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Temperament, Learning Styles and Demographic Predictors of Student Satisfaction in a Blended Learning Environment
Maribeth FergusonCECS 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 large-enrollment 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/ verbalactive/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 Chi-square 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
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Garson, David G. (2006). Logistical Regression. Retrieved on April 28, 2006 from http://www2.chass.ncsu.edu/garson/PA765/logistic.htm.
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References
McAllister, C. and Ting, E. (2001). Analysis of Discussion Items by Males and Females in Online College Courses. Seattle, WA: The Annual Meeting of the Ammerican Educational Research Association. (ERIC Document Reproduction Services No ED 458-237)
Paulson, Morton F. (2002). Online Education Systems: Discussion and Definition of Terms. Retrieved on 13, 2006 from http://www.nettskolen.com/forskning/Definition%20of%20Terms.pdf.
Schwarz, Carl J. (1998) Scales of Measurement. Retrieved on April 1, 2006 from http://www.math.sfu.ca/~cschwarz/Stat-301/Handouts/node5.html
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Stokes, Suanne P., (2003). Temperament, Learning Styles, and Demographic Predictors of College Student Satisfaction in a Digital Learning Environment. Biloxi, MS: The Annual Meeting of the Mid-South Educational Research Association. (ERIC Document Reproduction Service No. ED 482-454).
References Wegner, Scott P. (1999). The Effects of Internet-Based Instruction on
Student Learning. Retrieved on April 1, 2006 from http://www.sloan-c.org/publications/jaln/v3n2/v3n2_wegner.asp
Yang, Yi, and Cornelius, Linda. (2004). Students’ Perceptions Towards the Quality of Online Education: A Qualitative Approach. Retrieved on April 1, 2006 from http://www.nettskolen.com/forskning/Definition%20of%20Terms.pdf