Temperament, Learning Styles and
Demographic Predictors of Student
Satisfaction in a Blended Learning
Environment
Maribeth Ferguson
CECS 5610
Dr. G. Knezek
Purpose
The purpose of this study was to identify
predictors of student satisfaction in
undergraduate college students at a
mid-sized southern university enrolled
in courses with a blended learning
environment
Purpose


A mid-sized southern university states
that 25% of students who enroll in
traditional large-enrollment course do
not finish the course
The university plans to conduct
research to compare learner satisfaction
and learner outcomes between the two
learning environments
Quality Enhancement Plan
Quality Enhancement Plan:
 To improve student learning outcomes
and student experience in largeenrollment undergraduate courses

A component of the Southern
Association of Colleges and Schools
reaffirmation and accreditation process
Online Education




The separation of teachers and learners
The influence of an educational
organization
The use of a computer network to
present and distribute some educational
content
The provision of two-way
communication via a computer network,
may benefit from communication with
each other, teachers and staff
Instructional Delivery


Adult learners present a wide range of
individual differences including:
differences in orientation to learning
and readiness to learn
No assumptions should be made about
adult’s preferences for instructional
delivery simply because they are adults
Changes in Higher Education



Distance learning is an increasing
important component of higher
education
Studies have been conducted on the
effects of learner satisfaction in an online
learning environment
However, few research studies have
focused on improving learner satisfaction
through a blended learning environment
Recent Research


Recent research can be classified
generally into four categories:
interaction, active learning, student
perceptions, and learning outcomes
The quality of online education has also
prompted the attention of higher
education accreditation associations
Data Collection: Instruments
The Keirsey Temperament Sorter II:
 A personality survey: guardian, artisan,
idealist, or rational
 The Index of Learning Styles:
sensory/intuitive, visual/ verbal
active/reflective, sequential/global
Data Collection: Instruments
The Student Satisfaction Questionnaire:
 16 statements; the scores range from:
the least satisfaction scoring 16 to the
most satisfaction scoring 80
 The degree of satisfaction was recoded
as unsatisfied to satisfied with the
median score as the determinant for the
categories
Forward Selection


Forward selection starts with an empty
model
The random/independent variable with
the smallest P value, when it is the only
predictor in the regression equation, was
placed in the model
Forward Selection


Each subsequent step adds the variable
that has the smallest P-value in the
presence of the predictors already in
the equation
Variables were added one-at-a-time as
long as their P-values were small
enough, typically less than 0.05 or 0.10
P-Value



P value—the probability that any
particular outcome would have arisen
by chance
Small P-values suggest that the null
hypothesis is unlikely to be true
The smaller it is, the more convincing
is the rejection of the null hypothesis
Logical Regression


Regression analysis is any statistical
method where the mean of one or more
random/independent variables is
predicted on other response/dependent
variables
Random variables: Temperament,
Learning Styles, Demographic
Characteristics
Multiple Linear Regression



Multiple linear regression aims is to find a
linear relationship between a response
variable and several possible predictor
variables (Easton, Hall, & Young 1997)
Response/Dependent Variable: Student
Satisfaction
Predictor/Independent Variables:
temperament, learning styles, demographic
characteristics
Logistic Regression


Logistic Regression is a regression
method used when the
random/independent variable is
dichotomous
The Index of Learning Styles:
sensory/intuitive, visual/ verbal,
active/reflective, and sequential/global
Logistic Regression


Logistic regression is used to predict
the likelihood (the odds/ratio) of the
outcome based on the
predictor/independent variables
The significance of the logistic
regression can be evaluated by …a Chisquare test, evaluated at the p < .05
level (Lani, 2006)
Assumptions


The students enrolled in the five
blended learning courses had the
technical skills necessary to
participate in a partially Web-based
course
The students would understand and
answer the surveys honestly
Assumption


The target sample would be
representative of the institution
And the total student population
involved in blended learning
environments at the postsecondary
level
Limitations


This study’s generalizability of the
data is limited
The target sample involved
undergraduate college students
from only one institution in the
southern United States
Limitations




Additionally, the data is collected at
only one point in time
If independent samples are taken
repeatedly from the same population
And a confidence interval calculated for
each sample
Then a certain percentage (confidence
level) of the intervals will include the
unknown population parameter
Limitations

Confidence intervals are more
informative than the simple results of
hypothesis tests, where we decide
'reject H0' or 'don't reject H0‘, since
they provide a range of plausible values
for the unknown parameter
Data Analysis


The SSQ was recorded as interval,
ordinal and nominal data
Descriptive statistics were used to
report the temperament, learning
styles and demographic
characteristics of the target sample
Data Analysis

Responses to each satisfaction
statement with blended learning
environment were reported by using
frequencies and percentages for
each indicator level
Data Analysis


Each predictor/independent variable
was correlated with the
criterion/dependent variable,
determining the rating of satisfied or
unsatisfied
Two levels of experience were
considered in the analysis, novice and
intermediate users; and the proficient
users
Data Analysis
The regression equation:
 indicated whether or not a significant
effect from the predictor/independent
variables on satisfaction existed

and offered the probably of a correct
prediction of satisfaction for the set of
predictors/independent variables
Data Analysis

Variables that emerge as predictors of
satisfaction were also compared to the
individual satisfaction item responses to
identify possible relationships
Expectations



The participation should be high since
the suveys are require assignments
The grade classification characteristic
should be mostly lower classmen
The student experience with blended
learning environments should be low
Results

Other studies have found gender
and lnternet experience to be the
only significant predictors of
student satisfaction in digital
learning environment
Research Question
Are temperament, learning styles, and
demographic characteristics of college
students predictors of student satisfaction
in a blended learning environment?


Females were more likely to be satisfied
with digital learning environments than are
males
More experienced Internet users reported
more satisfaction than the less experienced
users
Research Significance


The significance of this study is in the
independent variables that did not show
significance as predictors of student
satisfaction
Sometimes knowing what does not
work is just as important as know what
does work
References

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of Terms.

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http://www.psy.pdx.edu/PsyTutor/Tutorials/Research/Elements.
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
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References





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
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