had a significant effect on learning performance. Alstete and Beutell (2004) concluded that class discussion was the higher indicator of student performance in online classes.  However, Holley (2002) contended that university students who participated in an online course using techniques such as virtual lectures and discussion boards, attained better outcome.A careful examination of the literature has indicated that researchers applied different methods to measure satisfaction and performance. This might be responsible for the inconsistencies among empirical studies in the research literature (Davis, 2014). A study conducted by Driscoll, Jicha, Hunt, Tichavsky, and  Thompson, (2012) observed that “ students may report higher satisfaction with courses that they perceive as easy, fun, or less demanding, none of which is necessarily linked to successful learning”  (p. 315). Driscoll et al. (2012) also concluded that there is a nonexistence relationship between performance and satisfaction. Simon and Yatrakis (2002) concurred with the previous statement, stating that there is a lack of relationship between performance and satisfaction. Some researchers have tried to establish relationship between students’ characteristics and previous online learning experience, and their satisfaction and performance in online learning settings (Marks et al., 2005; Sanders & Morrison-Shetlar, 2001; Thurmond, Wambach, Connors, and Frey (2002). However, they found that these variables cannot consistently predict students’ performance and satisfaction with their online learning experiences.On the other hand, Eom, Wen, and Ashill (2006) affirmed that learner satisfaction is a significant predictor of learning outcomes. This demonstrate that researchers have provided conflicting evidence regarding the relationship between satisfaction and performance in online learning environment. Grades. For centuries, educational researchers have discussed the criteria used in assigning grades (KunhiMohamed, 2012). Colleges and universities use GPA to assess student performance and academic achievement. Students use their grades to follow their performance throughout their degree programs.  Dalziel (1998) stated, that we can distinguish outstanding students from low achievers by their grades. Linn and Miller (2005) stated that “in the final analysis, letter grades should reflect the extent to which students have achieved the learning outcomes specified in the course objectives” (p. 377). Randall and Engelhard (2010) reported that grades, when assigned appropriately (a) enable teachers to compare the knowledge and skills of current students, (b) allow teachers to ascertain accurately the preparedness/readiness of incoming students, and (c) provide parents and students with a clear picture of each child’s knowledge and understanding of course content. (p. 1376).In the last decades, researchers have focused on the topic of learning outcomes in online education. One of the most often-cited research which has impacted the online modality was authored by Russell (1999). Russell (1999) chronicled over 355 studies from 1928 to 1998. These studies examined the effect of technology on education and distance education. This researcher concluded that no significant difference exists between learning outcomes and modality of delivery. Following Russell (1999) studies, other researchers commenced to study the academic impact of the online modality. However, the results of these studies have been mixed and varied. For instance, a longitudinal study conducted by Olmsted (2009), compared distance and face-to-face learner’s performance based on the National Board of Dental Hygiene Examination (NBDHE).  The analysis of student performance between face-to-face and distance learners over a ten-year period revealed no statistically significant differences between student performance on the NBDHE, GPA, or course grades. Leasure, Davis, and Thievon (2000) performed a study comparing student outcomes using test scores and course grades. These researchers concluded that there were no statistically significant differences on any of the examinations or final course grade. Maring, Costello, and Plack (2008) compared outcomes of students enrolled in a pathophysiology course.  Half of the course content was delivered in traditional classroom instruction, whereas the other half of the content was given via online education modality. To determine the level of student learning, the researchers used multiple choice exams. The researchers determined that online student performed better than traditional classroom students.  Buckley (2003) compared the learning outcomes of two undergraduate nursing nutrition courses. One course was delivered via online, the other was F2F. The researcher assessed the learning outcomes by using midterm scores, final exam scores, and course grades. Based on learning outcomes, Buckley (2003), did not find significant differences between the two delivery modalities.  In online Information Technology (IT) studies, Smeaton and Keough (1999), Piccoli, Ahmad, and Ives (2001), Jones, Moeeni, and Ruby (2005), and Lam (2009) did not find significant differences in academic outcomes. However, Bryan, Campbell, and Kerr (2003) concluded that the online modality actually provided better academic outcomes than traditional learning. In their meta-research, Tallent-Runnels et al. (2006) stated that they “did find that most studies comparing online and traditional classes concluded that both methods of delivery were adequate. In some studies, students in the online classes out-performed students in the traditional classes, and vice versa” (p. 109).Hargrove, Wheatland, Ding, and Brown (2008) measured the relationship between learning styles and students’ GPA based on their major in the School of Engineering using Kolb’s learning style instrument. The students were enrolled in the Civil, Electrical, or Industrial Engineering programs. The results showed that students were 14.22% accommodators 44.40% assimilators, 23.71% were identified as convergers, and 17.67% divergers.When Hargrove et al., (2008) compared students’ performance based on GPA, students with the accommodator learning style preference out performed students in the diverger category, and assimilators also out performed divergers. Convergers performed higher than divergers and also assimilators; however, the difference was not significant. When looking at the students’ overall GPA, the convergers had the highest GPA, and the lowest were the divergers (Hargrove et al., 2008). Grades allow students to track their performance throughout their degree programs.In their quantitative causal-comparative study, Schultz, Schultz, and Sieland (2010) compared the online grades of males and females in one graduate management class and one undergraduate management class at the University of West Florida. The results showed that; (1) there were not significant differences between the online grades of males and females; (2) no significant difference between the grades of males in online classes and males in traditional classes; and (3) no significant difference between the grades of females in online classes and females in traditional classes.   SummaryIn this chapter, a variety of topics were presented which was intended as a foundation to this research study. This researcher was interested in the relationship between learning styles, satisfaction and online learning. The aim of this study is to measure the learning styles, performance and satisfaction of undergraduate students, and the relationship with online education.For this reason, this chapter was divided into four different sections. In the first section, this researcher presented a brief discussion on learning and learning theories, followed by a discussion on cognitive theories of learning and learning styles. However, after reviewing the literature, it is unclear that the learning styles of students have an impact on the course delivery method.The literature presented in the next section, elaborated on topics such as experiential learning, Kolb’s experiential learning and Kolb’s learning style inventory. In addition, the researcher established the reason, Kolb’s learning style inventory has been selected as the best instrument for this study. In the following section, this researcher examined student satisfaction. Examining past studies, this researcher has discovered conflicting results in terms of the relationship between students’ satisfaction and gender, mode of delivery, learning style and performance. These conflicting results pose a need for additional research in this area. In the last section, the researcher explored the relationship between online learning and student satisfaction in connection to learning styles, performance, gender and grades.  There was a significant quantity of literature available on student outcomes in regards to online education. This literature supported the idea that there are little or no significant differences in outcomes regardless of the delivery modality. Therefore, in this quantitative study, the researcher will examine the relationship of learning styles, performance and satisfaction for students enrolled in online courses.Research QuestionsThe research questions for this study are:Which is preferred learning style, as measured by the Kolb Learning Style Inventory, of students enrolled in an online based course?Which individual learning style (diverger, converger, assimilator, or accommodator) best predicts performance in students enrolled in an online based course?What is the relationship of preferred learning style, as measured by the Kolb Learning Style Inventory, and the level of student satisfaction?Chapter 3: MethodologyParticipantsThis study would be conducted at Embry-Riddle Aeronautical University’s distance learning program during the academic year of 2017-2018. The participants for this study will be part-time online undergraduate students enrolled in a nine week online (MATH 111 College Mathematics for Aviation I) course (see Appendix F). It is expected that the participants would be culturally diverse.  It is likely that most of the population for this project would be considered non-traditional students. According to Kirtley (2002), non-traditional students are profiled as being goal oriented, highly motivated and over 26 years of age. They are working adults (Roblyer, 1997) with family obligations which make attending a traditional campus-based class difficult (Matthews, 1999; Thompson, 1998). The sampling would not be randomly selected, since the learners would choose the method of delivery. In addition, the participation in this study would be completely voluntary; therefore, it is expected to be small. This researcher is anticipating the recruitment of one hundred participants for the study.  InstrumentationThis researcher will utilize two different Likert-type scale instruments in this study. The first instrument that the participant will take is the Kolb’s Learning Style inventory 3.1 (see Appendix C). Towards the end of the semester, all participants will complete the Strachota’s Student Satisfaction Survey (see Appendix O). The Learning Style Inventory. As Cano-Garcia and Hughes (2000) declared, “Kolb’s test for measuring learning preferences, the Learning Styles Inventory (LSI) has been the most influential, and widely documented” (p. 415).  This instrument has been tested in a variety of professional learning setting such as medicine, business, management and education. The LSI 3.1 is grounded on the Experiential Learning Theory, which views learning through the experiences involved in the learning process (Kolb, 1984). This survey was designed with the purpose to assist individuals to identify the way they learn from experience (Jong, Wierstra, & Hermanussen, 2006; Kolb & Kolb, 2005b).  This self-assessment instrument, consists of 12 forced-choice items that measure the degree to which individuals demonstrate their different learning styles. The task of the participant is to rank order each of the four sentences, start with a “4” for the sentence that best describes how the participant learn, down to a “1” for the ending that seems the least like the way the participant would learn. Additionally, the most significant information obtained by the LSI relates to intra-individual differences (Kolb & Kolb, 2005b).The inventory assesses four ipsative variables. These are (1) abstract conceptualization (AC), (2) concrete experience (CE), (3) active experimentation (AE), and (4) reflective observation (RO). Skinner (2007) explained that, “Accommodator relying more on AE and CE: Assimilator, relying more on RO and AC: Converger, relying more on AE and AC: and Diverger, relying more on CE and RO” (p.93). The two combination scores which are not ipsative variables, measure the individual’s preference for abstractness over concreteness (AC-CE) and action over reflection (AE-RO) are plotted on an X Y coordinate graph.  These variables are illustrated in Appendix N. Each quadrant of the graph identifies one of the four learning style preferences. As Kolb and Kolb (2005b) explained, none of the quadrants are better or worse than the others, “nor is the scale placement (higher or lower, more to the right or to the left) within a particular quadrant better or worse than other placements” (Skinner, 2007).  The location in the scale and in the quadrants, is the measurement of the preferences for the learning orientations of an individual.The LSI 3.1 was introduced in 2005 however, only a few studies have used the revised version of the survey (Kolb & Kolb, 2005b). This version incorporated norms based on a larger, more diverse and representative sample (online, research university freshmen/MBA students, liberal arts college students, art college undergraduates, and distance e-learning adult undergraduates) of 6977 LSI users (Kolb & Kolb, 2005b).  The determined cut scores for placement for the four quadrants are as follows: “The cut point for the AC-CE scale is +7, and the cut point for the AE-RO score is +6. The Accommodating type would be defined by an AC-CE raw score ≤ 7 and an AE-RO score ≥ 7, the Diverging type by AC-CE ≤  7 and AE-RO ≤  6, the Converging type by AC-CE ≥ 8 and AE-RO ≥  7, and the Assimilating type by AC-CE ≥  8 and AE-RO ≤  6” (Kolb & Kolb, 2005, p. 14).Validity of the LSI. Creswell (2005) defined validity as; “the development of sound evidence to demonstrate that the test interpretation (of scores about the concept or construct that the test is assumed to measure) matches its proposed use” (p. 159).  Messick (1994) identified validity as; "an integrated evaluative judgment of the degree to which empirical evidence and theoretical rationales support the adequacy and appropriateness of interpretations and actions based on test scores or other modes of assessment” (p.6).  The validity of the LSI 3.1, as measured by a variety of correlational and factor analysis studies, has been established by a variety of investigators (e.g. Black, 2008; Elliott, 2006, Ferrara, 2009; Hudak & Anderson, 1990).Simelane-Mnisi and Mji (2015) verified the validity of the instrument by using the Pearson correlation coefficient of the different subscales of the LSI 3.1 instead of the factor analysis. As regards to the correlation coefficients, Simelane-Mnisi and Mji (2015) indicated that, “the theory of experiential learning proposes that the four primary modes of the learning cycle-CE, RO, AC and AE-are composed of two independent dialectic (bipolar) dimensions: a grasping dimension measured by the combination score AC–CE, and a transformation dimension measured by the AE–RO combination score”  (p.314). To establish validity in their study, Simelane-Mnisi and Mji (2015) determined that the following conditions should be satisfied to be acceptable. These conditions are as follow: AC–CE and AE–RO should be uncorrelated.The CE and AC scales should not correlate with AE–RO.The AE and RO scales should not correlate with AC–CE.The dialectic poles of both combination dimensions should be negatively correlated, though not perfectly, since the dialectic relationship predicts the possibility of developmental integration of the opposite poles.The cross-dimensional scales—CE/ RO, AC/AE, CE/AE and AC/RO—should not be correlated as highly as the within dimension scales.Using the Pearson correlation coefficient to establish the validity of the instrument, Simelane-Mnisi and Mji (2015) concluded that the instrument; “Was consistent with what is described in Kolb learning styles’ theory” (p.318). Internal Validity.  Correlation studies and factor analysis have been performed to test the internal validity of the instrument (Kolb & Kolb, 2005b).  The following table reports the inter-correlation matrix, and alpha coefficients for studies conducted by Kayes (2005) and Kolb and Kolb, (2005b).   Table 7. KLSI 3.1 Scores for Normative Groups.Construct Validity. In the attempt to evaluate the construct validity of the instrument, Sims, Veres, and Shake (1989) conducted a study utilizing the LSI 2 and the Learning Styles Questionnaire. The total population in both instruments were 317 undergraduate business students. The researchers discovered that three of the four scales on each dimension were statistically significant, with correlation coefficients ranging from .11 to .28. There were not statistically significant relationships between the pragmatist scales on the LSQ and the active experimentation scale on the LSI 2.Kayes (2005) conducted a study to examine the construct validity of this instrument. He administered the LSI-3 to 221 participants. Using the two factor analysis of Eigenvalue, he discovered a 70% variance. Kayes (2005) concluded that this version of the LSI does provide satisfactory and meaningful validity. This conclusion is consistent with Yahya (1998). In addition, Wen-Bin and Chao-Chin (2006) also investigated the construct validity of the LSI. They utilized the confirmatory factor analysis (CFA) to test it.  The results fluctuated between 0.34 to 1.11, demonstrating that the construct validity was significant at p = 0.05.Kayes (2005) used the discriminant construct validity to test construct validity of the instrument. Kayes (2005) stated, “If the correlations within each scale are greater than correlations between scales, then minimum evidence of construct validity is achieved. Between scale item correlations ranged from .18 to .48 for the 4 dimensional scores, (in contrast to within inter scale correlations of .76–.82) suggesting empirically distinct constructs” (p. 254). Through this study, Kayes (2005) established the construct validity of the LSI.  Cozby and Bates (2015) defined face validity as, “The degree to which a measurement device appears to accurately measure a variable” (p. 107). To create an instrument with face validity, Kolb team up four behavioral scientists familiar with experiential learning theory. At first, twelve set of words were created for pre-testing. The initial examination exposed random responses on three of the twelve set and were thus eliminated, resulting in a final version of the LSI 1 with nine items. Then, the remained nine set of words were polished through item-whole correlation analysis to incorporate six score item for each learning mode (Kolb & Kolb, 2005b). According to Emch (1990), “Only those words with correlations above .45 were used; most correlations ranged from .50 to .60” (p. 35). In addition, Kirby (1979) spent two-year studying various learning-style models and instrumentation at Ohio State University’s National Center for Research in Vocational. She arrived to the conclusion that, “The LSI had established “impressive face validity” (p. 72).External Validity. Hernandez, (2011) explained that; “The external validity is based on whether inferences can be made to the general population” (p. 66). Shaughnessy, Zechmeister, and Zechmeister, (2012) stated that external validity; “Examine the extent to which the study’s findings may be used accurately to describe people, settings, and conditions beyond those used in the study” (p. 94). The external validity of Kolb’s instrument contains research on gender, GPA, and academic performance (Kolb and Kolb, 2005b).Many researchers have investigated the relationship between learning style, and academic performance. For instance Lynch, Woelfl, Steele, and Hanssen (1998) used the LSI-2 to investigate the relation between learning style and academic performance in medical school. The total population for the study were 252 third-year medical students. Of the 252 third-year students participating in the study, 102 (45%) are classified as convergers, 59 (26%) as assimilators, 48 (21%) as accommodators, and 18 (8%) as divergers. To evaluate the academic performance of the population, the researchers used the result of the United States Medical Licensing Examination (USMLE 1), the National Board of Medical Examiners (NBME) subject examination in surgery, and NBME computer-based surgical case simulations (CBX). Table 8 reveals that convergers and assimilators perform better on the objective USMLE 1 and NBME.Table 8. The data suggest that students who describe themselves as highly abstract using the LSI have a distinct advantage when performance is measured by multiple-choice type instruments Exanimating the results depicted in table 9, Lynch, et al. (1998) inferred that, “students who describe themselves as highly abstract conceptualization using the LSI have a distinct advantage when performance is measured by multiple-choice type instruments” (p. 64).Table 9. A comparison of performance measures for students with strong (greater than one standard deviation above the mean) and weak (greater than one standard deviation below the mean) preferences for abstract conceptualization.Tsingos, Bosnic-Anticevich, and Smith (2015) explored the relationships between reflective and non-reflective learning styles, and academic performance of pharmacy students. To accomplish this, they invited 249 first year undergraduate pharmacy participants to be part of the study. All the participants took the Kolb’s Learning Style Inventory 3.1.  The study indicated a significant difference between participants who preferred reflective observation (m= 64.07) as a method to process information compared to those participants who did not (m= 61.46). Participants who choose the reflective observation attained higher academic results compared to those who did not. As shown in table 10, the study found that the greater preference amongst the pharmacy students was the assimilator learning style.Table 10. Distribution of learning styles in a pharmacy undergraduate cohort at an Australian University.Hargrove, Duowen Cordelia, Wheatland, Ding, and Brown, (2008) examined the relationship between students ‘learning styles, major, gender and academic performance in engineering at Morgan State University.  Two hundred and thirty-two freshman’s students majoring in Civil, Electrical, and Industrial Engineering enrolled in a freshman class, Introduction to Engineering took the LSI. The outcomes of the study revealed that 14.22% of the students had an accommodator preference; 44.40% of students had an assimilator preference; 23.71% students had a converger preference; and 17.67 % of students had a diverger preference. Looking at the data (see table 11), Hargrove, et al., (2008) concluded that; “There were more students with an Assimilator preference than any other learning style” (p.41). The purpose of Manochehri’s (2008) study was to compare the result of e-learning versus traditional learning based on Kolb learning style. The population for the study were undergraduate students at a major university in the United States who were enrolled in a subject course that was offered face-to face and online. The result of this study pointed out that assimilators and convergers did better in the web-based environment. In contrast, accommodators and divergers showed higher results in the face to face format.The goal of Stiefel (2012) study was to investigate if there were any relationship between undergraduate students’ learning style(s) and three learning environments, hybrid, online, and traditional.  A total of the 123 participants took the LSI 3.1. Using the chi square analysis, the researcher discovered that when examining the combination of learning style and hybrid environment, accommodator mode score higher than the other learning modes.  Looking at the data for learning styles and online environment, only the accommodator learning mode was reported. Table 12 also shows that traditional learners preferred the accommodators and divergers learning modes. Table 12. Relationship between Learning Style and Learning Environment.Kolb and Kolb (2005b) indicated significant differences between men and women. For instance, Fifty-nine percent of the women incline themselves concrete, while this number was 41% for men. Fifty-nine percent of the men lean toward abstract conceptualizing, while 41% of the women favored abstract conceptualizing. Stiefel (2012) study also found differences between males and females. For instance, comparing genders, fifty-two female score higher in the accommodator and diverging mode (52) than males (15). However, males score higher in converging than females. In spite of this, the cross-tabulation analysis in gender and learning styles did not display a significant result. As indicated in table 13, Hargrove, et al., (2008) reported that female students, (GPA = 2.7305), performed better academically than male students, (GPA = 2.5892).  Looking at the learning styles modes, the authors discovered that female students with an accommodator mode showed a higher GPA, than the other three. On the other hand, male students with a converger mode presented a higher GPA, than the other three.When comparing the GPAs learning styles between males and females, the researchers learned that the GPA for female students with the accommodator and assimilator preferences score higher than students with converger preferences. In contrast, male students with a GPA for accommodator and assimilator preferences score lower than students with converger preferences (Hargrove, et al., 2008b). Table 13.Kolb’s Learning Style Distributions and GPA in MSU’s School of Engineering.Note: Adapted from “The Effect of Individual Learning Styles on Student GPA in Engineering Education at Morgan State University” by Hargrove, et al., 2008, Journal of STEM Education, 9, p. 43.Reliability of the LSI. According to Creswell (2012); “Reliability is generally easier to understand as it is a measure of consistency. If scores are not reliable, they are not valid. Additionally, the more reliable the scores from an instrument, the more valid the scores may be” (p. 159).Numerous studies have confirmed the reliability of the Kolb LSI 3.1(see table 14). For instance, in a study, conducted by Kayes (2005), 221 liberal arts college students that took the online LSI, confirmed strong internal consistency as calculated by Cronbach’s alpha coefficients, with values reaching from .81 for CE, to .78 for RO, to .83 for AC, to .84 for AE. In another study, Weirstra and DeJong (2002) gave the same instrument to a total of 101 undergraduate psychology students. The results were similar, .81 for CE, .78 for RO, .83 for AC, and .84 for AE. Once again, Ruble and Stout (1994) showed comparable results, .72 for CE, .75 for RO, .72 for AC, and .73 for AE.Table 14. Cronbach’s alpha coefficients of different studies using LSI 3.1.    Note: Note: Adapted from “The Kolb Learning Style Inventory—Version 3.1 2005 Technical Specifications” by Kolb & Kolb, 2005b, p. 15. Jamieson (2010), described test-retest reliability as; “the process by which instruments and their results are measured over time to determine an instruments’ reliability”. For instance, in the Learning Style Instrument’s 3.1 Technical Specifications manual, Kolb mentioned studies conducted by Kayes (2005), and Wierstra and Dejong (2002) to discuss the internal reliability of the instrument when compared with the earlier versions analyzed by Veres et al. (1991) and Ruble and Stout (1994) (Kolb & Kolb, 2005b). According to Kolb (1984), test-retest reliability should be significant when it fluctuate between .71 to .85.  According to him, this means that, “The instrument is consistent with the underlying premise that learning modes are interdependent, context contingent, and variable” (p. 229).Veres et al. (1991) used the Kappa coefficients, the results for the LSI 2, were; “For the initial sample (N = 711) the mean coefficients for CE were .82 for Time 1 and for Time 2; the  mean coefficients for RO were .85, mean coefficients for AC were .83, and mean coefficients for AE were (.84)” (p. 148). This indicates that the instrument is reliable. Veres et al. (1991) conducted another study comparing the revised version of the LSI (2.1). Using the same statistical method, they discovered, “that the mean coefficients ranged from .92 to .97 in the initial sample and from .97 to .99 in the replication sample” (p.148). Furthermore, Simelane-Mnisi and Mji (2015) conducted a study using Cronbach’s alpha to compute the reliability of the LSI 3.1.  Their study revealed that alpha values obtained in their study were comparable to those reported by Kayes (2005); Kolb and Kolb, (2005b), and Ruble and Stout (1994). Therefore, they concluded that the instrument is reliable Online Satisfaction Survey. This research will use the Student Satisfaction Survey created by Strachota (2006) to measure students’ levels of satisfaction.  She designed it with two purposes in mind; (1) to identify the level of satisfaction in distance learning courses for college level students in the online environment; and (2) to be utilized in the educational setting to measure student satisfaction as an online program evaluation outcome. This survey addressed the four types of interactions postulated by Moore and Kearsley (1996, 2005); (1) learner-content interaction, (2) learner-instructor interaction, (3) learner-learner interaction, and (4) learner-technology interaction which is associated to Hanna, Glowacki-Dudka, and Conceicao-Runlee, (2000) and Palloff & Pratt (2001) and the last section of the instrument is correlated to the students' general satisfaction with their online course experience (Strachota, 2006).The survey (see Appendix O) comprised of 35 questions, seven questions are linked to learner-learner interaction satisfaction, nine questions measure learner-technology interaction satisfaction, and six questions are associated to general satisfaction (Strachota, 2006). The survey was created with a four-point Likert-type scale of (1) strongly disagree, (2) disagree, (3) agree, and (4) strongly agree. The survey can be completed in 30 minutes and it was written at the 12-grade level.Validity. To measure the validity of the instrument, Strachota (2003) employed a panel of subject matter experts and measurement experts in corresponding fields to review the five sections and their questions.  Then, a pilot study was performed to test the instrument (n = 249) for construct validity. Factor analysis was utilized to find the validity of the instruments in three construct, learner-content interaction, learner-instructor interaction and learner-learner interaction. The outcomes of the analysis showed that for learner-content interaction ranged from 0.604 to 0.780; for learner-instructor interaction ranged from 0.594 to 0.841; and for learner-learner interaction ranged from 0.588 to 0.786. These results shows that the instrument had a good internal or construct validity.However, Schmidt, Strachota, and Conceição (2006) stated that; “Items specific to learner-technology and general satisfaction were not pilot tested by the researchers as they had been piloted tested by Cassidy and Eachus (2000) and showed construct validity from 0.45-0.61 for learner-technology and 0.50-65 for general satisfaction constructs” (p. 1418). Reliability. In addition, after examining data from the pilot study, Strachota (2003) found that the instrument’s Cronbach’s alpha for learner-content interaction and general satisfaction was .90. The results for learner-instructor and learner-learner interaction was .89 (see table 14).  Strachota (2003) inferred that the instrument is; “Highly reliable and can be used at any institution of higher learning that offers online courses” (p. 67). Table 14.Shows the Cronbach’s alpha coefficients of different studies using the Student Satisfaction Survey.ProceduresDesign. This study will be conducted using a quasi-experimental methods design to examine and have a clearer knowledge of the research topic. According to Shadish, Cook, and Campbell, (2002) “A quasi-experiment is an experiment in which units are not assigned to conditions random” (p.12). This study is investigating whether the knowledge of participants learning style influences performance and satisfaction. Therefore, two groups will be assembled; group A will consist of an experimental group and group B will be the control group. These two groups of participants will not be randomly assigned to one group or the other. This study will include two different online sections of MATH 111 College Mathematics for Aviation I at Embry-Riddle Aeronautical University. The study will begin as soon as the researcher receive the approval from the Institutional Review Board (IRB) from both institutions (Nova Southeastern University and Embry-Riddle Aeronautical University).All students enrolled in the MATH 111 College Mathematics for Aviation I online course at this university will be invited to participate in the study a few weeks before the semester starts. A personal e-mail (see Appendix L) will be sent by the instructor to all students in his/her class. This e-mail will include: a brief description of the study, a student identification number and a link to the consent form. The student identification number will start with 1000.  This identification code will not be related to the students’ social security or student number. This identifier will be known only by the instructor.  In addition, this e-mail will explain that participation will be voluntary, the students could opt out at any time, and that the results of the study will not have any impact on their grades. To minimize nonresponse rate, the instructor will send to all participants a personal e-mail message one week later, again requesting their participation. This methodology is encouraged by Schaefer & Dillman (1998) who argued that response rate increases through multiple contacts.The consent form will be attached to Canvas, ERAU’S learning management system. Following the acceptance of the informed concern form, group A will be immediately directed to the learning style inventory which will be linked to the students’ page. After finishing the LSI instrument, the answers will be saved and he/she will receive his/her profile (see Appendix N).  Then, the participant will be directed to the course.  Group B will not see the LSI instrument and will be redirected to their course.If for any reason the participant declines to participate in the study, his/her answer will be placed in the “no desire to participate” list, and the participant will be immediately directed to his/her course. The course will last nine weeks in an online setting that consists of weekly assignments, activity, and assessment. The instruction will integrate all online technologies (e.g. online discussions, power point, written assignments, and email) while focusing on objectives that are set for the course. During the last week of the course, an announcement will be placed in the participants’ page.  A link to the Student Satisfaction Survey will direct all participants to the survey (see Appendix O).  The survey will be given to each student who participated in the study. Each participant will need their identification code to access it. If the participant does not have it, he/she can ask the instructor for it. This survey will be created in Canvas. A week later, the instructor will provide the researcher the final grade corresponding to the participants’ identification number.   Data collection procedures. Creswell (2005) addressed the importance of collecting data by stating that; “collecting data means identifying and selecting individuals for a study, obtaining their permission to study them, and gathering information by asking people questions or observing their behaviors” (p.9).The types of data collection methods used in this study will comprise the use of two survey instruments. Data for students’ performance will be collected by the instructor. The purpose of the surveys is to gather the necessary data on students’ learning styles and satisfaction. The instructor in this study will ensure that the students’ final grades be submitted to the researcher within a reasonable timeframe, which is ten days after the end of the course.   As soon as the participant agrees to participate in the study, the researcher will begin collecting the data.  In order to understand the student performance and satisfaction on distance education environment, a three-phase data collection procedure will be used.In the first phase, the researcher will collect the data from Kolb’s Learning Style Inventory v3.1 electronic version. It will take approximately ten minutes to complete. The inventory will be electronically scored and the results will be sent to the participant. A copy of the result will be also sent to the researcher. The results of all participants’ answers will be stored in a secure password protected online account. Only the researcher will have access to this information.  In the second phase, the researcher will collect the data from the satisfaction survey and the demographic questionnaire.  Learning analytics (LA) in Canvas will collect and will analyze student’s survey. In the final phase, the researcher will collect the student’s performing data. The instructor will provide this information to the researcher.Data analysis procedures. The purpose of this study is to analyze the relationship between the independent variables (learning styles) and the dependent variable, (performance and satisfaction).  As in many quasi-experimental studies, the population for this study will be assembled through their class sections. Ideally, the two groups (experimental and control) should be as comparable as possible. However, since this is quasi-experiments, no attempts will be made to balance the group’s characteristics, or to reduce the differences present between the groups. After collecting the data, this researcher will create four different worksheets in Microsoft Excel to store the data. The first worksheet will be labeled learning style. It is here where all the data collected from the leaning style survey will be stored. The next one will be performance. Satisfaction will follow, and lastly the demographic data set. Afterwards, all the data collected will be labeled by assigning a numeric value to each response. For instance, the accommodator will be equal to zero; assimilator will be equal to 1; converger will be equal to 2; and diverger will be assigned the number 3.In order to facilitate analysis of the data, the researcher will select a computer software rather than analyze data by hand. Therefore, once the data had been collected and coded, the data will be entered to SPSS version 18 to analyze it. The researcher will analyze the data in different stages. The first stage will consist of analyzing the descriptive statistics for each dependent and independent variables.  This will help the researcher to determine overall trends and the distribution of the data (Creswell, 2012).  For this type of analysis, the data is examined with the intention of describing what occurred in the sample. This will include the mean, standard deviation and variance of all the variables and demographics.  The researcher will collect the basic demographic data (e.g. age, gender, GPA, etc.) on participants.To answer the first research question, which is preferred learning styles, as measured by the Kolb Learning Style Inventory, of students enrolled in an online based course?,  the researcher will utilized  the result of the Learning Style Instrument. In addition, the researcher will conduct a descriptive statistics analysis. This study will appropriately report the preferred learning styles among the participants.Research question two, which individual learning style (diverger, converger, assimilator, or accommodator) best predicts performance in students enrolled in an online based course?, examines the relationship between learning styles (IV) and performance (DV). Therefore, this question will be answered by comparing the mean of the satisfaction scores and the means of the performance scores.  This means will be investigated by conducting a multiple regression analysis to analyze the independent variable (learning styles) and the dependent variable (performance). This statistical method will allow the researcher to study relationships between the independent and dependent (Creswell, 2012).Research question three, what is the relationship of preferred learning style, as measured by the Kolb Learning Style Inventory, and the level of student satisfaction?, examines the relationship between learning styles (IV) and satisfaction (DV). Consequently, this question will be answered by comparing the mean of the independent variable outcomes (learning styles) and the means of the dependent variable outcomes (satisfaction) performance scores.  The multiple regression analysis will be appropriated to analyze the independent variable (learning styles) and the dependent variable (satisfaction). This statistical method will allow the researcher to identify the variable responsible (diverger, converger, assimilator or accommodator style) for the variation in satisfaction.NOTE: RESULTS AND CONCLUSION WILL BE INCLUDED IN FINAL SUBMISSIONReferencesAdams, J. W. 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In The art of changing the brain: Enriching teaching by exploring the biology of learning (pp. 13-30). Sterling, VA: Stylus Pub.Appendix ADefinition of learning stylesAppendix B Brief List of Empirical Literature Using Experiential Learning Cycle (ELC) and Learning Style Inventory (LSI)Appendix CSample of Kolb’s Learning InventoryInstructions:Below there are 12 sentences with a choice of four endings. Rank the endings for each sentence according to how well you think each one fits with how you would go about learning something. Try to recall some recent situation where you had to learn something new; perhaps in your job.Using the spaces provided, rank order each sentence ending, start with a “4” for the sentence that best describes how you learn, down to a “1” for the sentence ending that seems the least like the way you would learn. BE sure to rank all the endings for each sentence unit. No two ending in a set can be given the same ranking. 4= MOST describe you1= LEAST describe you In raking the words, use your first impression. There are no right or wrong answer. The real you best revealed through a first impression. Analyzing each group will obscure the real you. Do not answer as you WISH you were or as you think you SHOULD be, just answer as you honestly think you are. Continue to rank all twelve sentences, one at the time.The ranking should take no more than 5 minutes.							  		Appendix DDistance Education DefinitionsAppendix EDefinitions of Online Learning/E-LearningAppendix FSample SyllabusCollege Mathematics for Aviation I MATH 111
Eagle Vision Classroom-Blended Learning
Course SyllabusCredit Hours: 3 CreditsAcademic Term: 2351: 31 May 2016 – 1 August 2016Meetings: 6:00 - 10:00 PM Central European Time; Thursday (June 2, 9, 16, 23, 30,July 7); 6:00 – 10:00 PM Central European Time; Tuesday (July 12)Location: Eagle Vision Classroom-Blended Learning (EVC + Online) Instructor:	Office Hours:	By appointmentTelephone: 49-(0)6371-51163 (Germany)E-mail:	Course Description:This is a pre-calculus course designed for the student aviation. Topics include a review of the fundamentals of algebra; linear equations and inequalities, quadratic equations; variation; polynomial, rational, exponential, logarithmic and trigonometric functions; radian measures; right triangle solutions, vectors and the laws of sines and cosines. Lecture hours per week (4:45 hours). Prerequisite(s): Qualifying score on the ERAU Mathematics Placement Examination or MATH 106Course Goals:This course provides students the necessary algebra, geometry and trigonometry required for the study of topics in aviation.Learning Outcomes: Alignment with Worldwide College of Arts and Sciences General Education program outcomes is indicated in parentheses. Upon completion of the course, students will be able to do the following:Use the basic properties of the real number system to perform algebraic operations including factoring, operations with fractions, exponents and radicals. (PO 1 & 7)Solve linear, quadratic, absolute value, rational, exponential, and logarithmic equations. Transpose formulas and solve verbal problems that require the use these types of equations. (PO 1 & 7)Solve linear inequalities and identify their solutions by using interval notation and graphs. (PO 1 & 7)Use the distance, midpoint, point-slope-intercept formulas to solve problems involving line and line segments. (PO 1 & 7)Solve verbal problems involving direct, inverse and joint variation. (PO 1 & 7)Define function, domain and range; and identify examples of each. (PO 1 & 7)Graph linear, quadratic, polynomial and rational functions by using standard forms,
symmetry, intercepts, asymptotes, and vertical and horizontal shifts. (PO 1 & 7)Graph exponential and logarithmic functions and solve word problems dealing with these functions. (PO 1 & 7)Convert radians to degrees, degrees to radians and solve arc length problems using radians. (PO 1 & 7)Define the six trigonometric functions and the fundamental trigonometric identities, and use them to find the values of various trigonometric functions. (PO 1 & 7)Use the six trigonometric functions to solve right triangle problems, resolve vectors into rectangular components, and solve force and displacement problems using vectors. (PO 1 & 7)Solve word problems by using linear systems of equations. (PO 1 & 7)Solve oblique triangle problems using the Law of Sines and the Law of Cosines. (PO 1 & 7)List of Program Outcomes for General Education of Arts and SciencesPO1 - Apply knowledge of college level mathematics to defining and solving problems; PO7 - Use digitally-enabled technology to organize and manipulate data, perform calculations, aid in solving problems, and communicate solutions, ideas, and concepts;Required Course Materials:Washington, A. J. (2014). Basic technical mathematics with calculus and MyMathLab (Custom 10th Edition). Upper Saddle River, New Jersey. Pearson Education, Inc. ISBN: 978- 1269723480. NOTE: Sections 21.1 and 21.2 were inadvertently omitted from the first printing of the custom textbook. They will be included in subsequent printings. Those sections are available in the electronic text available through MyMathLab (MML). To read these sections, students should access their MML course and go to the Multimedia Library option and then select Chapter 21 > All Sections > select the Multimedia Textbook option.A computer with reliable internet access is also required for this course. Note that you may need administrative access to be able to install applications or make other changes to your computer. If the computer you are using is not your own personal property, you may need to contact your organization's IT department to make such updates.MyMathLab (MML) is a course requirement. All the homework and exams are offered within MML. In addition to this, MML has some helpful videos and an online copy of the textbook that is readable from a multitude of platforms. While an eBook offers advantages, some strongly prefer the smell of paper and ink a learn best with a book to thumb through. The choice is ultimately yours.Students are required to have a calculator – handheld or virtual. Graphing calculators are not necessary for this course but would be useful (TI-83, TI-84, or equivalent are recommended). A scientific calculator that offers the trigonometric functions of SIN, COS, and TAN will suffice in this course. Whatever calculator you select, be sure that you know how to use it as this will minimize mistakes and frustration.Suggested Supplemental Materials:ERAU Worldwide provides MathType and Graphmatica software in the Resources page of this course. The software helps you write mathematical symbols in Canvas Discussions and in Word documents (although the equation editor in Canvas works just fine for your online Discussions). Use of these programs are optional and up to the individual student. Note that Graphmatica 2.0 is Shareware and as such you are bound by its terms—a trial is free, but once you decide it’s useful, you pay for the lifetime license ($25, as it has been since the last millennium). ERAU has no formal relationship with the owners of Graphmatica.Free tutoring for this course is available via Eduwizards. ERAU has contracted with Eduwizards to provide you with 10 hours of math tutoring available 24/7. Click here to be taken to the student logon page for this service. Students can request an instant “on-demand” session with a live person, schedule a session in advance with a tutor of their choice, or submit a question online and receive an answer within 24 hours. If you use up your initial block of time, simply contact the eLearning Support to request more.GradingGrade	90 – 100 A (Superior)80 - 89	 B (Above Average)70 - 79	 C (Average)60 - 69	 D (Below Average)Below 60 F (Failure)Library:The Jack R. Hunt Library, located on the Daytona Beach Campus, is the primary library for all
Worldwide Campus students.Web & Chat: http://huntlibrary.erau.edu/
Email: library@erau.edu
Text: (386) 968-8843
Library Phone: (386) 226-7656 or (800) 678-9428
Hours: http://huntlibrary.erau.edu/about/hours.htmlAssignments, Discussion Participation (If required for Blended Delivery and/or special administrative:All assignments will be completed in a professional manner and on time, unless prior arrangements have been made with the professor. This course includes weekly activities, each of which may have grade points associated with them. Unless prior arrangements have been made with the instructor, students are expected to participate each week, according to the course schedule. This is especially important with regards to discussion activities. Weekly Canvas discussions typically include both an initial posting and one or more substantive replies in courses offered via Blended Learning. The Blended Learning activities will include nine Application PIN (Pre-Calculus Identification Numbers) Problems, one in each module. Note: Proper etiquette has to do with keeping it simple by using proper English and proper spelling – spell check works well in Canvas.MyMathLab: MyMathLab will be purchased as part of the text for this course. Until your book arrives, you will use MyMathLab for a free, 14-day trial period. Using MyMathLab is not optional.Reading: You are required to read the assigned chapters before class and before attempting any of the assignments each week. Please make time to take notes while you read the text, watch MML videos, and work through the sample problems each week. This is not a graded component of the course, but your success in subsequent assignments depends on your understanding of this material.MyMathLab (MML) Homework assignments: There will be homework assignments delivered via MyMathLab every week of the term. You can access homework problem set the week before the assignment is due and work on it up until the due date. You will get unlimited chances to submit an answer. MML HW assignments submitted after the deadline will be automatically downgraded 10% per day for the portion of homework that is late.MyMathLab Exams: There are three exams for you to complete in MML. You will have a single opportunity to answer the set of questions. Once you start the exam, you must complete it. Each test will be graded immediately by MML, but your instructor will review your entries and any scratch work before determining your final score. If you experience any technical difficulties with the exam, contact your instructor as soon as possible for assistance. All exams will be available for a one-week period, after which the link will become unavailable.Study Plan: The MML/MSL system uses the results of your work in the homework, quizzes, and exams to build an individualized study plan. The purpose of the study plan is to provide an opportunity for you to review the areas of the course where you may need additional work. The study plan offers additional exercises in the areas of the course where you may need improve your knowledge and skills. This is not a graded component of the course, but is an opportunity to address weaknesses before taking more heavily-weighted assessments.Blended Learning Assignment: Blended Learning assignments for this course will consist of math questions presented via a forum created for each week in the Discussions area in Canvas. These are the Application PIN Problems. You must make an initial post by 11:59 PM ET on the third day of your week (Sunday) and then post a reply to the initial posts of two or more of your classmates by 11:59 PM ET at the end of your week (Wednesday). Points will be deducted for poor grammar, misspelled words, improperly cited web/textbook references, late initial posts, and posts made all at one time (do not “post and run”). Posts made after the end of the module will be reviewed by your instructor, but you will not get any credit for them.Class Participation: Extra credit points for class participation will be earned each night by working a problem on the whiteboard with the instructor and participating in class when called upon. These extra credit points will be student initiated.Course Policies:Embry-Riddle is committed to maintaining and upholding intellectual integrity. All students, faculty, and staff have obligations to prevent violations of academic integrity and take corrective action when they occur. The adjudication process will include the sanction imposed on students who commit the following academic violations, which may include a failing grade on the assignment, a failing grade for the course, suspension, or dismissal from the University:Plagiarism: Presenting as one’s own the ideas, words, or products of another. Plagiarism includes use of any source to complete academic assignments without proper acknowledgement of the source. All papers submitted for grading in this course will be submitted to Turnitin where the text of the paper is compared against information contained in the Turnitin database. Papers submitted will be included in the Turnitin database and become source documents for the purpose of detecting plagiarism.Cheating: A broad term that includes the following:Giving or receiving help from unauthorized persons or materials during examinations.The unauthorized communication of examination questions prior to, during, or following administration of the examination.Collaboration on examinations or assignments expected to be individual work.Fraud and deceit, that include knowingly furnishing false or misleading information or failing to furnish appropriate information when requested, such as when applying for admission to the University.APA 6th edition format is the ERAU Worldwide standard for all research projects.Course Specific Policies:Blended Learning Policy: This course is offered in blended format. 70% of the required course will be conducted in-class. 30% will take place online in Canvas. Class meetings will be comprised of lectures, audio-visual presentations, discussions, exercises, and student presentations. Online activities will include discussion with classmates, posting of your work, reviewing classmates’ work, and feedback from the instructor on your work. During the first class meeting, we will thoroughly review the online Blended Course activities.Missed Class Policy: You are expected to attend each class session in its entirety. Notify the instructor as soon as possible if you will not be present or if you will miss part of class. If you miss any part of class, you will need to review the EagleVision recording and use the Missed Class forum in the Discussions area to post a summary of the highlights from the lecture plus identify unresolved questions you have about the material you missed. Your instructor and classmates will reply to your post in order to clarify anything and bring you up to speed with the rest of the class. Your instructor will also check the EV playback log to verify that you watched the session. Students who fail to complete this make-up assignment 48 hours before the start of the next class session will lose 5% off their overall course grade.Late Work Policy: All course work is expected to be completed on time and should be submitted before 11:59 PM ET on the date indicated in the Course Schedule below. Please coordinate with the instructor as soon as possible if you know your assignment will be late. In some special cases, a penalty-free extension might be granted if you provide your expected date of submission in addition to the reason you cannot make the deadline (expect to provide supporting documentation). Keep in mind that you always have at least 7 days to complete your assignments, so difficulties encountered less than 24 hours prior to the deadline will not be viewed in a favorable light. This late work policy will include all MML Tests and Discussions.Disability and Special Needs:ERAU is committed to the success of all students. It is a University policy to provide reasonable accommodations to students with disabilities who qualify for services. If you would like to request accommodations due to a physical, mental, or learning disability contact the Worldwide Campus Disability Support Service Office at (888) 292-5727 or via email wwdss@erau.edu or worldwide.disability.support.services@erau.edu.Course ScheduleAppendix GSample Latter to Request Institutional Site ApprovalJohn Watret, Ph. D.Chancellor of Worldwide CampusDepartment of Mathematics, Physical & Life SciencesWorldwide College of Arts & SciencesWorldwide CampusMay 22, 2018Re: Permission to Conduct Research at ERAUDear Dr. Watret,I am writing to request permission to conduct a research study at your institution. I am an alumni and doctoral student at Nova Southeastern University at Fort Lauderdale, Florida. The dissertation title is The Relationship of Learning Styles, Performance and Satisfaction for Students Enrolled in Online Courses.I hope that the school administration will allow me to recruit 100 online undergraduate students enrolled in a nine week online (MATH 111 College Mathematics for Aviation I) from the ERAU Worldwide department. Half of these participant will anonymously complete the Kolb Learning Style Inventory v3.1 On-line (copy enclosed). In addition, all participant, anonymously, will take the Strachota’s Online Satisfaction Survey (copy enclosed). Interested students, who volunteer to participate, will be given a consent form to be signed before the study commence (copy enclosed).If approval is granted, student participants will complete the survey online and it should take no more than five minutes to complete the Kolb Learning Style Inventory v3.1 and Strachota’s Online Satisfaction Survey will take approximately 15-20 minutes to complete.The individual results of this study will remain absolutely confidential and anonymous.  Should this study be published, only pooled results will be documented.  No costs will be incurred by either your university or the individual participants.Your approval to conduct this study will be greatly appreciated.  If you wish we can meet to discuss the study. You may contact me at my email rosie.suarez@live.com or rs1414@nova.ed. You can also contact me at 386-299-9097.Sincerely,Rosabelle SuarezDoctoral StudentNova Southeastern University EnclosuresAppendix HSample Letter to Use the LSIRosabelle Suarez1171 Margina AveDaytona Beach, FL 32114May 31, 2018LSI Research ContactsHay Research GroupOne Atlantic Center Suite 620
1201 West Peachtree Street
Atlanta, GA 30309
Dear Sir/Madam:I am a doctoral student from Nova Southeastern University writing my dissertation titled The Relationship of Learning Styles and Success for Students Enrolled in Online Courses, under the direction of my dissertation committee chaired by Dr. Cesáreo Morales, who can be reached at mcesareo@nova.edu.  I would like your permission to use the Kolb’s Learning Style instrument electronic version 3.1 in my research study.  I would like to use the instrument under the following conditions:I will use the surveys only for my research study and will not sell or use it with any compensated or curriculum development activities.I will include the copyright statement on all copies of the instrument.I will send a copy of my completed research study to your attention upon completion of the study.If these are acceptable terms and conditions, please indicate so by replying to me through e-mail:  rosie.suarez@live.com or rs1414@nova.eduSincerely,Rosabelle SuarezDoctoral CandidateAppendix IApproval to Use LSIHi Rosabelle,

Congratulations! Your LSI 3.1 online use for research has been approved.

We wish you luck with your research and look forward to hearing about your findings. Please send a completed copy of your research to email address or you can mail a hardcopy to: LSI Research Contractsc/o Maggie ZennerKorn Ferry Hay Group, Inc.1650 Arch Street, Suite 2300Philadelphia, PA 19103 Please let me know if you have any questions.
Thank youMaggie Appendix JRequest and Approval to Use Online Satisfaction SurveyFrom: Elaine Strachota <strachotae@gmail.com>Sent: Tuesday, December 5, 2017 12:50 AMTo: rosie.suarez@live.comSubject: Fwd: PermissionRosabelleYou have my permission to use my Online Satisfaction survey for your doctoral research. Be sure to reference me as the author of the instrument. Best of luck to you with your research.Elaine StrachotaUniversity of Wisconsin MilwaukeeMilwaukee WIstracho3@uwm.edu---------- Forwarded message ----------From: Rosabelle Suarez <hit-reply-premium-inmail@linkedin.com>Date: Wed, Nov 29, 2017 at 2:59 PMSubject: PermissionTo: Elaine Strachota <strachotae@gmail.com>Dear Dr. Strachota: My name is Rosabelle Suarez and I am a doctoral student at Nova Southeastern University at Fort Lauderdale, Florida. The dissertation title is The Relationship of Learning Styles, Performance and Satisfaction for Students Enrolled in Online Courses. I am writing to you in regards to the use of your instrument entitled "Online Satisfaction Survey". During my literature review, I came across a survey you developed in 2003 with a title of "Online Satisfaction Survey". This survey fits the needs of my research questions as part of my study focus on determining the satisfaction of online students while comparing it with learning styles and performance. May I please ask for your permission to utilize this survey as part of my dissertation and data gathering process?The requested permission extend to any future revisions of my dissertation and to the public circulation of my dissertation by Nova southeastern University. If you are not the copyright holder, or if another permission is needed for world right from another source, please let me know. Yours Truly,Rosabelle SuarezAppendix KApproval to Use FiguresMs. Suarez,Stylus Publishing grants permission for you to use the figure in your dissertation free of charge provided that you put the following copyright notice on the page with the figure:Reprinted from The Art of Changing the Brain: Enriching the Practice of Teaching by Exploring the Biology of Learning by James E. Zull (Sterling, VA: Stylus Publishing, LLC) with permission of the publisher, Copyright © 2002, Stylus Publishing, LLC. Thank you,Jane LeathemStylus PublishingJane.Leathem@styluspub.comwww.styluspub.comTo place an order:Tel:  800.232.0223Fax:  703.661.1501stylusmail@presswarehouse.coAppendix LSample of Learning Style ProfileAppendix M Satisfaction QuestionnaireDirections: For each of the statements below, read the statement carefully and decide how well it describes the class you are now attending. Indicate your opinion by selecting one of the choices provided. Be sure that you select only one choice for each statement.

What two things does the text say you should decide when reading each statement?