Assessing the role of personality traits in student performance in traditional, hybrid and online classes.
Tidwell, Michael V. ; Southard, Sheryne ; Mooney, Mara 等
INTRODUCTION
With the rate of online learning growing exponentially each year,
virtually every university within the United States of America offers
some type of online educational programming. A 2008 study performed by
Allen and Seaman for the Sloan Consortium found that online learning
grew over 100% between 2002 and 2007. Moreover, the study found that
one-fifth of all higher education students are now taking at least one
online course. This growth trend is expected to continue for numerous
reasons. First, universities can expand their geographic reach with
little effort. While many of the large universities recruit nationally
or internationally, most universities recruit from a limited geographic
region. Distance education provides universities with an opportunity to
increase student access to their degree programs. Second, and related,
online courses allow flexibility in students' schedules, thereby
increasing enrollment. With the current economic environment many
students find it necessary to work while going to school, making
scheduling an increasingly central issue. Third, with limited
construction budgets, the bricks and mortar model of education is more
expensive to sustain. While some states, such as Georgia, are projecting
significant growth in the college population (University System of
Georgia, 2006) other states anticipate a period of moderate enrollment
decline, which will intensify the competition among colleges to attract
students (Redden, 2008). Offering online education makes it more
economical to accommodate these students without the infrastructure
investment. This affords universities the ability to adjust course
offerings as demand fluctuates. Lastly, distance education allows
universities to achieve greater economies of scale through increased
enrollment, which leads to increased revenue, while more effectively
containing costs.
Given these reasons, increased enrollment in online education makes
it important to understand student learning in an online environment
versus a traditional environment. As dozens of studies have been
performed in this area, two lines of research have developed. First,
some studies have explored how individual differences impact student
performance in online learning environments (e.g. Cheung & Kan,
2002; Didia & Hasnat, 1998; Wojciechowski & Palmer, 2005). The
second line of research has examined the types of external factors most
conducive to success in online learning programs (Hall, 2008; Liu,
2008). However, to date, few have explored the connections between these
two streams of research. More specifically, none have examined whether
individual differences are more central to performance in traditional,
hybrid, or online classes. The current study fills this void.
This study seeks to accomplish three goals. First, it delineates
whether personality traits like the Big 5 factors (neuroticism,
extroversion, openness to experience, conscientiousness, and
agreeableness) or intelligence are related to student performance.
Second, it assesses whether any differences emerge in student
performance based on the various course delivery methods. Lastly, it
explores whether individual differences and course delivery method
interact to influence student performance.
LITERATURE REVIEW
Personality is often conceptualized as one's enduring
tendencies, moods, or temperamental makeup that are stable across
situations and contexts (Daly & Bippus, 1998; Epstein &
O'Brien, 1985; George, 1992). Though years of scholarship suggests
its validity as an important factor in understanding human behavior
(e.g. George, 1996) others have significant reservations often stemming
from overly optimistic expectations regarding its predictive validity
(Davis-Blake & Pfeffer, 1989). Specifically, some have argued that
personality traits, not situational characteristics, are the central
factors necessary to understanding human behavior. In reality, behavior
is simultaneously affected by both (Epstein & O'Brien, 1985;
George, 1996; Weaver, 1998).
As a result when personality is predicted to play a role in
personal outcomes, most scholars limit its scope to very specific
outcomes and provide a conceptual basis for the trait selection process
(Beatty & McCroskey, 1998; Herold & Fedor, 1998; Weaver, 1998).
When it comes to learning there are several traits that have
historically helped us understand the learning process and outcomes.
Personality and Learning
Intelligence is the ability to learn, reason, and process
information (Schmidt & Hunter, 2000). Over the years, researchers
have examined the cognitive processes responsible for differences in
intellect. For example, Carroll (1993) maintains that several different
intellectual abilities exist and dozens are assessed within modern
intelligence tests. Verbal abilities, mathematical skills, memory,
information processing, and analytical skills are just a few of the
factors being utilized within differential measures of intelligence.
Given that most of these specific abilities tend to be intercorrelated,
statistical analysis suggests a general mental ability referred to as
general intelligence or g exists (Campbell & Catano, 2004; Spearman,
1927; Vigil-Colet & Codorniu-Raga, 2002).
G represents overarching commonality of these disparate processes
and has consistently predicted learning and knowledge development
(Hunter & Hunter, 1984; Jensen, 1998). According to Jensen (1998),
the link between intelligence and learning exists "not because
intelligence measures what is taught ... but because g is intrinsic to
learning novel material, grasping concepts, distinctions, and
meanings." (Section IX, 47). As a result, those with higher levels
of intelligence tend to learn more and do so more quickly (Borman,
White, Pulakos, & Oppler, 1991; Ree, Carretta, & Teach-out 1995;
Schmidt & Hunter, 2000). "The relationship between general
intelligence and the ability to acquire knowledge appears
ubiquitous" (Brody, 1985, p. 360). That is, when considering
learning within any type of environment, particularly a classroom
environment, theory suggests intelligence will play an important role.
Less certain however, is the link between the Big 5 personality traits
and learning.
The five-factor model of personality (A.K.A. the Big 5) is based on
the factor analysis of Allport and Odbert's (1936) pool of 17,953
personality adjectives (Piedmont, 1998). Over the years, the Big 5 has
become one of the preeminent personality constructs because it measures
traits that are stable across time and situations (Costa & McCrae,
1994; Piedmont, 1998). The Costa and McCrae Big 5 Model (1984) is
represented by the five factors, and each factor is represented by six
separate facets combining to generate its overarching domain (see Chart
1). As a result, the Big 5 has been predictive of a number of relevant
individual outcomes, but only three have been linked to academic success
and student learning (e.g. Maki & Maki, 2003).
Conscientiousness is sometimes described as the will to achieve
(Smith, 1967). Those high in conscientiousness tend to show signs of
dependability, thoroughness, and responsibility. However, recent
classifications include more scholastic characteristics (Barrick &
Mount, 1991; Costa & McCrae, 1988) such as hard work,
achievement-orientation, responsibility, and perseverance. As the
sub-traits indicate, those possessing this trait tend to consistently
perform better on academic challenges. Specifically, a meta analysis by
O'Connor and Paunonen (2007) found that conscientiousness is
strongly and consistently associated with academic success across dozens
of studies (e.g. Bauer & Liang, 2003; Busato, Prins, Elshout, &
Haymaker, 2000; Conand, 2006). Indeed, the evidence supporting
conscientiousness' relationship to academic success is clear. This
relationship has developed over time because those who are more
conscientious tend to possess reproduction and application directed
learning styles which are more closely linked to higher test scores
(Busato, Prins, Elshout, & Hamaker, 1998). In addition, the
conscientious tend to take a deep approach to learning, meaning they
think critically during the learning process, link new knowledge to old
knowledge, associate disparate concepts, and create context between
isolated content and life (Zhang, 2002). Unlike surface learning
approaches, deep learning leads to greater long-term information
retention.
Sometimes interpreted as culture (Hakel, 1974; Norman, 1963) or
intellect (Peabody & Goldberg, 1989), openness to experience is the
tendency for one to be curious and open-minded, pursue things high in
aesthetic value, and be proactive in their interactions with others. It
seems logical that high performance within academic settings would be a
priority for those high in openness to experience, but this relationship
has not always panned out. While there is substantial support for a
positive relationship between openness to experience and academic
performance (e.g. Farsides & Woodfield, 2003) others have found that
no relationship exists (e.g. Diseth, 2003). For example, Dollinger and
Orf (1991) found that openness to experience was related to exam grades
but unrelated to essay or course grades. Similarly, Rothstein, Paunonen,
Rush, & King (1994) found a weak relationship with classroom
performance and GPA and no relationship with written performance grades.
The negative and non-significant findings are perplexing given that
those high in openness tend to engage in deep learning by gathering new
and unique information (Zhang, 2002).
Given its link to sociability, extroversion has also been theorized
to predict learning outcomes. Defined as one's tendency to be
expressive or show initiative (Costa & McCrae, 1992) extroversion is
often used to describe communicative phenomena. Those possessing these
traits are very talkative and proactive in most of their interactions,
spending a significantly larger portion of their time socializing than
do introverts (Costa & McCrae, 1992). And since students more
central within social networks tend to perform better academically
(Russo & Koesten, 2005) it should follow that extroverts would have
strong academic performance. This theorizing has found some evidentiary
support (e.g. Rothstein, et al. 1994). However, while some scholars
suggest links to academic performance tasks where initiative is
critical, the results have been mixed at best. For example, Paunonen
(1998) found no relationship between student performance and
extroversion; similarly, Chamorro-Premuzic and Furnham (2003a, 2003b)
found no relationship between extroversion and class performance or
extroversion and thesis research. Of greater theoretical importance is
that some scholars have found negative relationships. Goff and
Ackerman's (1992) exploration generated a negative relationship
with student performance while Bauer and Liang (2003) discovered that
those higher in extroversion tended to have lower performance. Many
scholars argue these negative associations imply that introverts spend
their time studying while extroverts spend their time socializing (e.g.
Chamorro-Premuzic & Furnham, 2005).
In short, outside of conscientiousness, the relationship between
academic performance and personality traits is mixed and deserves
greater attention. Given personality theory and previous findings, the
following is hypothesized:
H1: Intelligence, extroversion, conscientiousness, and openness to
experience will be positively related to classroom performance.
Since many of these findings have varied over the last several
years, further exploration is important. However, this exploration
should not occur within a vacuum. That is, education has changed
dramatically over the last decade and future explorations should take
those changes into account. With the changes in educational delivery, it
is essential to explore academic performance in a more dynamic
educational environment.
Course Delivery and Academic Performance
As noted earlier, many colleges and universities have provided
students with several educational delivery format options. The two major
types of online course delivery formats are pure online and hybrid. Pure
online is defined as any course which is delivered 80% or more online,
while a hybrid course typically ranges from 31% - 79% of the course
being delivered online. Though traditional face-to-face delivery is
still the most prevalent form of college course offering, online
education is growing tremendously as professors develop detailed
curriculums geared toward the online and hybrid environments. The growth
in quantity and popularity of online education has caused scholars to
question whether online course delivery formats yield better student
performance.
The traditional face-to-face delivery method was initially thought
to yield superior student performance because of student access to
immediate feedback, professor-pupil interactivity, professor immediacy,
and many other factors. However, many of these assumptions were based in
anecdotal evidence, not empirical findings. As empirical findings were
generated, unexpected results were found. For example, in one of the
most comprehensive studies to date, Maki and Maki (2003), examined more
than 300 online and 300 lecture students on a variety of independent and
dependent variables. In short, they discovered that students in online
classes performed better than their counterparts in traditional classes.
This finding supports earlier studies suggesting that students in online
classes perform better than those in traditional classes (e.g. Maki
& Maki, 2002; Maki, Maki, Patterson, & Whitaker, 2000). However,
Maki and Maki (2003) maintain that these differences are due to elegant
course design, and not the delivery method. Their assertion may be
accurate given the insignificant findings within other studies. For
example, Sankaran, Sankaran, and Bui (2000) found no differences between
delivery methods while Wang and Newlin (2000) discovered that students
in traditional classes performed better than those in online classes.
Given this disagreement within the literature, it is important to
further examine this issue.
While there are many reasons why performance may or may not be
different within traditional, hybrid, and online classes, examining
student performance in light of student personality may expand our
knowledge base regarding the differences in course delivery models.
Given the historic role of personality in driving specific behavior, we
expect that personality will play a differential role. The following
research question is explored:
RQ1: What role does personality play in student performance in
online, hybrid, and traditional classes?
METHODOLOGY
Sample and Data Collection
The potential subject pool was comprised of business, psychology,
healthcare management, and legal studies majors from a four-year
undergraduate southeastern university. These students were enrolled in
courses representing one of three different types of delivery methods.
The first class type was a traditional format, with less than 30% of the
class meetings held on online. The second class type was a hybrid
format, with 31%-79% of class meetings held online. The third class type
was an online format, with 80% or more of the class meetings held
online.
The researchers contacted the course instructors to request
permission to enter the classroom to request voluntary student
participation in the study. After receiving an overview and explanation
of the study, students who wished to participate in the study signed a
consent form and completed a survey instrument. Each respondent was
assured confidentiality and each student completed the survey instrument
in 20-30 minutes. Over the course of two consecutive semesters, 170
students were asked to participate in the study, and 133 completed the
surveys (traditional classes, n=63; hybrid classes, n=43; online
classes, n=27).
Measurement
Researchers collected objective data from each subject's
electronic school records. These data included GPA, course grades, and
class type. Subjects provided subjective data by completing portions of
the Big-5 personality assessment test. Means, standard deviations,
correlational, and regression data were computed for all variables.
Analysis of variance was then computed to assess the difference in how
students performed based upon each of their personality traits (e.g.
intelligence or openness to experience) and class type. SPSS was used
for all data analysis.
Independent/Class Variables
The personality assessment instrument used was Costa and
McCrae's (1992) NEO Five Factor Inventory-Form S (NEO-Form S), an
abbreviated 60-item version of the 240-item NEO Personality Inventory -
Revised. The NEO-Form S was used to measure participant's
self-reported levels of Conscientiousness, Extroversion, and Openness to
Experience. It was constructed by selecting the first twelve items with
the largest structure coefficients for each of the five factors.
Respondents rated their answers in these areas on a 5-point Likert scale
of measurement ranging from 1 = strongly disagree, 2 = disagree, 3 =
neutral, 4 = agree, and 5 = strongly agree. Several statements were
reverse keyed, and the individual items were summed then averaged to
create an overall measure for that personality characteristic.
Reliability coefficient alphas ranged from .80 to .95.
Extroversion
Extroversion is measured by a subject's level of warmth,
gregariousness, assertiveness, activity, excitement seeking, and
positive emotion. Respondents answered questions, such as, "I
really enjoy talking to people," and "I laugh easily."
After attaining each subject's extroversion score their score was
categorized in one of five groups; very high, high, moderate, low, or
very low. These rankings and categories were based upon norms developed
within previous studies (see Costa & McCrae, 1992, 1994).
Openness
Openness is measured by a subject's level of fantasy,
aesthetics, feelings, actions, ideas, and values. Respondents answered
questions, such as, "I often try new and foreign foods," and
"I am intrigued by the patterns I find in art and nature."
After attaining each subject's openness score their score was
categorized in one of five groups; very high, high, moderate, low, or
very low. These rankings and caterogories were based upon norms
developed within previous studies (see Costa & McCrae, 1992, 1994).
Conscientiousness
Conscientiousness is measured by a subject's level of
competence, order, dutifulness, achievement striving, self-discipline,
and deliberation. Respondents answered questions, such as, "I work
hard to accomplish my goals," and "I keep my belongings neat
and clean." After attaining each subject's conscientiousness
score their score was categorized in one of five groups; very high,
high, moderate, low, or very low. These rankings and caterogories were
based upon norms developed within previous studies (see Costa &
McCrae, 1992, 1994).
Intelligence
Intelligence is operationalized by each subject's
comprehensive GPA. Similar to many popular general intelligence tests
(e.g. Wonderlic, 2003), comprehensive GPA is comprised of various verbal
and mathematical elements because students take dozens of courses from
disparate fields of study. Like general intelligence tests,
"academic success predictors (e.g. GPA) usually consist of
cognitive measures, pertaining to mental ability or intelligence; and
non-cognitive measures" (Ridgell & Lounsbury, 2004). GPA is
categorized on a four point scale.
Course Delivery Format
As noted above, course delivery format was measured by segmenting
the amount of face to face interaction between student and professor.
Three categories were developed; traditional classes held less than 30%
of the class meetings online, the hybrid classes held 31%-79% of class
meetings online, and online classes held 80% or more of the class
meetings online.
Dependent/Response Variable
Classroom Performance
Classroom performance was measured by the grade a student received
in their traditional, hybrid, or online course. Grades were assigned a
score on a scale from 0-4 with each class grade corresponding to a
numerical score; F=0, D=1, C=2, B=3, A=4. Researchers collected this
data from each subject's electronic school records.
THE RESULTS OF THIS STUDY
Hypothesis one was partially supported. Regression analysis
confirms that intelligence ([r.sup.2]=.57, p<.001) predicted
classroom performance (Table 2). While conscientiousness ([r.sup.2]=.07,
ns),22 extroversion ([r.sup.2] =.09, ns), and openness to experience
([r.sup.2] =.04, ns) were positively related to classroom performance,
they were non-significant.
While assessing the RQ, analysis of variance data suggests that
there is a main effect for intelligence relative to performance over the
three types of courses (F(1, 3)=4.28; p<.001) (Table 2). This linear
relationship indicates that as intelligence increases so does student
performance, regardless of type of delivery method.
Intelligence and conscientiousness has an interactive effective
relative to student performance (F(1, 4)=2.335; p<.10). Specifically,
student performance is dependent on levels of intelligence and
conscientiousness. This relationship is non-linear.
There is an interaction between openness to new experience and
class type (F(1, 2)=4.151; p<.05) and conscientiousness and class
type (F(1, 4)=2.565; p<.05) relative to student performance.
Surprisingly, students in hybrid classes outperformed students in other
classes.
DISCUSSION
Personality and Classroom Performance. As expected, this study
represents yet another confirmation that more intelligent students
perform better than those lower in intelligence in classroom settings.
Consistent with a substantial body of scholarship, regression analysis
confirms that intellectual ability is positively related to classroom
performance (e.g., Butaso, et al, 2000; Ridgell & Lounsbury, 2004).
Intelligence "reflects a broader and deeper capability for
comprehending our surroundings--"catching on," "making
sense" of things, or "figuring out" what to do"
(Gottfredson, 1997, p. 13). Since students in today's dynamic
learning environments are confronted with multiple teaching methods
(e.g. lectures, group work, PowerPoint presentations, etc) often within
the same class and on the same day, an ability to quickly "catch
on" or "figure things out" may enhance learning. These
findings suggest this is true regardless of learning environment because
intelligence helps students "get up to speed."
Though some scholars have found a positive relationship between
openness to experience and classroom performance (Blickle, 1996; Eyong
& Schniedergans, 2004, Lounsbury, et al., 2003), like other works,
this study found no significant relationship (Busato, et al., 2000;
Chamorro-Premuzic & Furnham, 2003). These varied results are
consistent with findings in the O'Connor and Paunonen (2007)
meta-analysis that the correlation between openness to new experience
and classroom performance is tenuous at best. This study's results,
and those of others, are likely mixed for two reasons. First, each study
uses a different outcome measure. For example, this study, like others
(e.g. Rothstein et al., 1994) uses the comprehensive measure of course
grade while others use limited measures of performance, such as exam
grades (e.g. Dollinger & Orf, 1991). Second, while high openness may
initially stimulate subject matter intellectual curiosity it may not
translate into higher performance in class or on specific class
assignments.
Similarly, this data did not reveal a relationship between
extroversion and classroom performance. This is consistent with studies
by Chamorro-Premuzic and Furnham (2003a, 2003b) and Paunonen (1998).
However, this contradicts some empirical findings (e.g. Eyong &
Schniederjans, 2004; Rothstein et al., 1994) and social network
theorizing in this area which notes that students more central within
social networks tend to perform better academically (Russo &
Koesten, 2005). Positive results were likely elusive because being
gregarious and expressive, and maintaining a central position within
one's social network does not necessarily lead to higher classroom
performance.
Several researchers maintain that of the Big 5 five personality
dimensions, conscientiousness has the strongest relationship with
classroom performance (e.g., Busato, et al., 2000; Conard, 2006;
O'Connor & Paunonen, 2007). Indeed, this theorizing is
supported by the correlational finding that conscientious students
outperform their counterparts across class settings. However, when
regression analyses were performed, the contributions of
conscientiousness became insignificant because of the role of
intelligence. Specifically, when students are highly intelligent their
levels of conscientiousness are less of a factor than when they are of
low to moderate intelligence. Stated plainly, if one is highly
intelligent his/her level of conscientiousness plays a diminished role
because the student will still learn quicker and retain more than
his/her counterparts. If one is moderately intelligent, s/he must
maintain a strong work ethic to ensure peak performance. This is true
across the class types studied herein. The practical implication of this
finding is that highly intelligent students can be placed in virtually
any setting and perform well. While conscientious students will still
generally perform better than their counterparts in most settings, the
differences are more pronounced when they are moderately intelligent.
Class Type, Personality, and Classroom Performance. Today's
educational environment is dynamic and in constant flux. This study
makes a value-added contribution to current learning and educational
scholarship by exploring personality within the context of today's
dynamic educational environment. Specifically, we explored how
personality influences educational outcomes within the three major
classroom settings. The most notable result is that the more intelligent
and conscientious students within hybrid classes outperformed others in
virtually every other scenario. That is, while students in hybrid
classes did not necessarily perform better than their counterparts, when
intelligence and conscientiousness were considered, significant
differences in performance became apparent. As the first work to explore
these elements within these contexts, this study makes two significant
contributions to the literature.
First, students enrolled in hybrid classes who are high in
conscientiousness outperformed their classmates in virtually every other
class setting. That is, hybrid students that possess a will to achieve
tended to outperform their classmates lower in these traits across all
other class types. At first glance this finding was somewhat perplexing
because one would expect that those who take initiative would perform
better under the non-constraining conditions of an online class, as some
scholars have suggested (e.g. Maki & Maki, 2003). This theorizing
may be best explained by the level of social presence (e.g. immediacy)
and flexibility present when taking a hybrid class.
Social presence is defined as the level of inclusiveness,
connectedness, and immediacy a learner experiences (Tu & McIsaac,
2002). Students experience immediacy and inclusiveness when a professor
reduces the psychological distance with the student by speaking directly
to the student's needs and concerns. While students in traditional
classes have greater opportunities to experience social presence because
of the physical presence of the professor, hybrid students have similar
opportunities because they meet with the professor on a regular basis.
As a result, students who have been in physical contact with their
professor and classmates may benefit from a level of connectedness not
experienced by their online counterparts. Hybrid students also enjoy the
flexibility online classes provide. Since these students have the
ability to work at their own pace within the confines of the online
course, they also have the opportunity to complete work at times
conducive to their personal schedules, thereby avoiding the rigid time
constraints of traditional classes. Coupling flexibility with the
inclusiveness of physical contact can lead to improved outcomes.
Second, students enrolled in hybrid classes who are high in
openness to experience outperformed their peers across class settings.
Since a hybrid class represents the nexus between the traditional and
online learning environment, students who are high in openness excel
when presented with a continued variation of their learning experience
offered in a hybrid setting. Hybrid classes create a more stimulating
learning environment, thereby enhancing deeper learning which is a
driving force to the academic performance of students open to new
experiences.
CONCLUSIONS
Personality traits play an increasingly central role in
understanding how students interact with the changing dynamics of
today's learning environments. Since students are expected to
operate under multiple teaching methods they must possess the traits
necessary to deal with the variety they are confronted with on a daily
basis. The current work explicates these elements. As expected, highly
intelligent students performed better than others across all class
types. More importantly, however, the current study differentiates the
roles of intelligence, conscientiousness, and openness to experience
within these class settings. Specifically, the hybrid classroom format
creates a learning environment that is conducive to learning for
students with certain traits because it incorporates the best features
of the online and traditional classes; namely flexibility and professor
immediacy.
IMPLICATIONS AND FUTURE DIRECTIONS
This study's finding regarding intelligence is consistent with
the literature noting intelligence is the most significant correlate of
classroom performance. While conscientiousness was also found to be an
indicator of classroom performance, the inclusion of intelligence in the
model greatly reduced its significance. The additional findings in this
study regarding the relatively insignificant impact of a student's
openness to new experience and level of extroversion on classroom
performance are consistent with much of the literature, although
external factors, such as outcome measures, has historically produced
varied results. Further exploration in this area might benefit from
research models incorporating students from a wider array of disciplines
and examining additional personality traits, such as self-discipline.
This study also builds upon the literature by contributing an
additional finding that students who are high in conscientiousness and
enrolled in hybrid classes outperformed their peers across class types,
and students high in openness to experience also outperformed their
peers across class settings. These findings provide valuable insight for
administrators faced with the challenge of creating post-secondary
curricula in an effort to balance the immediacy of traditional courses
with the flexibility offered by distance education. Based on these
findings, further research is recommended to explore the unique dynamics
of hybrid classes and the way in which this alternative delivery format
can be most effectively utilized in higher education.
LIMITATIONS
This study's results are limited in two ways. First, while
studying one organization is not uncommon (e.g. Busato, et al., 2000),
these results may not always generalize beyond the type of university
studied or the region in which it was studied. Future works should
explore various types of universities (e.g. research oriented schools or
teaching-oriented schools) in different geographic regions to assess the
broader implications of the current work's findings. Second, while
the sample size provided adequate power given the number of factors
studied (e.g. Bayram, Deniz & Erdogan, 2008; Offir, Bezalel &
Barth, 2007), a larger sample would offer a broader base from which to
assess the theory. The marginal sample size led to several missing cells
(see Table 3 for missing cells and DF analysis) in the ANOVA analysis.
When assessing variables like intelligence or conscientiousness within a
university environment, it's common to have the majority of a
university population score high because it takes moderate to high
levels of intelligence and conscientiousness to qualify for admission
into most universities. Therefore, no subjects scored in the lowest
categories of these measures. This may be one cause for missing cells
within these variables. As a result, the breadth of the dataset could
have been enhanced by a larger sample size. Future studies should gather
data from a more sizable pool of participants.
REFERENCES
Allen, I., & Seaman, J. (2008). Staying the course: Online
education in the United States. Retrieved from
http://www.sloan-c.org/publications/survey/pdf/staying_the_course.pdf.
Allport, G.W., & Odbert, H.S. (1936). Trait names: A
psycho-lexical study. Psychological Monographs, 47 (1, Whole No. 211).
Barrick, M., & Mount, M. (1991). The big five personality
dimensions and job performance: A metaanalysis. Personnel Psychology,
44, 1-26.
Bauer, K.W., & Liang, Q. (2003). The effecdt of personality and
precollege characteristics on firstyear activities performance. Journal
of College Student Development, 44, 277-290.
Bayram, S., Deniz, L., & Erdogan, Y. (2008). The role of
personality traits in web based education. The Turkish Online Journal of
Educational Technology, 7 (2): Retrieved from
http://www.tojet.net/articles/725.htm.
Beatty, M.J., & McCroskey, J. C. (1998). Interpersonal
communication as temperamental expression: A commmunibiological
paradigm. In J.C. McCroskey, J.A. Daly, M.M. Martin, and M.J. Beatty
(Eds.), Communication and Personality: Trait Perspectives (pp. 41-68).
Cresskill, N.J.: Hampton Press.
Blickle, G. (1996). Personality traits, learning strategies and
performance. European Journal of Personality, 10, 337-352.
Borman W. C., White, L. A., Pulakos E. D., & Oppler, S. H.
(1991). Models of Supervisory Job Performance Ratings. Journal of
Applied Psychology, 76, 863-872.
Busato, V.V., Prins, F.J., Elshout, J.J., & Hamaker, C. (1998).
The relation between learning styles, the Big Five personality traits
and achievement motivation in higher education, Personality and
Individual Differences, 26 (1): 129-140.
Busato, V.V., Prins, F.J., Elshout, J.J., & Haymaker, C.
(2000). Intellectual ability, learning style, personality, achievement
motivation and academic success of psychology students in higher
education. Personality and Individual Differences, 29, 1057-1068.
Campbell, S., & Catano, V. M. (2004). Using Measures of
Specific Abilities to Predict Performance in the Canadian Forces
Operator Occupations. Military Psychology, 16(3): 193-201.
Carroll, J.B. (1993). Human Cognitive Abilities: A Survey of Factor
Analytic Studies. Cambridge, U.K.: Cambridge University Press.
Chamorro-Premuzic, T., & Furnham, A. (2003a). Personality
traits and academic examination performance. European Journal of
Personality, 17, 237-250.
Chamorro-Premuzic, T., & Furnham, A. (2003b). Personality
predicts academic performance: Evidence from two longitudinal university
samples. Journal of Research in Personality, 37, 319-338.
Chamorro-Premuzic, T., & Furnham, A. (2005). Personality and
intellectual competence. New Jersey: Lawrence Erlbaum Associates.
Cheung, L., & Kan, A. (2002). Evaluation of factors related to
student performance in a distancelearning business communication course.
Journal of Education for Business, 77 (5): 257-263.
Conard, M.A. (2006). Aptitude is not enough: How personality and
behavior predict academic performance, Journal of Research in
Personality, 40, 339-346.
Costa, P.T., & McCrae, R.R. (1980). Influence of extraversion
and neuroticism on subjective wellbeing: Happy and unhappy people.
Journal of Personality and Social Psychology, 38, 668378.
Costa, P.T., & McCrae, R. R. (1992). Reviews NEO personality
inventory and NEO five-factor inventory professional manual. Odessa, FL:
Psychological Assessment Resources.
Costa, P.T., & McCrae, R.R. (1994). "Set like
plaster"? Evidence for the stability of adult personality. In T.F.
Heatherton and J.L. Weinberger (Eds.), Can personality change?
Washington, D.C.: American Psychological Association.
Daly, J.A., & Bippus, A.M. (1998). Personality and
Interpersonal Communication: Issues and Directions. In J.C. McCroskey,
J.A. Daly, M.M. Martin, and M.J. Beatty (Eds.), Communication and
Personality: Trait Perspectives (pp. 1-40), Cresskill, N.J.: Hampton
Press.
Davis-Blake, A., & Pfeffer, J. (1989). Just a mirage: The
search for dispositional effects in organizational research. Academy of
Management Review. 14: 385-400.
DePaulo, B.M. (1993). The ability to judge others from their
expressive behaviors. Fifty Years of Personality Psychology. (K.H.
Craik, Ed.). New York: Plenum Press.
Didia, D., & Hasnat, B. (1998). The determinants of performance
in the university introductory finance course. Financial Practice and
Education, 8 (1), 102-107.
Diseth, A. (2003). Personality and approaches to learning as
predictors of academic achievement. European Journal of Personality, 17,
143-155.
Dollinger, S. J., & Orf, L. A. (1991). Personality and
performance in "personality": Conscientiousness and Openness.
Journal of Research in Personality, 25, 276-284.
Dutton, J., Dutton, M., & Perry, J. (2002). How do online
students differ from lecture students? Journal of Asynchronous Learning
Networks, 6 (1): Retrieved from
http://www.alnweb.org/alnweb/journal/Vol6_issue1/ 6dutton.htm.
Epstein, S., & O'Brien, E.J. (1985). The person-situation
debate in historical and current perspective. Psychological Bulletin,
98, 513-537.
Eyong, B.K., & Schniedergans, M.J. (2004). The role of
personality in web-based distance education courses. Communications of
the ACM, 47 (3): 95-98.
Farsides, T., & Woodfield, R. (2002). Individual differences
and undergraduate academic success: the roles of personality,
intelligence, and application. Personality and Individual Differences,
Personality and Individual Differences, 34, 1225-1243.
George, J.M. (1992). The role of personality in organizational
life: Issues and evidence. Journal of Management, 18 (2).
George, J.M. (1996). Trait and State Affect. In K. Murphy (ed.)
Individual Differences and Behavior in Organizations. Jossey-Bass.
Hair, P., & Hampson, S.E. (2006). The role of impulsivity in
predicting maladaptive behaviour among female students. Personality and
Individual Differences, 40, 943-952.
Hakel, M. D. (1974). Normative personality factors recovered from
ratings of personality descriptors: The beholder's eye. Personnel
Psychology, 27, 409-421.
Hall, M. (2008). Predicting student performance in web-based
distance education courses based on survey instruments measuring
personality traits and technical skills. Online Journal of Distance
Learning Administration, 11 (3). Retrieved from
http://www.westga.edu/~distance/ojdla/fall113/hall113.html.
Halsne, A., & Gatta, L. (2002). Online versus
traditionally-delivered instruction: A descriptive study of learner
characteristics in a community college setting. Online Journal of
Distance Learning Administration, 5 (1). Retrieved from
http://www.westga.edu/~distance/ojdla/spring51/halsne51.html.
Herold, D.M., & Parsons, C.K. (1985). Assessing the feedback
environment in work organizations: Development of the job feedback
survey. Journal of Applied Psychology, 70.
Hunter, J. E., & Hunter, R. F. (1984). Validity and Utility of
Alternative Predictors of Job Performance. Psychological Bulletin, 761,
72-93.
Jensen, A.R. (1998). The g Factor: The Science of Mental Ability.
Wesport, CT: Praeger.
Liu, S. (2008). Student interaction experiences in distance
learning courses: A phenomenological study. Online Journal of Distance
Learning Administration, XI (I).
Lounsbury, J.W., Sundstrum, E., Loveland, J.M., & Gibson, L.W.
(2003). Intelligence, "big five" personality traits, and work
drive as predictors of course grade. Personality and Individual
Differences, 35, 1231-1239.
Maki, R.H., Maki, W.S., Patterson, M., & Whittaker, P.D.
(2000). Evaluation of a Web-based
introductory psychology course: learning and satisfaction in online
versus lecture courses. Behavior Research Methods, Instrument, &
Computers, 32(2): 230-239.
Maki, W. S., & Maki, R. H. (2002). Multimedia comprehension
skill predicts differential outcomes of web-based and lecture courses.
Journal of Experimental Psychology: Applied, 8, 85-98.
Maki, R.H.& Maki, W.S. (2003). Prediction of learning and
satisfaction in web-based and lecture courses. Journal of Educational
Computing Research, 28(3): 197-219.
Norman, W. T. (1963). Toward an adequate taxonomy of personality
attributes: Replicated factor structure in peer nomination personality
ratings. Journal of Abnormal Social Psychology, 66, 574-583.
O'Connor, M.C., & Paunonen, S.V. (2007). Big five
personality predictors of post-secondary academic performance.
Personality and Individual Differences, 43, 971-990.
Offir, B., Bezalel, R., & Barth, I. (2007). Introverts,
extroverts, and achievement in a distance learning environment, American
Journal of Distance Education, 21 (1): 3-19.
Paunonen, S.V. (1998). Hierarchical organization of personality and
prediction of behavior. Journal of Personality and Social Psychology,
74, 538, 556.
Peabody, D., & Goldberg, L. R. (1989). Some determinants of
factor structures from personality-trait descriptors. Journal of
Personality and Social Psychology, 57, 552-567.
Piedmont, R.L. (1998). The revised NEO Personality Inventory:
Clinical and research applications. New York: Plenum Press.
Redden, E. (2008). Demographic Boom and Bust. Retrieved from
www.insidehighered.com/news/2008/03/20/projections.
Ree, M. J., & Carretta, T. R. (1995). Group Differences in
Aptitude Factor Structure on the ASVAB. Educational and Psychological
Measurement, 55, 268-277.
Ridgell, S.D., & Lounsbury, J.W. (2004). Predicting academic
success: general intelligence, "big five" personality traits
and work drive. College Student Journal, 38 (4), 607-619.
Russo, T.C., & Koesten, J. (2005). Prestige, centrality and
learning: A social network analysis of an online class. Communication
Education, 54(3), 254-261.
Sankaran, S. R., Sankaran, D., & Bui, T. X. (2000). Effect of
student attitude to course format on learning performance: An empirical
study in web vs. lecture instruction. Journal of Instructional
Technology, 27, 66-73. Wang, A. Y., & Newlin, M. H. (2000).
Characteristics of students who enroll and succeed in web-based
psychology classes. Journal of Educational Psychology, 92, 137-143.
Schmidt, F.L., & Hunter, J.E. (2000). Select on Intelligence.
In E.A. Locke (Ed), Handbook of Principles of Organizational Behavior,
3-14. Blackwell Business Publishing.
Smith, G. M. (1967). Usefulness of peer ratings of personality in
educational research. Educational and Psychological Measurement, 27,
967-984.
Spearman, C. (1927). The Abilities of Man. London: MacMillan. Tu,
C. H., & McIsaac, M. S. (2002). The relationship of social presence
and interaction in online classes. The American Journal of Distance
Education, 16(3): 131-150.
University System of Georgia Office of Research and Policy Analysis
(2006). Retrieved from
www.usg.edu/research/students/enroll/trends2018.pdf.
Vigil-Colet, A., & Codorniu-Raga, M. J. (2002). How Inspection
Time and Paper and Pencil Measures of Processing Speed are Related to
Intelligence. Personality and Individual Differences, 337, 1149-1161.
Weaver III, J.B. (1998). Personality and self-perceptions about
communication. In J.C. McCroskey, J.A. Daly, M.M. Martin, and M.J.
Beatty (Eds.), Communication and Personality: Trait Perspectives (pp.
95-118). Cresskill, N.J.: Hampton Press.
Wojciechowski A., & Palmer, L.B. (2005). Individual student
characteristics: Can any be predictors of success in online classes?
Online Journal of Distance Learning Administration,8 (2). Retrieved from
http://www.westga.edu/~distance/ojdla/summer82/wojciechowski82.htm.
Wonderlic Personnel Test, Inc. (2003). Wonderlic Personnel Test:
User's manual. Libertyville, IL: Wonderlic Personnel Test, Inc.
Zhang, L. (2002). Does the big five predict learning approaches?
Personality and Individual Differences, 34, 1431-1446.
About the Authors:
Michael V. Tidwell is Dean of the College of Business at Bloomsburg
University of Pennsylvania. He completed his doctoral degree at
Washington State University and has published in several scholarly
journals including the International Journal of Management Theory and
Practices, the Journal of Business Communication, and the journal of
Nonprofit Management and Leadership. In addition to speaking at numerous
national and international conferences, he has spent time working in
Corporate Communications within the high tech industry and traveling,
teaching, and/or consulting in Europe, Middle-East, India, S. Africa,
and Kenya.
Sheryne Southard earned her Juris Doctor from the Sandra Day
O'Connor College of Law at Arizona State University and a Bachelor
of Science in Business Management from the University of Nevada. She has
extensive experience in pure online and hybrid instruction and the
development of strategies to improve student success and engagement in
alternative delivery courses. She serves as the faculty online
coordinator. In 2009, Dr. Southard was recognized as the Grand Award
Winner of the Pearson-Prentice Hall Online Teaching Competition.
Mara Mooney is a graduate of Lafayette College in Easton, PA (B.A.,
cum laude, Government & Law; Phi Beta Kappa) and Emory University
School of Law in Atlanta, GA (Notes & Comments Editor, Emory Law
Journal; Dean's Fellow in Legal Research and Writing). She is the
author of the article, "The Nutrition Labeling and Education Act of
1996: A Proposal for a Less-Restrictive Scientific Standard," which
was published in the Emory Law Journal and noted as "worth
reading" in the National Law Journal, and the textbook,
Fundamentals of Georgia Real Estate Law, published by Carolina Academic
Press. Prior to joining the faculty at Clayton State University, she
practiced law with the multinational firm, Alston & Bird, LLP, in
Atlanta, Georgia, and taught at two ABA- approved legal assistant
studies programs. At Clayton State University, Ms. Mooney teaches
traditional, hybrid, and online courses in ethics, contracts, torts,
legal research & writing, and real property law.
Michael V. Tidwell
Bloomsburg University of Pennsylvania
Sheryne Southard
Mara Mooney
Clayton State University
Table 1
Correlational Data
Course
Intelligence Grade Neuroticism Extroversion
Class Type 0.00 -0.07 -0.02 -0.03
Intelligence 0.60 -0.21 -0.01
Course Grade -0.12 0.08
Neuroticism -0.27
Extroversion
Openness
Agreeableness
Openness Agreeableness Conscientiousness
Class Type 0.21 -0.03 -0.05
Intelligence 0.09 0.03 0.19
Course Grade 0.08 -0.04 0.17
Neuroticism -0.08 -0.26 -0.27
Extroversion 0.16 0.14 0.12
Openness 0.01 0.00
Agreeableness 0.26
Bold and italicized: Correlation is significant at the 0.01 level
(1-tailed).
Bold: Correlation is significant at the 0.05 level (1-tailed).
Table 2
Results of Regression Analysis
Un standardized Standardized
Model B Std. Error Beta
(Constant) 0.82 1.03
Class type -0.08 0.08 -0.07
tnteffigence 0.60 0.08 0.57
Neuroticism 0.05 0.09 0.04
Extroversion 0.11 0.09 0.09
Ooenness 0.06 0.11 0.04
Agreeableness -0.11 0.12 -0.07
Conscientio.isness 0.09 0.10 0.07
Model t
(Constant) 0.80
Class type -0.99
tnteffigence 7.59
Neuroticism 0.49
Extroversion 1.20
Ooenness 0.51
Agreeableness -0.94
Conscientio.isness 0.94
Bold and Italics: Correlation is significant at the 0.01 level
(1-tailed).
Bold: Correlation is significant at the 0.05 level (1-tailed].
Table 3
Results of ANOVA for Course Grades
Sums of Missing
Source Squares df Cells
Corrected Model 82.30 85
Intercept 296.95 1
Class Type 0.92 2 0
Intelligence 12.36 3 0
Extroversion 0.10 2 2
Conscientiousness 1.13 3 1
Openness to Experience 1.01 3 1
Class Type * Intelligence 0.90 4 0
Class Type * Extroversion 2.55 4 0
Class Type Openness to Experience 3.93 2 4
Class Type * Conscientiousness 4.86 4 2
Intelligence * Extroversion 074 3 0
Intelligence * Openness to Experience 0.64 2 3
Intelligence * Conscientiousness 4.42 4 3
Extroversion * Conscientiousness 0.83 4 2
Extroversion * Openness to Experience 1.81 2 1
Conscientiousness * Openness to 0.20 1 5
Experience
Error 21 78 46
Total 1359 132
Corrected Total 104.08 131
Table 4
Marginal Means
Groups
Conscien-
Intelligence tiousness Mean Std. Error
1 2 a.
3 1 0638
4 2 0231
5 2 0.437
2 2 a.
3 2.778 0.229
4 2.411 0 149
5 3 214 0 231
3 2 a
3 3.167 0 269
4 3 325 0 161
5 3.137 0.177
4 2 4 0688
3 4 0688
4 3.9 0.275
5 3.722 0.191
Groups Groups
Intelligence Class Type Openness Mean Std. Error
1 1 2 2 0 487
3 2 0.397
4 3.112 0.131
5 3.133 0.162
2 2 2 a.
3 a.
4 3 104 0 171
5 3 385 0 167
3 3 2 a
3 a.
4 2 318 0 193
5 2 733 0.205
4
Groups Groups
Conscien-
Intelligence Class Type tiousness Mean Std Error
1 1 2 4 0.683
3 3 0.281
4 2.763 0.142
5 3.167 0.154
2 2 2 a.
3 2 833 0.269
4 3 167 0.166
5 3 75 0.22
3 3 2 a.
3 28 0 303
4 2 479 0 216
5 3.062 0.223
4
a This level combination of factors is not observed, thus the
corresponding population marginal mean is not estimable.
