Personality Learning Style Preferences and Student Achievement in
Online University Learning Environments

Teresa A. Le Sage
Barba A. Patton
University of Houston-Victoria

Introduction
   The demand for online courses is increasing worldwide, which has raised quality issues among educators.   It is no secret that many institutions rushed onto the electronic super highway have floundered in the delivery of web-based instruction, “Everyone thought it was going to take off and if they didn’t get into right away, they’d miss out” (Managan, 2001).  Although some university distance education programs have failed, it is expected that there will be major growth in higher education delivered electronically.  Experts project that electronic learning will transform the way learning occurs in most, if not all, college classrooms (Allen & Seaman 2006; Carnevale, D. 2005; Newman & Scurry, 2001), thus it is very important to understand how virtual students learn and the differences/similarities between them. In 2005, about 70.2% of all college students (3.2 million) in the United States took at least one online course, which was an increase of 39 % from 2004.  Worldwide, nearly 19 % of the population has been internet penetrated (Internet World Stats, 2007 & Pew Research Center, 2007).   
     Students participating in web-based courses have different cognitive learning styles and individual differences. Although one or more learning and thinking styles may be suited for electronically delivered content, others may actually hinder learning in an electronic environment.   Some students in web-based courses may have characteristic primary personality preferences that are better suited for the electronic learning environment, thus requiring no learning style adaptations and those students who do not have a natural tendency to learn in the electronic learning environment will be at a disadvantage.
     The major purpose of this study was to examine the effects and interactions of undergraduate students’ personality learning style and electronic activity on student achievement in an electronic university environment.  The secondary purpose of this research was to explore the relationship of gender, age, and ethnicity with personality learning style. Subjects were 106 undergraduates in six business web-based courses.  Factorial analysis of variance (ANOVA) was utilized to determine significant main effects and/or interactions of personality learning style, electronic activity, and achievement.  The Pearson chi-square test for independence was used to determine if there was a relationship between learning style personality preference and ethnicity, gender, and age. 
Significance of Study
     Accessing education on the “go” is quickly becoming the norm in higher education.  College students are early adapters (natives) and heavy users of the internet (Jones, 2002), while most instructors are digital immigrants. Many educators question if college students can learn as well in an electronic environment as they can in the traditional classroom. Education delivered virtually is in its infancy (a little over 10 years) compared to instruction delivered face-to-face over the past thousands of years. Higher education institutions are offering web-based courses without investigating if students are successful in this environment. The learning styles of students in virtual learning environments have not been examined thoroughly (Whiteley, 2007). Pedagogical research is needed to help determine best instructional practices.
     It is important to investigate student learning styles since most college students are taking online courses.  There has been an explosion in the utilization of electronic media since the internet became commercially available over ten years ago with 71% of the adults in the United States now accessing the world-wide-web (Pew Research Center, 2007).  The integration between information and telecommunication technologies is promoting greater accessible learning environments, which has produced a demand for a web-based education (Musa & Wood, 2003).  More than 1,100 colleges and universities in the United States were offering web-based courses a few years ago (Newman & Scurry, 2001) and the numbers are rising to over 2000 (Allen & Seaman, 2006).  Distance education is continuing to grow, although, some educators have been slower to embrace the technology as an integral part of their educational delivery, web-based education is now worldwide.  Several institutions have even abandoned their online spinoffs like rotten fish (Foster & Carnevale, 2007) while others have embraced the delivery of online higher education products.  
      Research has been conducted on personality learning style preferences of students in the traditional face-to-face learning environment, yet very little in the web-based environment. Learning style theory suggests that individuals have different ways of learning, and when teaching accommodates these cognitive preferences, student achievement is greater (Sonnenwald & Li 2003; Whitely, 2007).  The online learning environment is more complex than the basic physical face-to-face learning environment, but it does not change the fundamental process of human learning and is a new space for teaching and learning (Ellis, 2006 as cited from Alexander & Boud, 2001). Student personality learning style preference and characteristics that contribute to student achievement in virtual learning environments needs to be investigated to help continuously improve the delivery of curriculum and instruction. Results of this research may aid administrators, faculty, students, and instructional designers in understanding and enhance electronic learning, and also stimulate further research.  Monitoring student learning styles over time can also reveal if students are adapting to the new learning environments. 
Personality Learning Styles
     Over the past 20 years, researchers have made significant progress in understanding human cognitive styles and personality. Personality preferences are a combination of inherited disposition and environmental shaping (Keirsey & Bates, 1984; Keirsey, 2000). Research has shown that personality learning styles preferences are both socially and culturally correlated (Sternberg, 1997), which indicates a framework open to some adaption. Environmental factors include a cultural component with different cultures expressing different percentages of personality styles, and a rearing component that indicates personality styles are dramatically shaped at a very early age (Ouellette, 2001).  Consequently, heredity, social, and environmental factors have a major role in the development of learning styles.  Genetics and environment are always interacting with each other.   Thus, personality preferences and human learning are associated with the environment. 
     Basic differences in an individual’s personality are the way people prefer to use their minds (Myers & Myers, 1980). It is the preferred and habitual approach to organizing and representing information (Riding & Rayner, 1998). Individual personality learning preferences, however, typically refer to a singular capability or preference that may enhance learning in some situations yet hinder it in others. The nature of personality learning style preferences and how people learn are factors, which make adaptation a possibility.  Dewey (1973) and Vygotsky, (Kozulin, 1990) both noted that what people experience is directly related to how people learn.  It is through experience that students adapt to new learning stimuli in the environment, and gradually adjust as they recognize what is necessary for success.  Many common examples of adaptations are readily seen in our culture, such as adaptation to the utilization of video cameras, cell phones, and personal computers as they were introduced to society. 
     Many learning preference theories are based upon extensive research examining personality types that reflect a person’s orientation either toward the inner world (introversion) or toward the external reality (extraversion). One of the most well known theories was developed by Carl Jung who proposed that personality is composed of three major dimensions: introversion versus extraversion, thinking versus feeling, and sensation versus intuition (Cloninger, 1996). The fundamental attitude of the individual, according to Jung, is the inclination toward introversion or extraversion. Combining this fundamental attitude with the functions of thinking, feeling, sensation, and intuition, eight psychological types emerged. It is the differences in these psychological-types expressed by individuals as temperaments and characteristics that contribute to individual uniqueness. In addition to Jung’s three dimensions, which he categorized as attitudes, functions, or preferences, Myers and Myers (1980) added another function called theJudgment-Perceptive preference, which is concerned with how the person prefers to live. 
     An excellent illustration and description of the four preferences is portrayed on Figure 1 This simple and excellent figure clearly describes the four preferences and the bi-attribute of each (McCaulley & Natter, 1974).  The less preferred personality style is called the auxiliary function or the inferior function, since it is not as developed as the dominant attitude or function.  For example, the auxiliary attitude for an extravert is the internal reality and vice versa for the introvert where the external reality is the auxiliary function (Cloninger, 1996).  Jung believed that every individual has all of these characteristic types but to a different degree; the attitude and function are the ego’s characteristics of orientation and processes.
     Research on college student personality preferences and learning has shown that students majoring in different disciplines have different preferences, which have affected academic achievement (Melear, 1989; Raiszadeh 1997; Shuler, 1999; Skauge, 1999; Soucy, 1996; & Tribble, 1998). Some personality preferences are predominant in science and mathematics. For example, it has been found that students majoring in science are likely to be introverted and intuitive (Raiszadeh, 1997 & Skage, 1999). Melear (1989) found significant differences between biology and non-biology majors as well.  Ethnicity, age, and gender have all been shown to impact personality and learning preferences in some studies, while others show no relationship (Gordon, 1996; Lu, Yu, & Liu, 2001; Shuler, 1999; Soucy, 1996).  Personality preferences are also related to mode of course delivery. According to Tribble (1998), students enrolled in nontraditional education programs expressed an extraversion preference over the introversion preference. It is important to take notice of these characteristics especially since more women are attending college, and a growing number of students are over thirty years of age, work full time, and have families (Gorden, 1996; Souncy, 2001).
     Although we have long been aware that students learn differently from each other, we traditionally use one teaching method at colleges and universities, the lecture (Newman & Scurry, 2001). Students approach their work differently, depending on their psychological type (Cloninger, 1996).  Effectiveness of student learning depends largely on the strategy employed by the individual.  Students often fail to choose the strategies that are most effective for their own learning and often do not match a particular strategy to the learning task (McKeachie, 1994). 
     When a student's preference does not match an instructional method, they are at a disadvantage.  Researchers have determined that when student and faculty learning preferences do match, the classroom experience yields enhanced learning (Dinham, 1996; Eble, 1988).  Also, student achievement can be influenced by the personality preference of both the teacher and student (Elias & Steward 1991; Mark, Michaels, & Levas, 2003; Wicklein & Rojewksi, 1995). In addition, preferences need to match the environmental setting (Sternberg, 1997), where environment the layout and design of the virtual space is important (Ellis, 2006).  Finally, faculty performance benefits from an environment that matches their preference.  
     Although one or more preferences may excel in an electronic environment, others may actually hinder learning.  Among the options for electronic delivery is the use of web-based courses, designed to deliver higher education to students in place of a traditional face-to-face class.  Determining the web-based student personality preference(s) will help educators design an on-line curriculum that enhances learning.   
Methodology
     The primary purpose of this study was to determine the effects and interactions of university student personality learning style preferences and electronic activity on student achievement in a web-based classroom environment.  The secondary purpose was to determine the relationship between ethnicity and personality learning style preference, the relationship between age and personality learning preference style, and the relationship between gender and personality learning style preference.
Research Hypotheses
     The following hypotheses were developed to guide this study:
Ho1.  There is no significant main effect of personality learning style preference on student achievement of undergraduate students enrolled in a web-based course.
Ho2. There is no significant main effect of student electronic activity on student achievement of undergraduate students enrolled in a web-based course.
Ho3.  There is no significant interaction or interactions between personality learning style preference and electronic activity on student achievement in a group of undergraduate students enrolled in a web-based course.
Ho4.  There is no significant relationship between personality learning style preference and ethnicity in a group of undergraduate students enrolled in a web-based course.
Ho5.  There is no significant relationship between personality learning style preference and gender in a group of undergraduate students enrolled in a web-based course.
Ho6.  There is no significant relationship between personality learning style preference and age in a group of undergraduate students enrolled in a web-based course.
Procedure
     This study was designed to determine whether there was a significant main effect and interaction between students’ personality learning style preferences, electronic activity  (student activity courseware report tracking) on student achievement in web-based courses.  Personality learning style preferences were measured utilizing the Keirsey Temperament Sorter (KTS) and student electronic activity rate was gathered through the Manage Course application provided with Web Course Tools (WebCT) web-based courseware.  All web-based courses under investigation utilized WebCT courseware.  A student data form was utilized to record the students’ personality learning style preferences, electronic activity, and other individual characteristics that answered the hypotheses.  Final semester grades were used as an indicator of course achievement.
     Students’ personality learning style preferences and data from the student data forms were compiled into the Statistical Package for Social Science (SPSS 10.0) for Windows.  Hypotheses 1 through 3 were tested utilizing a factorial analysis of variance (ANOVA) to determine whether there was a significant effect of the independent variables personality learning style preference, student electronic activity, and/or a significant interaction on the dependent variable, student achievement.  Hypotheses 4 through 6 were tested utilizing a Pearson chi-square test for independence to determine if there was a relationship between learning style personality preference and ethnicity, gender, and age.
Participants
     Students enrolled in undergraduate web-based business courses at the University of Houston-Victoria were the sample for this study.  There were 106 students (subjects) for this study.  The web-based course offerings were as follows:  Business Finance, Quantitative Management Science, Principles of Marketing, International Marketing, and Management Leadership.  Each course had enrollments between 10 to 28 students.  
Limitations
     This study was limited by the following factors:
1.  Research was conducted at one university and during a ten-week semester.
2. Students enrolled in web-based courses may demonstrate a preference for the electronic learning environment through self-selection. 
3. This study was confined to web-based business courses; data from this research may not be applicable to other college disciplines.
Instruments
     Two instruments were utilized: The Keirsey Temperament Sorter (Keirsey, 2000), and a student data form.  The Keirsey Temperament Sorter is widely utilized in psychological, business, and educational research (Cloninger, 1996; Keirsey, 2000).  The Keirsey Temperament Sorter (KTS) was administered to determine the personality temperaments and characteristics for this research since it is based on the Jungian functions, and it is reliable, valid, and available electronically to faculty and students alike. The KTS consists of 70 items, each with 2 choices.  After an individual submits the instrument electronically, the results are automatically reported to them.  Students received a personality profile describing their temperament and personality characteristics. 
     The reliability coefficients of the KTS are between 0.54 and 0.86 (Kelly & Jugoviv, 2001; Quinn, Lewis, & Fischer, 1992).  The KTS measures the same constructs as the Myers-Briggs Type Indicator which has also been proven to a valid indicator (Kelly & Jugoviv, 2001; Tucker & Gillespie, 1993). 
Frequency Data Results
     Of the 106 undergraduates surveyed (6 courses), 71 (76%) responded.  Sixty-nine of the 71 students (65% of the total) completed the Keirsey Temperament Sorter (KTS) and recorded their results on the Student Data Form (SDF).  All percents have been rounded up unless otherwise noted. 
     The majority of students were over 23 years of age (approximately 86%) with 47 % of the students between 23 and 29 years of age. Sixty-two percent of the undergraduates surveyed were women. The majority of students were white-non-Hispanic (76 %) and the next largest ethnic group was Hispanic (11 %).  Approximately 60 % of the undergraduate students were Sensing-Judging (SJ personality learning style preference) followed by Sensing-Perceptive (SP) with 23 %. Intuition-Thinking (NT) comprised about 10 %, while Intuition-Feeling (NF), composed 7 %. The personality preferences of the undergraduates in the present study agreed with the literature when their personalities were compared to the business disciplines and general population. The majority of individuals in business disciplines are SJ’s (Detibertio & Hammer, 1993; Jacoby, 1980; Laribee, 1994; Lawrence, 1982; Satava, 1994).  Sensors generally comprise 75 % of the general population and J’s and P’s each have 50 %; the population under study was greater in S’s with 83 % and lower in P’s at 32 %.
     The majority of male and female students were the SJ personality learning style, 56% and 62 % respectively. The second highest personality learning style preference was SP for both genders with 19 % male and 26 % female. Males totaled 11 %  NT’s and 15 % NFs’ personality learning style preferences. It was interesting to note that males had a higher percent NF’s than females even though there were more female students in this study.  Females comprised 10 % NT’s and 2 % NFs’.           
     Crosstabulation of undergraduate age group and personality learning style preference revealed that 59 % of the students between the age of 23 and 29 were SJ’s, and 90 % of the students under age 22 were SJ’s as well.  Students over 40 years of age had the highest percentage of NT’s at 33 %. The majority (60%) of all the undergraduates were SJ’s when personality learning style preference was crosstabbed with ethnicity, except Asian or Pacific Islander with 100 % NT.     
Statistics Data Results
     Hypotheses 1, 2, and 3 were tested with the Analysis of Variance Between-Subjects Factors Full Factorial Model (ANOVA) to determine the main effects and interactions of Personality Learning Style Preferences and Electronic Activity on Student Achievement.  Full factorial model contains all the factor main effects and all factor-by-factor interactions.  Pearson Chi Squares were deployed on Hypotheses 4, 5, and 6:  Personality Learning Style Preference and Gender; Personality Learning Style Preference and Ethnicity; and Personality Learning Style Preference and Age Group.
     Student majors were nearly equally distributed in Accounting (30%), General Business (27%), and Management (24%).  Crosstabs revealed that the majority of these students in the above majors are SJ’s and are as follows:  Accounting, 56 %; General Business, 65 %; and Management, with 69 %. Sensing-Perception was the second highest learning preference (39 %) in Accounting and General Business (24 %). Intuition-Thinking (NT) comprised 25 % of the Management major.
     The most frequent Keirsey primary personality profile was ISTJ at 25 %, followed by ESTJ, ESFJ and ISFJ with each totaling approximately 12 %, and the ESFP profile with 13 %.  The primary
Jungian functions of the students are as follows:  Extraversion, 52 % and Introversion, 48 %; Sensing, 86 % and Intuition, 14 %; Feeling, 52 % and Thinking, 48 %; and Judging, 68 % and Perceptive, 32 %.  Crosstabs on student final grade and personality learning style preferences revealed that most students achieved a grade of B or better in all personality learning style preferences.  Ninety-seven percent of the undergraduates received a C or better (approximately, 48 %--A, 24 %--B, and 25 % --C).
     Most students had either a medium electronic activity rate (42 %) or High electronic activity rate (39 %). Activity rates were divided into “Low, Medium, and High categories due to the nature of required activity in each course. 
     Crosstabs on student final grade and electronic activity rate revealed a greater percent of students with high and medium activity rates (54 and 47 % respectively) achieved higher grades—B or better.  Crosstabs on personality learning style preference and electronic activity rates results are as follows:  SP, 43% Medium activity, and 29 % in both Low and High activity; SJ, 49% High activity, 39 % Medium, and 13 % Low; NT, 67 % High activity, and 33 % Medium and 0 % Low; NF, 75 % Medium activity, 25 % Low, and 0 % High.
Hypotheses
Hypothesis 1:  There is no significant main effect of personality learning style preference on student achievement of undergraduate students enrolled in a web-based course.
Result: Personality learning style preferences SP, SJ, NT, and NF were found to not have a significant effect on student achievement when tested with the Analysis of Variance as shown below on Table One.  The null hypothesis was not rejected for personality learning style preferences.  There was no significant main effect at alpha .05 for personality types on student achievement as shown above on Table 1.  Eta squared showed that personality accounted for approximately 2% of variability of student achievement. 
Hypothesis 2:  There is no significant main effect of student electronic activity on student achievement of undergraduate students enrolled in a web-based course.
Result: Student electronic activity rate was found not to have a significant effect on student achievement when tested for Analysis of Variance as shown on Table 1.  The null hypothesis was not rejected. 
Hypothesis 3: There is no significant interaction or interactions between personality learning style preference and electronic activity on student achievement in a group of undergraduate students enrolled in a web-based course.
Result: There was no significant interaction between personality learning style preference and electronic activity rate on student achievement as shown on Table 1.  The null hypothesis was not rejected. 
     Figure 2 below shows that personality learning style attribute Feeling/Thinking and student electronic activity significantly interacted, but it was not a significant effect on student achievement as portrayed on Table 2. The Levene test was significant at p < .05 at .015 with 5 degrees of freedom across groups and 63 degrees of freedom across the subjects. The F statistic was 3.097. There may be differences across the groups. The other six personality attributes Introverion/Extraversion, Judging/Perceptive, and Sensing/Intuition did not show an interaction with student electronic activity. 
     The combined main effects of personality learning style preference and electronic activity rate on course achievement in the model was significant when tested utilizing Analysis of Variance Tests of Between subjects Effects (Table 2).  Levene's test of equality--homogeneity was not rejected as shown on Table 2. Error variance of the dependent variable was considered satisfactory across the groups of student personality learning style preferences and electronic activity rate on student achievement, and was not rejected. 
Hypothesis 4:  There is no significant relationship between personality learning style preference and ethnicity in a group of undergraduate students enrolled in a web-based course.
Result: Although the Pearson Chi-Square was significant, there are too many cells with counts less than five to conclude that there was a significant relationship between personality learning style preference and ethnicity (Table 3).  The results show there was a significant relationship at p > .05 between the personality type and ethnicity with a chi-square of 38.13 and 12 degrees of freedom.   Although, it should be noted that most of the cells had lower than expected cell counts of 5 as shown on Table 3.
Hypothesis 5:  There is no significant relationship between personality learning style preference and gender in a group of undergraduate students enrolled in a web-based course.
Result: Although the Pearson Chi-Square was not significant, there are too many cells with counts less than five to conclude that there was not a significant relationship between personality learning style preference and gender (Table 4).  No significant relationship was produced between personality type and gender.  The Pearson chi-square of 4.076 was not significant at p < .05. 
Hypothesis 6:  There is no significant relationship between personality learning style preference and age in a group of undergraduate students enrolled in a web-based course.
Result: Although the Pearson Chi-Square was significant, there are too many cells with counts less than five to conclude that there was a significant relationship between personality learning style preference and age (Table 5).
Conclusions
     Although the results of this study revealed that the main effects and interactions of university students’ personality learning style preferences and electronic activity on student achievement produced no significant effects or interactions, it is important to explore student achievement and cognitive preferences to help ensure effective learning environments. It was interesting that the personality learning style attribute, Feeling/Thinking and electronic activity significantly interacted. The results revealed that students with different levels of electronic activity can be successful.  The relationship of personality learning style preferences with age, gender and ethnicity generated mixed significant results, which was consistent with the literature.
     There were nearly an equal number of extraverts and introverts in the undergraduate population.  The 18-22 years of age group were nearly all SJ’s.  Although fewer subjects in this age group were compared to the others, it was a notable result.  Since learning style has been related to culture, research on personality learning style preference and its relationship to ethnicity and age could be investigated to study how individual’s learning style characteristics may change as they age and the relationship to culture.  It would be interesting to determine if there are any relationships between personality learning style preferences and the other learning styles—visual, auditory, and the kinesthetic/tactile commonly utilized in educational settings.  Few faculty revealed their personality learning style preferences in this study.   Future research on faculty may include qualitative methods to encompass a more inclusive portrayal of college faculty personality learning style on student achievement.
     Additional research is needed on a consistent long-term basis to determine student personality learning style relationships and factors in web-based learning environments, and its impact on student achievement.  Examining the students, the faculty, and the electronic learning environment will help higher education institutions promote better teaching and instructional methods.  Determining the web-based students’ personality learning style preference(s) should help educators design an on-line curriculum that enhances learning.  Monitoring student personality learning style preferences over time can also reveal if students are adapting to the new learning environments. 

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Figure 1: Personality Orientation

Table 1: Undergraduate Personality Types SP, SJ, NT, and NF ANOVA
Test of Between-Subjects Effects

Table 2:  Personality Preferences Feeling/Thinking ANOVA
ANOVA Tests of Between-Subjects Effects

Figure 2: Interaction of Personality Learning Style and Student Electronic Activity on Grade 

Table 3: Personality Type and Ethnicity Chi Square

Table 4: Personality Type and Gender Chi-Square

Table 5: Personality Type and Age Chi-Square